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Teknium c49fa88b80 refactor(skills): shipped-set slim — 15 to optional, github 6-way merge, pdf absorbs OCR, channel-gated teams pipeline (index −26%) (#98539 )

* refactor(skills): shipped-set slim — 15 skills to optional, github six-way merge, pdf absorbs OCR+nano-pdf, channel-gated teams pipeline

Maintainer-directed shipped-skills curation (skills index 1,900 -> ~1,400
tok/call on desktop; every session pays the index, so this is a per-call
diet on all installs):

- optional-skills moves (installable via skills hub, history preserved):
  creative comfyui/ascii-art/excalidraw/pretext/sketch/touchdesigner-mcp;
  ALL of mlops (huggingface-hub, llama-cpp, serving-llms-vllm,
  weights-and-biases, evaluating-llms-harness — subcategory structure
  kept); research-paper-writing (55 supporting files, 17.3K-tok load);
  openhue; blogwatcher (first taught the cronjob monitor-field watch
  pattern + web_extract instead of pre-cron manual workflows)
- DELETED session-librarian (Aug-12 'inspired by Perplexity Computer'
  port, never maintainer-intended; session_search covers discovery)
- github: six skills (auth, issues, pr-workflow, issue-to-pr,
  code-review, repo-management) merged into ONE software-development/
  github skill — routing body + complete per-workflow references;
  benbarclay authorship credited; codebase-inspection rides along;
  discipline pins from test_github_issue_to_pr_skill.py preserved
  against the reference body in the new test_github_skill.py
- pdf absorbs ocr-and-documents + nano-pdf as references/ + scripts
  (extract_pymupdf, extract_marker converted to the argparse house
  standard its contract test enforces)
- NEW session_platforms frontmatter gate (metadata.hermes): hides a
  skill from the index on gateway channels it is not for; fail-open on
  unknown platform; teams-meeting-pipeline gated to [teams, cron]
- blocked-page-recovery: research -> new web category; trigger-first
  description ('Use when a fetch fails: 403/429, paywall, WAF, bot
  wall.') so the model actually reaches for it on blocked fetches
- docs regenerated via generate-skill-docs.py (195 pages); related_skills
  swept repo-wide; tests: 1672 passed (2 openclaw failures pre-existing
  on clean main, Windows-local)

* chore: ignore .skills_prompt_snapshot.json (local index cache, accidentally committed)

2026-08-30 04:53:39 -07:00

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Hugging Face URL Workflows for llama.cpp

Use URL-only workflows first. Do not require hf or API clients just to find GGUF files, choose a quant, or build a llama-server command.

Core URLs

Search:
https://huggingface.co/models?apps=llama.cpp&sort=trending

Search with text:
https://huggingface.co/models?search=<term>&apps=llama.cpp&sort=trending

Search with size bounds:
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending

Repo local-app view:
https://huggingface.co/<repo>?local-app=llama.cpp

Repo tree API:
https://huggingface.co/api/models/<repo>/tree/main?recursive=true

Repo file tree:
https://huggingface.co/<repo>/tree/main

1. Search for llama.cpp-compatible models

Start from the models page with apps=llama.cpp.

Use:

search=<term> for model family names such as Qwen, Gemma, Phi, or Mistral
num_parameters=min:0,max:24B or similar if the user has hardware limits
sort=trending when the user wants popular repos right now

Do not start with random GGUF repos if the user has not chosen a model family yet. Search first, shortlist second.

Example: https://huggingface.co/models?search=Qwen&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending

2. Use the local-app page for the recommended quant

Open:

https://huggingface.co/<repo>?local-app=llama.cpp

Extract, in order:

The exact Use this model snippet, if it is visible as text
The Hardware compatibility section from the fetched page text or HTML:
- quant label
- file size
- bit-depth grouping
Any extra launch flags shown in the snippet, such as --jinja

Treat the HF local-app snippet as the source of truth when it is visible.

Do this by reading the URL itself, not by assuming the UI rendered in a browser. If the fetched page source does not expose Hardware compatibility, say that the section was not text-visible and fall back to the tree API plus generic guidance from quantization.md.

3. Confirm exact files from the tree API

Open:

https://huggingface.co/api/models/<repo>/tree/main?recursive=true

Treat the JSON response as the source of truth for repo inventory.

Keep entries where:

type is file
path ends with .gguf

Use these fields:

path for the filename and subdirectory
size for the byte size
optionally lfs.size to confirm the LFS payload size

Separate files into:

quantized single-file checkpoints, for example Qwen3.6-35B-A3B-UD-Q4_K_M.gguf
projector weights, usually mmproj-*.gguf
BF16 shard files, usually under BF16/
everything else

Ignore unless the user asks:

README.md
imatrix or calibration blobs

Use https://huggingface.co/<repo>/tree/main only as a human fallback if the API endpoint fails or the user wants the web view.

4. Build the command

Preferred order:

Copy the exact HF snippet from the local-app page
If the page gives a clean quant label, use shorthand selection:

llama-server -hf <repo>:<QUANT>

If you need an exact file from the tree API, use the file-specific form:

llama-server --hf-repo <repo> --hf-file <filename.gguf>

For CLI usage instead of a server, use:

llama-cli -hf <repo>:<QUANT>

Use the exact-file form when the repo uses custom labels or nonstandard naming that could make :<QUANT> ambiguous.

5. Example: `unsloth/Qwen3.6-35B-A3B-GGUF`

Use these URLs:

https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF?local-app=llama.cpp
https://huggingface.co/api/models/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main?recursive=true
https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/tree/main

On the local-app page, the hardware compatibility section can expose entries such as:

UD-IQ4_XS - 17.7 GB
UD-Q4_K_S - 20.9 GB
UD-Q4_K_M - 22.1 GB
UD-Q5_K_M - 26.5 GB
UD-Q6_K - 29.3 GB
Q8_0 - 36.9 GB

On the tree API, you can confirm exact filenames such as:

Qwen3.6-35B-A3B-UD-Q4_K_M.gguf
Qwen3.6-35B-A3B-UD-Q5_K_M.gguf
Qwen3.6-35B-A3B-UD-Q6_K.gguf
Qwen3.6-35B-A3B-Q8_0.gguf
mmproj-F16.gguf

Good final output for this repo:

Repo: unsloth/Qwen3.6-35B-A3B-GGUF
Recommended quant from HF: UD-Q4_K_M (22.1 GB)
llama-server: llama-server --hf-repo unsloth/Qwen3.6-35B-A3B-GGUF --hf-file Qwen3.6-35B-A3B-UD-Q4_K_M.gguf
Other GGUFs:
- Qwen3.6-35B-A3B-UD-Q5_K_M.gguf - 26.5 GB
- Qwen3.6-35B-A3B-UD-Q6_K.gguf - 29.3 GB
- Qwen3.6-35B-A3B-Q8_0.gguf - 36.9 GB
Projector:
- mmproj-F16.gguf - 899 MB

Notes

Repo-specific quant labels matter. Do not rewrite UD-Q4_K_M to Q4_K_M unless the page itself does.
mmproj files are projector weights for multimodal models, not the main language model checkpoint.
If the HF hardware compatibility panel is missing because the user has no hardware profile configured, or because the fetched page source did not expose it, still use the tree API plus generic quant guidance from quantization.md.
If the repo already has GGUFs, do not jump straight to conversion workflows.

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