Frank
a34322aeb0
The managed-skill library was regenerated: 94 narrow per-incident skills
replaced by 25 broader ones (radiance-*, llamacpp-*, homepage-*, fleet/
endpoint ops). Git renders this as mass delete + mass add because whole
directories changed name; old content stays in history at 12c5e88.
Surface check: the 25 on disk are exactly the skills the harness advertises
this session.
27 lines
2.7 KiB
Markdown
27 lines
2.7 KiB
Markdown
---
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name: hf-checkpoint-profile
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description: "Profile a Hugging Face safetensors checkpoint (sizes, dtypes, tensor-name space, gated/license) with compact HTTP-range reads — safe for subagents on small context slots."
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---
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# Profile an HF safetensors checkpoint cheaply
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Goal: inventory a repo (files/bytes, dtype histogram, tensor-name patterns, per-group GiB, config keys, license/access) without pulling gigabytes and without overflowing a subagent's context slot.
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## Endpoints (all public, no token needed even for gated models)
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- `https://huggingface.co/api/models/<id>?blobs=true` → `siblings` (name+size), `gated`, `private`, `downloads`, `safetensors` metadata, `tags`.
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- `https://huggingface.co/api/models/<id>` for tags/base model; `https://huggingface.co/api/models?author=<x>&limit=100` to find sibling repos (canonical id check: cards often link both `X-1.5-Y` and `X-Y`).
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- `https://huggingface.co/api/models/<id>/resolve/main/<file>` for `config.json`, `model.safetensors.index.json`, `chat_template.jinja`, `LICENSE`, `generation_config.json`; `/raw/main/<file>` for text.
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## dtype + per-group bytes without downloading shards
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Parse the safetensors header over HTTP Range: first 8 bytes = little-endian u64 header length, then that many bytes of JSON (`{name: {dtype, shape, data_offsets}}`). Fetch every shard header with a thread pool (131 shards ≈ seconds), then bytes = prod(shape) × dtype size. Collapse names with `re.sub(r"\.\d+\.", ".N.", k)` (and `\.shard_\d+` → `.shard_N`) to get the pattern space; group-sum by prefix to see where the mass sits (experts / ngram-PLE / MTP / vision / trunk).
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Compare the key set against the base model's index to prove "full-weight finetune, identical names" (a zero-diff key set means per-tensor recipes written for the base apply verbatim, and it reveals fused vs per-expert layout, PLE/ngram shards, MTP head, vision tower, absence of `quantization_config`).
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## Guardrails
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- Run python scripts from a clean directory: a stray `/tmp/inspect.py` shadows stdlib `inspect` and kills `concurrent.futures` imports.
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- Never put an HF token on a command line (`/proc/<pid>/cmdline` is world-readable); use `curl -K <mode-600 file>`.
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- `curl -f` / `--fail-with-body` and verify byte counts — a 401/403 writes the error body as the "artifact" and exits 0.
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## Subagent context budget
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Subagents on a 131k-token slot die from `exceed_context_size_error` when told to read whole doc pages. Give grep-only instructions (`| head -40`, `-o`, `-c`), demand a line cap ("report in ≤250 lines, no verbatim dumps"), and prefer `git clone --depth 5` + local grep to web-page reads. A peer scout that overflows returns exit 1 with the 400 in the harness log and NO structured output — assume nothing was learned and redo the fact inline.
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