Register reserve under agents hub and pin VAULT storage.

Classify freedom-intelligence for State Hub (agents domain), sync FI-WP-0001..0003 with hub IDs, enrich catalog entries with profile/SWOT, and pin the open-weight reserve to D:\vault\coulomb\freedom-intelligence\ with strategic S-tier policy.

inventory/README.md

@@ -1,32 +1,56 @@
 # Model inventory
 
 In-repo **catalog of open-weight revisions** reserved (or nominated) for the lab.
-Weight blobs live on backup storage — see `docs/backup-storage-policy.md`.
+Weight blobs live on the **VAULT HD** at `D:\vault\coulomb\freedom-intelligence\` —
+see `docs/backup-storage-policy.md`.
 
 ## Layout
 
 ```text
 inventory/
 ├── README.md                 # this file
-├── schema.yaml               # field reference + example
-├── collection-policy.md      # approval and eligibility rules
+├── schema.yaml               # field reference (v0.2+)
+├── collection-policy.md      # approval and eligibility (R/S/W tiers)
 └── catalog/
     └── *.yaml                # one file per model revision
 ```
 
+## What each catalog entry contains
+
+| Section | Purpose |
+| ------- | ------- |
+| **Identity** | `id`, `name`, `org`, `status`, `tags`, `tier-*` |
+| **source** | HF/org URLs, revision pin |
+| **profile** | Use cases, sweet spots, anti-patterns, **original_source** link |
+| **swot** | Compact strengths / weaknesses / opportunities / threats |
+| **license / size / hardware_class** | Retention and run guidance |
+| **collection** | Approval, NAS path, brief refs |
+
+Operators should be able to open one YAML and decide *whether* and *why* to pull
+or serve a model without reading the whole research tree.
+
 ## Status values
 
 `candidate` → `approved` → `collecting` → `collected` → `verified`  
 also: `superseded` | `evicted` | `rejected`
 
+## Tiers
+
+| Tag | Meaning |
+| --- | ------- |
+| `tier-r` | Runnable spine (current lab hardware) |
+| `tier-s` | Strategic capability (may be beyond run envelope) |
+| `tier-w` | Watch / optional |
+
 ## Adding an entry
 
 1. Confirm eligibility in `collection-policy.md`.
-2. Create `catalog/{org}__{name}__{short_revision}.yaml` using fields from `schema.yaml`.
-3. Set `status: candidate` (or `approved` if already signed off).
-4. After download and checksums: set `collected` / `verified` and `collection.storage_path`.
+2. Create `catalog/{org}__{name}__….yaml` from `schema.yaml` **including `profile` + `swot`**.
+3. Set `profile.original_source` to the canonical card/release URL.
+4. Set `status: candidate` (or `approved` if signed off).
+5. After download: `collected` / `verified` + `collection.storage_path`.
 
-## Empty catalog
+## Related
 
-The catalog starts empty on purpose. First entries come from daily brief
-**collection candidates** after policy checks — not from bulk scraping.
+- Collection plan: `research/2026-07-24-nas-strategic-collection-plan.md`
+- Strategic NAS decision: `docs/decisions/2026-07-24-nas-strategic-reserve.md`

inventory/catalog/BAAI__bge-m3__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/BAAI/bge-m3
   revision: main
   model_card_url: https://huggingface.co/BAAI/bge-m3
+  project_url: https://github.com/FlagOpen/FlagEmbedding
 license:
   spdx: MIT
   url: https://huggingface.co/BAAI/bge-m3
@@ -14,6 +15,41 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: ""
+profile:
+  summary: "Default multilingual dense embedding model for local RAG."
+  original_source: https://huggingface.co/BAAI/bge-m3
+  use_cases:
+    - "NetKingdom / Coulomb document and wiki retrieval"
+    - "Multilingual (DE/EN/…) semantic search"
+    - "Hybrid retrieval experiments (dense + multi-granularity features)"
+    - "Offline RAG in sandboxed agents"
+    - "Indexing ops runbooks and code comments"
+  sweet_spots:
+    - "Self-hosted production-quality multilingual embed"
+    - "MIT license; easy ops story"
+    - "Strong general retrieval without API spend"
+    - "Pairs cleanly with a small cross-encoder reranker"
+  not_ideal_for:
+    - "Generation / chat (not an LLM)"
+    - "Pure code retrieval if a code-specialized embed clearly wins A/B"
+    - "Ultra-tiny edge when EmbeddingGemma-class is enough"
+  capability_notes: >
+    BAAI BGE-M3 is a staple open embedder: multi-lingual, multi-granularity,
+    widely deployed for RAG. Primary R-tier retrieval backbone for the lab.
+swot:
+  strengths:
+    - "Proven multilingual retrieval quality for self-host"
+    - "MIT; small enough for CPU/GPU flexibility"
+    - "Mature FlagEmbedding ecosystem"
+  weaknesses:
+    - "Not optimized solely for code or for extreme long-context embed niches"
+    - "Newer embed lines (Qwen/Gemma/Nomic) may win specific A/B tests"
+  opportunities:
+    - "Domain-adapted embed fine-tune on Coulomb corpora later"
+    - "Companion bge-reranker for precision@k gains"
+  threats:
+    - "Leaderboard churn; risk of holding a stale 'default' without re-eval"
+    - "Pipeline lock-in if vector DBs assume fixed dimension without migration plan"
 size:
   total_bytes: 0
   total_human: "~2 GB"
@@ -31,12 +67,17 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 multilingual embedding staple for local RAG (docs, ops notes, DE/EN)."
-tags: [embedding, multilingual, rag]
-companions: []
+reason: "P0/R multilingual embedding staple for local RAG (docs, ops notes, DE/EN)."
+tags: [tier-r, embedding, multilingual, rag]
+companions:
+  - BAAI__bge-reranker-v2-m3__candidate
 notes: "Small download — within agent auto-collect band after license check once storage pinned."
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/BAAI__bge-reranker-v2-m3__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/BAAI/bge-reranker-v2-m3
   revision: main
   model_card_url: https://huggingface.co/BAAI/bge-reranker-v2-m3
+  project_url: https://github.com/FlagOpen/FlagEmbedding
 license:
   spdx: Apache-2.0
   url: https://huggingface.co/BAAI/bge-reranker-v2-m3
@@ -14,6 +15,39 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: "Confirm SPDX on card."
+profile:
+  summary: "Cross-encoder reranker companion to BGE-M3 for higher-precision RAG."
+  original_source: https://huggingface.co/BAAI/bge-reranker-v2-m3
+  use_cases:
+    - "Second-stage rerank of top-k chunks from BGE-M3 (or hybrid) retrieval"
+    - "Improving answer grounding for NetKingdom / ops runbook Q&A"
+    - "Eval A/B of retrieval quality without changing the generator"
+    - "Low-cost precision boost on multilingual corpora"
+  sweet_spots:
+    - "Cheap RAG quality win after a solid bi-encoder"
+    - "Small VRAM; easy always-on sidecar"
+    - "Same BAAI/FlagEmbedding family as BGE-M3"
+  not_ideal_for:
+    - "First-stage retrieval over full corpora (too slow as sole retriever)"
+    - "Generation / chat"
+    - "When latency budget forbids a second pass"
+  capability_notes: >
+    Lightweight multilingual cross-encoder from the BGE family. W-tier optional
+    after R spine; high value per byte when RAG is a primary workload.
+swot:
+  strengths:
+    - "Material precision lift for little disk/VRAM"
+    - "Proven pairing with BGE-M3"
+    - "Apache-friendly open stack"
+  weaknesses:
+    - "Cross-encoder cost scales with candidate count"
+    - "Not a substitute for better chunking or generators"
+  opportunities:
+    - "Default two-stage pipeline for lab RAG demos"
+    - "Tune candidate depth (k) per latency tier"
+  threats:
+    - "Newer BGE / open rerankers may supersede this checkpoint"
+    - "LLM-as-judge rerank experiments may reduce need for a dedicated model"
 size:
   total_bytes: 0
   total_human: "~1–2 GB (estimate)"
@@ -31,8 +65,8 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P1 — cheap RAG quality win as companion to BGE-M3."
-tags: [reranker, rag]
+reason: "P1/W — cheap RAG quality win as companion to BGE-M3."
+tags: [tier-w, reranker, rag]
 companions:
   - BAAI__bge-m3__candidate
 notes: ""
@@ -41,3 +75,7 @@ history:
     event: nominated
     by: baseline-survey
     detail: "P1 recommendation from initial deep research."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/Qwen__Qwen3-14B__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/Qwen/Qwen3-14B
   revision: main
   model_card_url: https://huggingface.co/Qwen/Qwen3-14B
+  project_url: https://qwenlm.github.io/
 license:
   spdx: Apache-2.0
   url: https://huggingface.co/Qwen/Qwen3-14B
@@ -14,6 +15,40 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: "Confirm card at download."
+profile:
+  summary: "Mid-size Qwen3 step up from 8B for stronger single-GPU chat/code."
+  original_source: https://huggingface.co/Qwen/Qwen3-14B
+  use_cases:
+    - "Higher-quality local assistant when 8B is the bottleneck"
+    - "Harder coding and long-context drafting on T2 GPUs"
+    - "A/B baseline vs R1-Distill-14B (general vs reason-specialist)"
+    - "Domain FT when 8B capacity is insufficient"
+  sweet_spots:
+    - "Quality step within still-single-GPU open dense class"
+    - "Multilingual instruct continuity with Qwen3-8B"
+    - "Quota-friendly alternative to jumping to 70B"
+  not_ideal_for:
+    - "Always-on default if VRAM is tight (prefer 8B)"
+    - "Deepest reasoner tasks (prefer R1 distill or full R1 reserve)"
+    - "Embedding / retrieval"
+  capability_notes: >
+    W-tier optional after R spine. Collect when soft-quota headroom and a clear
+    quality gap vs Qwen3-8B show up in daily work. Prefer Instruct sibling if
+    separate at pin time.
+swot:
+  strengths:
+    - "Clear capability bump over 8B without 70B cost"
+    - "Same Qwen3 stack and tooling as the R default"
+    - "Apache-friendly licensing typical"
+  weaknesses:
+    - "Near-duplicate niche vs strong 8B + selective 14B reason distill"
+    - "Still not frontier closed quality on hard agentic SWE"
+  opportunities:
+    - "Promote to R if lab defaults move off 8B"
+    - "FT target when domain data needs more capacity"
+  threats:
+    - "Disk spent better on S1 MoE or 70B dense under tight quota"
+    - "Next Qwen mid-size may obsolete this checkpoint quickly"
 size:
   total_bytes: 0
   total_human: "~28 GB fp16 / ~9 GB Q4 (estimate)"
@@ -31,12 +66,17 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P1 — stronger single-GPU chat/code when quota allows after P0."
-tags: [instruct, qwen3]
-companions: []
+reason: "P1/W — stronger single-GPU chat/code when quota allows after R spine."
+tags: [tier-w, instruct, qwen3]
+companions:
+  - Qwen__Qwen3-8B__candidate
 notes: ""
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P1 recommendation from initial deep research."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/Qwen__Qwen3-72B__strategic.yaml

@@ -0,0 +1,83 @@
+id: Qwen__Qwen3-72B__strategic
+status: candidate
+name: Qwen3-72B
+org: Qwen
+source:
+  kind: huggingface
+  url: https://huggingface.co/Qwen/Qwen3-72B
+  revision: main
+  model_card_url: https://huggingface.co/Qwen/Qwen3-72B
+  project_url: https://qwenlm.github.io/
+license:
+  spdx: Apache-2.0
+  url: https://huggingface.co/Qwen/Qwen3-72B
+  allows_offline_retention: true
+  allows_local_ops: true
+  allows_fine_tune: true
+  notes: "Confirm card; use Instruct/Coder sibling if that is the capability peak for the line."
+profile:
+  summary: "Strategic large Qwen dense — multilingual/tool open 70B-class reserve."
+  original_source: https://huggingface.co/Qwen/Qwen3-72B
+  use_cases:
+    - "Future multi-GPU dense instruct without MoE serving complexity"
+    - "Strong multilingual (DE/EN) offline assistant at 70B scale"
+    - "Domain FT base when 8B/14B capacity is too small"
+    - "Dense alternative to MoE giants for portable lab stacks"
+  sweet_spots:
+    - "S4 dense open with Qwen tooling continuity from the R 8B default"
+    - "Easier multi-GPU dense serve story than 600B MoE"
+    - "Apache-friendly licensing typical of Qwen3 line"
+  not_ideal_for:
+    - "Current default single-GPU ops"
+    - "Collecting both this and Llama-70B-class first under tight quota"
+    - "Embedding / retrieval"
+  capability_notes: >
+    Large dense strategic pick. Prefer Instruct/Coder sibling if that is the line
+    peak at pin time. If soft quota is tight after S1, pick at most one of
+    Qwen-72B vs Llama-70B-class first.
+swot:
+  strengths:
+    - "High multilingual dense quality in open 70B class"
+    - "Same family as R-tier Qwen3-8B — transfer of prompts/tools"
+    - "Usually simpler ops than giant MoE"
+  weaknesses:
+    - "~40–150 GiB depending on quant; still heavy"
+    - "May lag top MoE on some frontier-open benches"
+  opportunities:
+    - "First dense strategic fill after R + optional S1 compressed"
+    - "FT / LoRA at 70B when facility upgrades"
+  threats:
+    - "Llama 70B / next Qwen large may be better use of same bytes"
+    - "Supersession by Qwen next large release"
+size:
+  total_bytes: 0
+  total_human: "~145 GB fp16 / ~40 GB Q4 (estimate)"
+hardware_class:
+  min_vram_gb_q4: 40
+  min_vram_gb_fp16: 145
+  notes: "T3+ to run; strategic dense multilingual open."
+axes: [A, B, C]
+priority: high
+collection:
+  approved_by: ""
+  approved_at: null
+  downloaded_at: null
+  downloaded_by: ""
+  storage_path: ""
+  brief_refs:
+    - research/2026-07-24-nas-strategic-collection-plan.md
+    - docs/decisions/2026-07-24-nas-strategic-reserve.md
+reason: "S4 strategic — large Qwen dense open for multilingual/tool capability reserve."
+tags: [tier-s, strategic, dense, qwen3]
+companions:
+  - Qwen__Qwen3-8B__candidate
+notes: "Pick at most one of Llama-70B-class vs Qwen-72B-class first if quota tight after S1 MoE."
+history:
+  - at: "2026-07-24"
+    event: nominated
+    by: operator-policy
+    detail: "Strategic dense open for NAS."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/Qwen__Qwen3-8B__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/Qwen/Qwen3-8B
   revision: main
   model_card_url: https://huggingface.co/Qwen/Qwen3-8B
+  project_url: https://qwenlm.github.io/
 license:
   spdx: Apache-2.0
   url: https://huggingface.co/Qwen/Qwen3-8B
@@ -14,6 +15,42 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: "Confirm exact card license at download time; Qwen3 line generally Apache-2.0."
+profile:
+  summary: "Default mid-small open instruct for local ops, tools, and domain fine-tunes."
+  original_source: https://huggingface.co/Qwen/Qwen3-8B
+  use_cases:
+    - "Local NetKingdom / Coulomb assistant (chat, docs, runbooks)"
+    - "Tool-using agent loops on consumer GPU"
+    - "QLoRA / LoRA domain specialization base"
+    - "Multilingual (incl. DE/EN) drafting and summarization"
+    - "Offline code help when 14B+ is too heavy"
+  sweet_spots:
+    - "Best balance of quality vs VRAM in the ~8B open class for many 2026 stacks"
+    - "Instruction + tool-use oriented workflows"
+    - "Homelab fine-tune target (axis C)"
+    - "Runnable spine default when one model must wear many hats"
+  not_ideal_for:
+    - "Hardest SWE-bench-class multi-file engineering (use larger or closed frontier)"
+    - "Deep multi-step math/reason vs R1-class distill or full reasoners"
+    - "Embedding / retrieval (use BGE-M3 or nomic)"
+  capability_notes: >
+    Flagship small-mid dense open generalist in the Qwen3 line. Strong multilingual
+    and instruct behavior for its size; primary R-tier workhorse for the lab. Prefer
+    Instruct sibling on the card if separate repo exists at pin time.
+swot:
+  strengths:
+    - "High capability density at 8B; Apache-friendly licensing typical"
+    - "Good multilingual + tool/instruct posture for local agents"
+    - "Excellent FT base for domain specialization"
+  weaknesses:
+    - "Still far from frontier closed models on hard agentic coding"
+    - "8B ceiling on long-horizon planning and rare knowledge"
+  opportunities:
+    - "Domain LoRAs (security, ops, railiance) on NAS-held base"
+    - "Pair with BGE-M3 RAG for grounded NetKingdom answers"
+  threats:
+    - "Rapid supersession by next Qwen/peer 8–14B release"
+    - "Quant quality variance across third-party GGUF repacks"
 size:
   total_bytes: 0
   total_human: "~16 GB fp16 / ~5 GB Q4 (estimate)"
@@ -31,8 +68,8 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 spine — best default open general/tool model for local ops and QLoRA domain specialization."
-tags: [instruct, text, qwen3, ft-base]
+reason: "P0/R spine — best default open general/tool model for local ops and QLoRA domain specialization."
+tags: [tier-r, instruct, text, qwen3, ft-base]
 companions: []
 notes: "Prefer Instruct variant on card if separate repo; pin commit SHA at collection."
 history:
@@ -40,3 +77,7 @@ history:
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/deepseek-ai__DeepSeek-R1-Distill-Qwen-14B__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
   revision: main
   model_card_url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+  project_url: https://github.com/deepseek-ai/DeepSeek-R1
 license:
   spdx: MIT
   url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
@@ -14,6 +15,42 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: "R1 distill series MIT — confirm card at pin time."
+profile:
+  summary: "Local reasoning-specialist distill — hard tasks without full R1 MoE."
+  original_source: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+  use_cases:
+    - "Offline multi-step reasoning (math, logic, planning sketches)"
+    - "Harder agent tool-selection and self-checks"
+    - "Local alternative when DeepSeek API is unavailable"
+    - "Eval harness baseline for 'reason' quality on lab tasks"
+    - "QLoRA experiments on reason-style behavior"
+  sweet_spots:
+    - "Reasoning quality above plain 14B chat models at similar size"
+    - "Single enthusiast GPU (Q4) without multi-node MoE"
+    - "MIT-friendly open reason lineage"
+  not_ideal_for:
+    - "Cheapest always-on chat (use 3B/8B)"
+    - "Full parity with frontier closed reasoners or full R1"
+    - "Pure embedding"
+  capability_notes: >
+    Distilled from DeepSeek-R1 into a Qwen-14B-class dense student. Captures much
+    of the reasoner value proposition for homelab VRAM budgets. Primary R-tier
+    reason model; full R1 remains strategic S-tier.
+swot:
+  strengths:
+    - "Strong open reasoner at single-GPU scale"
+    - "MIT distill story; clear provenance from R1 line"
+    - "Complements general 8B chat without replacing it"
+  weaknesses:
+    - "Heavier and slower than 8B for routine chat"
+    - "Distill ≠ full R1; still loses on hardest problems"
+    - "May overthink simple tasks if not prompted carefully"
+  opportunities:
+    - "Route only hard queries here; keep 8B as default"
+    - "Domain FT for structured ops decision checklists"
+  threats:
+    - "Newer distill or mid-size reasoners may obsolete this checkpoint"
+    - "VRAM growth of defaults may push lab to 32B distill instead"
 size:
   total_bytes: 0
   total_human: "~28 GB fp16 / ~9 GB Q4 (estimate)"
@@ -31,12 +68,17 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 local reasoning without full R1 MoE — agent/tool loops and harder offline tasks."
-tags: [reasoning, distill, deepseek]
-companions: []
-notes: "If disk/VRAM constrained, substitute DeepSeek-R1-Distill-Qwen-8B as P0 alternate."
+reason: "P0/R local reasoning without full R1 MoE — agent/tool loops and harder offline tasks."
+tags: [tier-r, reasoning, distill, deepseek]
+companions:
+  - deepseek-ai__DeepSeek-R1__strategic
+notes: "If disk/VRAM constrained, substitute DeepSeek-R1-Distill-Qwen-8B as R alternate."
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/deepseek-ai__DeepSeek-R1-Distill-Qwen-32B__candidate.yaml

@@ -7,13 +7,47 @@ source:
   url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
   revision: main
   model_card_url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+  project_url: https://github.com/deepseek-ai/DeepSeek-R1
 license:
   spdx: MIT
   url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
   allows_offline_retention: true
   allows_local_ops: true
   allows_fine_tune: true
-  notes: ""
+  notes: "R1 distill series MIT — confirm card at pin time."
+profile:
+  summary: "Larger R1 distill for stronger local reason when 14B is not enough."
+  original_source: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+  use_cases:
+    - "Hard offline reasoning beyond 14B distill quality"
+    - "Local eval ceiling before considering full R1 MoE"
+    - "Heavier agent planning loops on multi-GPU / high-VRAM hosts"
+    - "FT experiments on reason-style traces at 32B"
+  sweet_spots:
+    - "Best dense open reason step between 14B distill and full R1"
+    - "Still denser/portable than 600B-class MoE"
+    - "MIT-friendly R1 lineage"
+  not_ideal_for:
+    - "Default always-on chat (too heavy)"
+    - "Hosts without ~20+ GB VRAM for Q4"
+    - "When 14B distill already saturates task quality"
+  capability_notes: >
+    W-tier optional. Approve only with VRAM + soft-quota headroom after R spine
+    and primary S pulls. Do not confuse with full DeepSeek-R1 MoE (separate S entry).
+swot:
+  strengths:
+    - "Material reason quality jump over 14B distill"
+    - "Dense, so simpler serving than full MoE"
+    - "Clear provenance in R1 distill family"
+  weaknesses:
+    - "VRAM and latency cost; poor default chat model"
+    - "Still not full R1; diminishing returns vs S2 full weights"
+  opportunities:
+    - "Route only hardest local tasks here"
+    - "Bridge until multi-GPU can host full R1"
+  threats:
+    - "Quota competition with S1/S4 strategic weights"
+    - "Newer mid-large open reasoners may leapfrog"
 size:
   total_bytes: 0
   total_human: "~65 GB fp16 / ~20 GB Q4 (estimate)"
@@ -31,12 +65,18 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P1 stronger local reasoner — approve only with VRAM + quota headroom."
-tags: [reasoning, distill, deepseek]
-companions: []
+reason: "P1/W stronger local reasoner — approve only with VRAM + quota headroom."
+tags: [tier-w, reasoning, distill, deepseek]
+companions:
+  - deepseek-ai__DeepSeek-R1-Distill-Qwen-14B__candidate
+  - deepseek-ai__DeepSeek-R1__strategic
 notes: "Do not collect full DeepSeek-V3/R1 MoE under this id."
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P1 recommendation from initial deep research."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/deepseek-ai__DeepSeek-R1__strategic.yaml

@@ -0,0 +1,84 @@
+id: deepseek-ai__DeepSeek-R1__strategic
+status: candidate
+name: DeepSeek-R1
+org: deepseek-ai
+source:
+  kind: huggingface
+  url: https://huggingface.co/deepseek-ai/DeepSeek-R1
+  revision: main
+  model_card_url: https://huggingface.co/deepseek-ai/DeepSeek-R1
+  project_url: https://github.com/deepseek-ai/DeepSeek-R1
+license:
+  spdx: MIT
+  url: https://huggingface.co/deepseek-ai/DeepSeek-R1
+  allows_offline_retention: true
+  allows_local_ops: true
+  allows_fine_tune: true
+  notes: "MIT for R1 series per public release notes; re-confirm card at pin."
+profile:
+  summary: "Strategic full open reasoner — best-known open R1-class weights for future multi-GPU."
+  original_source: https://huggingface.co/deepseek-ai/DeepSeek-R1
+  use_cases:
+    - "Offline retention of top open reasoner lineage"
+    - "Future local hosted reason when multi-GPU is available"
+    - "Teacher / reference for distill and eval harnesses"
+    - "Hard planning and math when API reasoners are unavailable"
+  sweet_spots:
+    - "S2 pure-reason niche on the reserve"
+    - "Complements V3 general MoE rather than replacing the 14B distill spine"
+    - "MIT-friendly open reasoner identity"
+  not_ideal_for:
+    - "Current single-GPU daily reason (use R1-Distill-14B)"
+    - "Full-precision pull if soft quota already holds S1 giant"
+    - "Casual chat default"
+  capability_notes: >
+    Full DeepSeek-R1 (or open successor reasoner). Distills cover runnable reason;
+    this entry is strategic optionality. If quota forces a choice between full R1
+    and full V3, re-evaluate from latest brief which niche is the larger gap.
+swot:
+  strengths:
+    - "Reference open reasoner class for the era"
+    - "Distill ecosystem already proves value at smaller sizes"
+    - "Open weights for offline continuity"
+  weaknesses:
+    - "Enormous resource cost to serve"
+    - "Overlaps capability budget with S1 MoE on a small disk"
+    - "Version supersession risk"
+  opportunities:
+    - "Hold compressed if needed; keep identity for later upgrade"
+    - "Use as teacher target for lab distill experiments"
+  threats:
+    - "Quota: competing with S1 for hundreds of GiB"
+    - "Newer open reasoners may displace this checkpoint"
+size:
+  total_bytes: 0
+  total_human: "large — similar class to V3 full; prefer compressed if needed for 850 GiB soft quota"
+hardware_class:
+  min_vram_gb_q4: 0
+  min_vram_gb_fp16: 0
+  notes: "Beyond current lab run envelope. Distills (14B/32B) cover runnable reason; this is full strategic reasoner."
+axes: [A, B, C]
+priority: high
+collection:
+  approved_by: ""
+  approved_at: null
+  downloaded_at: null
+  downloaded_by: ""
+  storage_path: ""
+  brief_refs:
+    - research/2026-07-24-nas-strategic-collection-plan.md
+    - docs/decisions/2026-07-24-nas-strategic-reserve.md
+reason: "S2 strategic — best-known open reasoner lineage; optionality for future multi-GPU / hosted local reason."
+tags: [tier-s, strategic, beyond-run-envelope, reasoning]
+companions:
+  - deepseek-ai__DeepSeek-R1-Distill-Qwen-14B__candidate
+notes: "If quota forces a choice between full R1 and full V3, prefer the stronger current open general (often V3/V4 line) unless pure reason is the gap; re-evaluate at pull time from latest brief."
+history:
+  - at: "2026-07-24"
+    event: nominated
+    by: operator-policy
+    detail: "Strategic NAS reserve — unrunnable OK."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/deepseek-ai__DeepSeek-V3__strategic.yaml

@@ -0,0 +1,83 @@
+id: deepseek-ai__DeepSeek-V3__strategic
+status: candidate
+name: DeepSeek-V3
+org: deepseek-ai
+source:
+  kind: huggingface
+  url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  revision: main
+  model_card_url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  project_url: https://github.com/deepseek-ai/DeepSeek-V3
+license:
+  spdx: MIT
+  url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  allows_offline_retention: true
+  allows_local_ops: true
+  allows_fine_tune: true
+  notes: "Confirm active card license at pin; prefer latest V3.x/V4 open successor if it is the new SOTA open general."
+profile:
+  summary: "Strategic open general MoE — among the most capable open chat/code weights."
+  original_source: https://huggingface.co/deepseek-ai/DeepSeek-V3
+  use_cases:
+    - "Long-term offline optionality if frontier API access is lost or restricted"
+    - "Future multi-GPU / training-facility inference or continued pretrain"
+    - "Capability benchmark reference against closed frontier"
+    - "Source of student weights / distill experiments (when license + hardware allow)"
+  sweet_spots:
+    - "S1 primary giant: best open general MoE class on the reserve"
+    - "Capability identity even when only compressed weights fit disk"
+    - "MIT-friendly open SOTA lineage (confirm at pin)"
+  not_ideal_for:
+    - "Current lab single-GPU daily ops (use R spine)"
+    - "Storing multiple near-duplicate full-precision giants on one disk"
+    - "Assuming immediate local serve without major hardware upgrade"
+  capability_notes: >
+    Flagship open general MoE (or successor) for the strategic tier. On ~1–2 TB
+    lab bulk storage, prefer official compressed distributions when full precision
+    would exhaust soft quota. Catalog identity stays the base model id + revision.
+swot:
+  strengths:
+    - "Top-tier open general capability for the era"
+    - "Open weights enable true offline retention and future fine work"
+    - "Strong coding/agent relevance as hardware catches up"
+  weaknesses:
+    - "Huge disk and multi-GPU serve cost"
+    - "Rapid version churn (V3 → V3.x → V4)"
+    - "Quant choice materially affects quality"
+  opportunities:
+    - "Hold compressed primary; upgrade hardware later without re-scrape risk"
+    - "Anchor daily briefs against a fixed offline SOTA open baseline"
+  threats:
+    - "Soft quota: one full pull can block other S niches"
+    - "License or card terms change at successor releases — re-check at pin"
+size:
+  total_bytes: 0
+  total_human: "hundreds of GiB (quant) to multi-hundred+ GiB; measure at pull — may need official compressed release"
+hardware_class:
+  min_vram_gb_q4: 0
+  min_vram_gb_fp16: 0
+  notes: "Beyond current lab run envelope (T4+). Reserved for strategic capability, not current inference."
+axes: [A, B, C]
+priority: high
+collection:
+  approved_by: ""
+  approved_at: null
+  downloaded_at: null
+  downloaded_by: ""
+  storage_path: ""
+  brief_refs:
+    - research/2026-07-24-nas-strategic-collection-plan.md
+    - docs/decisions/2026-07-24-nas-strategic-reserve.md
+reason: "S1 strategic — among the most capable open general MoE weights; keep even if unrunnable today."
+tags: [tier-s, strategic, beyond-run-envelope, moe, general]
+companions: []
+notes: "At download: pin exact revision; if full precision exceeds remaining soft quota, collect best official compressed distribution of the same model line."
+history:
+  - at: "2026-07-24"
+    event: nominated
+    by: operator-policy
+    detail: "1TB NAS strategic reserve — capability-first, not run-envelope-gated."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/meta-llama__Llama-3.2-3B-Instruct__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
   revision: main
   model_card_url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
+  project_url: https://www.llama.com/
 license:
   spdx: custom
   url: https://ai.meta.com/llama/license/
@@ -14,6 +15,41 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: "Llama Community License — not MIT; review terms before commercial redistribution."
+profile:
+  summary: "Tiny edge instruct for always-on and low-VRAM agent micro-services."
+  original_source: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
+  use_cases:
+    - "Always-on local helper with minimal power/VRAM"
+    - "Fast classification, routing, and short-form rewrites"
+    - "CPU or 4–8 GB GPU edge boxes"
+    - "Smoke-test harnesses before promoting to 8B+"
+    - "Embedded tooling demos and offline kiosks"
+  sweet_spots:
+    - "Latency and footprint over peak intelligence"
+    - "Huge Llama ecosystem (llama.cpp, Ollama, many templates)"
+    - "Cheap parallel micro-agents in a fleet"
+  not_ideal_for:
+    - "Serious coding or long documents"
+    - "Domain FT when quality matters (prefer 8B base)"
+    - "Standalone reasoning-heavy ops decisions"
+  capability_notes: >
+    Compact Meta instruct model. Best as a floor for local intelligence and
+    high-volume simple tasks, not as the lab's primary brain.
+swot:
+  strengths:
+    - "Very small; runs almost anywhere in the lab"
+    - "Massive tooling and community template coverage"
+    - "Good enough for structured short outputs"
+  weaknesses:
+    - "Shallow capability; fails hard tasks silently if not constrained"
+    - "Custom Llama license (not pure OSS-permissive)"
+    - "HF gated access friction"
+  opportunities:
+    - "Fleet of specialized tiny adapters per workflow"
+    - "Guardrail / router model in front of larger backends"
+  threats:
+    - "Qwen/Gemma/Smol peers may outclass at same size"
+    - "License or regional policy changes for Llama family"
 size:
   total_bytes: 0
   total_human: "~6 GB fp16 / ~2 GB Q4 (estimate)"
@@ -31,8 +67,8 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 edge instruct — tiny, huge ecosystem, good CPU/GPU floor for agents."
-tags: [instruct, edge, llama]
+reason: "P0/R edge instruct — tiny, huge ecosystem, good CPU/GPU floor for agents."
+tags: [tier-r, instruct, edge, llama]
 companions: []
 notes: "HF gated model — need accepted license on account before download."
 history:
@@ -40,3 +76,7 @@ history:
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/meta-llama__Llama-3.3-70B-Instruct__strategic.yaml

@@ -0,0 +1,84 @@
+id: meta-llama__Llama-3.3-70B-Instruct__strategic
+status: candidate
+name: Llama-3.3-70B-Instruct
+org: meta-llama
+source:
+  kind: huggingface
+  url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+  revision: main
+  model_card_url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+  project_url: https://www.llama.com/
+license:
+  spdx: custom
+  url: https://ai.meta.com/llama/license/
+  allows_offline_retention: true
+  allows_local_ops: true
+  allows_fine_tune: true
+  notes: "Llama Community License — review before commercial redistribution. Prefer Llama 4 open text sibling if it is clearly stronger at pull time."
+profile:
+  summary: "Strategic dense Llama 70B instruct — ecosystem-rich open reserve for multi-GPU."
+  original_source: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+  use_cases:
+    - "Future multi-GPU general instruct with mature Llama tooling"
+    - "Portable dense alternative to giant MoE for lab upgrades"
+    - "Ecosystem baselines (vLLM, llama.cpp, eval harnesses expect Llama ids)"
+    - "Domain FT when Llama license fits the use"
+  sweet_spots:
+    - "S4 dense open with the broadest third-party tooling surface"
+    - "More portable serve path than 600B-class MoE"
+    - "Strong general instruct heritage in the 70B class"
+  not_ideal_for:
+    - "Current single-GPU daily ops (use 3B/8B R spine)"
+    - "Uses forbidden by Llama Community License terms"
+    - "Duplicate with Qwen-72B under tight quota — pick one first"
+  capability_notes: >
+    HF gated. Strategic dense pick. If Llama 4 open text weights supersede
+    clearly, nominate successor and mark this entry superseded. License is not
+    Apache — operator must re-read terms before redistribution.
+swot:
+  strengths:
+    - "Huge ecosystem and ops familiarity"
+    - "Solid 70B dense instruct quality"
+    - "Easier multi-GPU dense story than MoE giants"
+  weaknesses:
+    - "Custom Llama license (not Apache/MIT)"
+    - "HF gating friction for automated pulls"
+    - "May lag peer open MoE on some benches"
+  opportunities:
+    - "First dense strategic if Qwen-72B is deferred"
+    - "Broad eval comparability with published Llama numbers"
+  threats:
+    - "Llama 4 open may obsolete 3.3 quickly"
+    - "Quota competition with Qwen-72B and S1 MoE"
+size:
+  total_bytes: 0
+  total_human: "~140 GB fp16 / ~40 GB Q4 (estimate)"
+hardware_class:
+  min_vram_gb_q4: 40
+  min_vram_gb_fp16: 140
+  notes: "T3+ to run well; still valuable reserve for future multi-GPU."
+axes: [A, B, C]
+priority: high
+collection:
+  approved_by: ""
+  approved_at: null
+  downloaded_at: null
+  downloaded_by: ""
+  storage_path: ""
+  brief_refs:
+    - research/2026-07-24-nas-strategic-collection-plan.md
+    - docs/decisions/2026-07-24-nas-strategic-reserve.md
+reason: "S4 strategic dense — strong ecosystem 70B open instruct; more portable than full MoE for a future lab upgrade."
+tags: [tier-s, strategic, dense, instruct, llama]
+companions:
+  - meta-llama__Llama-3.2-3B-Instruct__candidate
+notes: "HF gated. If Llama 4 open weights supersede, nominate successor and supersede this entry."
+history:
+  - at: "2026-07-24"
+    event: nominated
+    by: operator-policy
+    detail: "Strategic dense open for NAS capability reserve."
+  - at: "2026-07-28"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/catalog/nomic-ai__nomic-embed-text-v1.5__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
   revision: main
   model_card_url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+  project_url: https://www.nomic.ai/
 license:
   spdx: Apache-2.0
   url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
@@ -14,6 +15,39 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: ""
+profile:
+  summary: "Lightweight open text embedder for A/B with BGE-M3 and long-context retrieval experiments."
+  original_source: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+  use_cases:
+    - "Secondary / A/B embedding path vs BGE-M3"
+    - "English-heavy RAG with low resource cost"
+    - "Long-context embedding experiments (per card capabilities)"
+    - "Portable laptop offline search indexes"
+  sweet_spots:
+    - "Small footprint Apache embed"
+    - "Fast iteration when re-embedding corpora often"
+    - "Nomic tooling and Matryoshka-style dimension flexibility (per card)"
+  not_ideal_for:
+    - "Sole multilingual production embed without A/B (prefer BGE-M3 default)"
+    - "Generation tasks"
+    - "Code-only retrieval (prefer code embed if held)"
+  capability_notes: >
+    Compact Nomic text embedding model. Held as runnable dual-path to avoid
+    single-vendor embed lock-in and to re-eval retrieval quality cheaply.
+    Prefer current v2 text card at pull if clearly better.
+swot:
+  strengths:
+    - "Tiny, Apache-2.0, easy to re-run indexes"
+    - "Diversifies embed dependency next to BGE-M3"
+  weaknesses:
+    - "May lose multilingual or domain A/B vs BGE-M3"
+    - "Version churn (v1.5 vs v2) needs careful pin"
+  opportunities:
+    - "Dimension-reduced storage for large corpora"
+    - "Hybrid ensembles (BGE + nomic signals)"
+  threats:
+    - "Becomes redundant if one embed wins all lab evals"
+    - "Upstream rename/deprecation of v1.5"
 size:
   total_bytes: 0
   total_human: "<1 GB"
@@ -31,12 +65,17 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 lightweight embed for A/B with BGE-M3; long-context text retrieval."
-tags: [embedding, rag]
-companions: []
+reason: "P0/R lightweight embed for A/B with BGE-M3; long-context text retrieval."
+tags: [tier-r, embedding, rag]
+companions:
+  - BAAI__bge-m3__candidate
 notes: "If v2 text is preferred at collection time, update id/url and supersede this candidate."
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."

inventory/collection-policy.md

@@ -1,48 +1,71 @@
 # Collection policy — open-weight reserve
 
-**Status:** foundation  
-**Related:** `schema.yaml`, `docs/backup-storage-policy.md`, `INTENT.md`
+**Status:** updated for 1 TB NAS + strategic capability reserve (2026-07-24)  
+**Related:** `schema.yaml`, `docs/backup-storage-policy.md`,
+`docs/decisions/2026-07-24-nas-strategic-reserve.md`, `INTENT.md`
 
 ---
 
 ## Purpose
 
 Decide **what** enters the open-weight reserve, **who** may approve it, and
-**when** a daily-brief candidate becomes a catalog entry with blobs on backup
-storage.
+**when** a daily-brief candidate becomes a catalog entry with blobs on the NAS.
 
 ---
 
 ## Goals
 
-* Keep a **small, high-leverage** reserve — not a Hugging Face mirror
+* Hold a **capability-first strategic reserve** of the best open weights we may
+  need later — **including models too large to run on current lab hardware**
+* Also hold a **runnable spine** for day-to-day local ops and fine-tunes
 * Enforce **license and integrity** before download completes
-* Match capacity to the backup storage soft quota
-* Prefer models that serve axes **B** and **C**, plus strategic **A** open releases
+* Stay within the **1 TB NAS soft quota (850 GiB)** — quality over mirror volume
+* Prefer official provenance; not a full Hugging Face scrape
+
+---
+
+## Two envelopes (do not conflate)
+
+| Envelope | Question | Effect on collection |
+| -------- | -------- | -------------------- |
+| **Run** | Can we infer / FT this *now*? | Tags `hardware_class`, prioritizes R tier for local work |
+| **Reserve** | Is this among the best open artifacts worth keeping *just in case*? | **Not gated by current VRAM** |
+
+**Runnability is not an eligibility gate.** An unrunnable SOTA open MoE can be
+priority **high** if license and quota allow.
 
 ---
 
 ## Eligibility (must pass all)
 
-1. **Open weights** — weights obtainable under terms that allow offline retention for lab use
-2. **Clear license** — SPDX or linkable license text; `allows_offline_retention: true`
-3. **Stable provenance** — official org, tagged release, or commit revision (not anonymous drive-by reupload as sole source)
-4. **Lab rationale** — written `reason` tied to at least one axis A–D (usually B/C)
-5. **Capacity** — estimated size fits under remaining soft quota (see backup storage policy)
+1. **Open weights** — obtainable under terms that allow offline retention for lab use  
+2. **Clear license** — SPDX or linkable license text; `allows_offline_retention: true`  
+3. **Stable provenance** — official org, tagged release, or commit revision  
+4. **Lab rationale** — written `reason` (capability SOTA, runnable spine, embed, FT base, …)  
+5. **Capacity** — estimated size fits under remaining soft quota (850 GiB)
 
-Fail any gate → status `rejected` with reason, or never enter catalog.
+Fail any gate → `rejected` or never enter catalog.
 
 ---
 
-## Priority rubric
+## Collection tiers
+
+| Tier | Code | Meaning |
+| ---- | ---- | ------- |
+| **Runnable spine** | **R** | Default local chat/code/embed/FT bases; keep resident |
+| **Strategic capability** | **S** | Most capable open models (often large); may be T4+ only to *run* |
+| **Watch / optional** | **W** | Secondary; collect only with clear headroom |
+
+Catalog field: use `tags` including `tier-r` / `tier-s` / `tier-w` and
+`priority: high|medium|low`.
+
+### Priority rubric
 
 | Priority | Guidance |
 | -------- | -------- |
-| **high** | Rare or strategically important; license/access risk of disappearance; uniquely strong for B/C at our hardware class; hard to re-obtain |
-| **medium** | Clear lab use within 1–2 quarters; good quality/cost; easy enough to re-download but worth having cold |
-| **low** | Nice to have; only collect if quota headroom is large and pull is cheap |
-
-Daily brief **collection candidates** should set a suggested priority; approval may change it.
+| **high** | Top open capability (S) or essential runnable spine (R); hard to re-obtain; license/access risk |
+| **medium** | Strong but not unique; mid-size upgrades; companions (rerankers) |
+| **low** | Nice-to-have; W tier |
 
 ---
 
@@ -50,16 +73,48 @@ Daily brief **collection candidates** should set a suggested priority; approval
 
 | Estimated total size | Approval |
 | -------------------- | -------- |
-| **< 5 GiB** | Operator or lab agent may collect after license check; catalog entry required before or immediately after |
-| **5–40 GiB** | Explicit operator approval (chat, workplan task, or signed catalog `approved_by`) |
-| **> 40 GiB** | Operator approval **plus** check against soft quota and whether a smaller quant/variant suffices |
-| **Any size if quota ≥ 70% used** | Operator approval required regardless of size |
-| **Unclear license or ToS risk** | Do not collect; status `rejected` |
+| **< 5 GiB** | Operator or lab agent after license check |
+| **5–40 GiB** | Explicit operator approval |
+| **> 40 GiB** | Operator approval + remaining soft-quota check |
+| **> 200 GiB (typical S giants)** | Operator approval + written note on which other S models may need to wait |
+| **Any size if quota ≥ 70% used** | Operator approval required |
+| **Unclear license or ToS risk** | Do not collect; `rejected` |
 
-“Operator” means the human lab owner (or a documented delegate). Agents may
-**nominate** (`status: candidate`) freely from briefs; they may **collect** only
-within the < 5 GiB band when licenses are unambiguous — otherwise stop at
-`candidate` / `approved`.
+Agents may **nominate** freely; they **collect** only in the < 5 GiB band with
+unambiguous licenses unless the operator has approved the catalog entry.
+
+---
+
+## What we prefer to collect
+
+### Runnable spine (R)
+
+* Small/mid instruct and code models that fit the run envelope  
+* Strong embedding / rerank models for local RAG  
+* Bases known to fine-tune well under QLoRA on lab GPUs  
+
+### Strategic capability (S)
+
+* **Best available open general / reasoning / code weights** at the frontier of open  
+* Large MoE or dense models even if current infra cannot serve them  
+* Prefer official compressed releases (FP8, published quant) when full precision
+  would exhaust the 1 TB NAS  
+* One clear “best open” per capability niche is better than five near-duplicates  
+
+### Companions
+
+Tokenizers, LoRA adapters, small eval fixtures when required to use a reserved base.
+
+---
+
+## What we usually skip
+
+* Duplicate quants of the same revision already reserved  
+* Anonymous merges/repacks without provenance  
+* Closed weights  
+* Entire org mirrors  
+* Giant pretraining corpora (default out of band unless separately justified)  
+* Anything whose license forbids offline retention  
 
 ---
 
@@ -67,68 +122,30 @@ within the < 5 GiB band when licenses are unambiguous — otherwise stop at
 
 ```text
 brief nominates
-    → candidate          (catalog YAML, no blobs required)
-    → approved           (license + size + quota OK)
-    → collecting         (download in staging/)
-    → collected          (blobs complete, checksums recorded, storage_path set)
-    → verified           (optional re-hash / smoke load)
-    → superseded|evicted (replaced or removed; metadata kept)
+    → candidate
+    → approved
+    → collecting     (NAS staging/)
+    → collected
+    → verified
+    → superseded|evicted
 ```
 
-Rejected candidates stay in catalog only if useful as a decision record; otherwise omit.
-
----
-
-## What we prefer to collect
-
-* Small/mid instruct and code models that fit the hardware envelope
-* Strong embedding / rerank models for local RAG
-* Base models known to fine-tune well under QLoRA/LoRA on lab GPUs
-* Official quant releases when they are the supported distribution
-* Adapters and tokenizers that unlock a reserved base (as companions)
-
-## What we usually skip
-
-* Duplicate quants of the same revision already reserved
-* Huge models with no near-term local run/train path and no access-risk story
-* Merges/repacks without provenance
-* Datasets larger than model weights unless separately justified (default: out of band)
-* Anything requiring acceptance flows we cannot satisfy offline
-
----
-
-## Companions
-
-Tokenizers, LoRA adapters, and small eval fixtures may be collected when:
-
-* they are required to use a reserved base, or
-* they are small (< 1 GiB) and high leverage
-
-Link via `companions` in the catalog schema.
-
 ---
 
 ## Brief integration
 
-1. Brief section **Collection candidates** nominates items.
-2. Operator/agent opens `inventory/catalog/{id}.yaml` with `status: candidate`.
-3. Approval and download follow this policy and `docs/backup-storage-policy.md`.
-4. Brief `brief_refs` on the entry point back to the nominating day(s).
+1. Brief **Collection candidates** nominates R/S/W.  
+2. Catalog YAML under `inventory/catalog/`.  
+3. Download only after storage path is pinned (`docs/backup-storage-policy.md`) and approval rules pass.  
+4. `collection.brief_refs` / research refs for provenance of the nomination.
 
 ---
 
 ## Eviction rule of thumb
 
-When over quota or cleaning:
+When over soft quota:
 
-1. `low` priority, easily re-obtainable from still-live official URLs
-2. Superseded revisions with a newer `verified` replacement
-3. Never silent-delete: set `status: evicted`, clear or note `storage_path`, append `history`
-
----
-
-## Non-goals
-
-* Automatic bulk mirrors of entire orgs
-* Collecting on every brief mention without priority
-* Bypassing license gates for “research only” convenience
+1. **W** tier and easily re-obtainable duplicates  
+2. Superseded revisions with a stronger verified successor  
+3. Never silent-delete **S** SOTA or sole **R** spine without operator note  
+4. Always set `status: evicted` and append `history`

inventory/schema.yaml

@@ -3,7 +3,7 @@
 # Filename suggestion: {org}__{name}__{short_revision}.yaml
 #
 # Schema version documents field meaning for humans and future validators.
-schema_version: "0.1.0"
+schema_version: "0.2.0"
 
 # ---------------------------------------------------------------------------
 # Example entry (illustrative only — not a real collection claim)
@@ -15,60 +15,56 @@ schema_version: "0.1.0"
 # source:
 #   kind: huggingface  # huggingface | github_release | direct_url | other
 #   url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
-#   revision: abc1234def...   # commit sha, tag, or release id
+#   revision: abc1234def...
 #   model_card_url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
-# license:
-#   spdx: llama3.2          # or MIT, Apache-2.0, etc.; use "custom" + notes if needed
-#   url: https://...
-#   allows_offline_retention: true
-#   allows_local_ops: true
-#   allows_fine_tune: true
-#   notes: ""
-# artifacts:
-#   - path: blobs/model.safetensors
-#     sha256: "..."
-#     bytes: 0
-# size:
-#   total_bytes: 0
-#   total_human: "0 B"
-# hardware_class:
-#   # Rough lab guidance — not a guarantee
-#   min_vram_gb_q4: 4
-#   min_vram_gb_fp16: 8
-#   notes: "fits consumer 8GB at Q4"
-# axes:                    # why this matters to the lab
-#   - B
-#   - C
-# priority: medium         # low | medium | high
-# collection:
-#   approved_by: ""
-#   approved_at: null      # ISO-8601 date
-#   downloaded_at: null
-#   downloaded_by: ""
-#   storage_path: ""       # absolute or facility-relative path on backup storage
-#   brief_refs:            # briefs that nominated this candidate
-#     - briefs/2026/07/2026-07-23.md
-# reason: "Strong small instruct base; clear license; homelab-fit."
-# tags:
-#   - instruct
-#   - text
-# companions: []           # optional related catalog ids (adapters, tokenizers)
+#   project_url: https://www.llama.com/   # optional: org project / paper home
+#   paper_url: https://arxiv.org/...     # optional
+# license: { ... }
+# profile:
+#   summary: "One-line what this model is for."
+#   original_source: "https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct"
+#   use_cases:
+#     - "Always-on local assistant on small VRAM"
+#     - "Edge classification and simple tool routing"
+#   sweet_spots:
+#     - "Latency-sensitive chat under 3–8 GB VRAM"
+#     - "Teaching / prototyping agent loops cheaply"
+#   not_ideal_for:
+#     - "Hard multi-file software engineering"
+#     - "Long-horizon reasoning"
+#   capability_notes: >
+#     Compact instruct model; strong ecosystem tooling; limited depth vs mid-size.
+# swot:
+#   strengths:
+#     - "Tiny footprint; huge Llama tooling ecosystem"
+#   weaknesses:
+#     - "Shallow reasoning and coding vs 8B–70B class"
+#   opportunities:
+#     - "Domain LoRA for NetKingdom micro-agents"
+#   threats:
+#     - "Superseded quickly by next small open release"
+# size: { ... }
+# hardware_class: { ... }
+# axes: [B]
+# priority: high
+# collection: { ... }
+# reason: "..."
+# tags: [tier-r, instruct]
+# companions: []
 # notes: ""
-# history:
-#   - at: "2026-07-23"
-#     event: nominated
-#     by: operator
-#     detail: "From daily brief collection candidates."
+# history: []
 
 # ---------------------------------------------------------------------------
-# Required fields by status (normative for humans; tooling may enforce later)
+# Required fields by status
 # ---------------------------------------------------------------------------
-# candidate:  id, status, name, org, source.url, source.revision, license, reason, priority
+# candidate:  id, status, name, org, source.url, source.revision, license,
+#             reason, priority, profile (summary + original_source + use_cases +
+#             sweet_spots), swot (all four lists, compact)
 # approved:   + collection.approved_by, collection.approved_at
-# collected:  + collection.downloaded_at, collection.storage_path, size, artifacts[].sha256
-# verified:   + checksums re-read OK (notes or history event)
-# rejected:   + reason (why rejected)
-# superseded / evicted: + history event; storage_path may be empty after eviction
+# collected:  + collection.downloaded_at, collection.storage_path, size, artifacts
+# verified:   + checksums re-read OK
+# rejected:   + reason
+# superseded / evicted: + history event
 
 field_reference:
   id:
@@ -83,10 +79,24 @@ field_reference:
     type: string
   source:
     type: object
-    fields: [kind, url, revision, model_card_url]
+    fields: [kind, url, revision, model_card_url, project_url, paper_url]
   license:
     type: object
     fields: [spdx, url, allows_offline_retention, allows_local_ops, allows_fine_tune, notes]
+  profile:
+    type: object
+    description: Human-facing capability card for operators choosing models
+    fields:
+      summary: One-line positioning
+      original_source: Canonical URL (usually HF model card or org release page)
+      use_cases: List of typical lab / product uses
+      sweet_spots: Where the model punches above weight or is the default pick
+      not_ideal_for: Explicit anti-patterns
+      capability_notes: Short free-text capability narrative
+  swot:
+    type: object
+    description: Compact SWOT for reserve / ops decisions (2–4 bullets each)
+    fields: [strengths, weaknesses, opportunities, threats]
   artifacts:
     type: list
     item_fields: [path, sha256, bytes]