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BF hugging-face-tool-builder

Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 1 391 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate…

As a process F 49/100 · Will not run — References files that are not bundled: references/hf_model_papers_auth.sh, references/find_models_by_paper.sh, references/hf_model_card_frontmatter.sh

GeneratorSoftware developmentInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
69
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: references/hf_model_papers_auth.sh, references/find_models_by_paper.sh, references/hf_model_card_frontmatter.sh
Tools and files w 18
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: hugging-face-tool-builder (sickn33/agentic-awesome-skills)

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration net-credential-use SKILL.md:24
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service; security demo / example; quoted — discussed, not commanded)
    - IMPORTANT: Use the `HF_TOKEN` environment variable as an Authorization header. For example: `curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/`. This provides higher rate limit
    known servicedemoquoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/hf_model_papers_auth.sh
  • warning missing-ref reference to a missing file: references/find_models_by_paper.sh
  • warning missing-ref reference to a missing file: references/hf_model_card_frontmatter.sh
  • warning missing-ref reference to a missing file: references/baseline_hf_api.sh
  • warning missing-ref reference to a missing file: references/baseline_hf_api.py
  • warning missing-ref reference to a missing file: references/baseline_hf_api.tsx
  • warning missing-ref reference to a missing file: references/hf_enrich_models.sh
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note edit-residue the text marks something as outdated (lines 111): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: references/hf_model_papers_auth.sh, references/find_models_by_paper.sh, references/hf_model_card_frontmatter.sh
  • 0Tools and files. 7 referenced file(s) missing: references/hf_model_papers_auth.sh, references/find_models_by_paper.sh, references/hf_model_card_frontmatter.sh
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 22 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1391 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 251: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.