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BF lecture-notes-master

Obsidian lecture notes with recursive atomic decomposition. Generates main note (hub), atomic notes (3+ layers deep, rich structure each), and unlimited glossary entries. Inputs: lectures, articles, videos, URLs, transcripts, PDFs. Outputs: Obsidian markdown with Mermaid diagrams, comparison tables, bilingual terms, wikilinks.

ClawHub Agent Skills author: SchaeferAnjon v1.0.0 MIT-0 15 files body ≈ 4 848 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 58/100 · Will not run — References files that are not bundled: assets/performance-chart.png

GeneratorObsidianInfrastructureMedia and videoWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
96
Quality 40%
67
Run on models
none yet
Process rating
F
58/100
Will not run
References files that are not bundled: assets/performance-chart.png
Tools and files w 18
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Secrets in code secret-high-entropy-token EXAMPLES.md:213
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    - [[2028…tes]]
    fixture
  • low Secrets in code secret-high-entropy-token EXAMPLES.md:337
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    - [[2028…tes]]
    fixture
  • low Secrets in code secret-high-entropy-token EXAMPLES.md:358
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    Topic: GPU-…ion
    fixture
  • low Secrets in code secret-high-entropy-token scripts/generate.py:14
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    --parent "GPU-…tes" \
    quoted

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: assets/performance-chart.png

Process rating: all ten parameters 58/100

Will not run. References files that are not bundled: assets/performance-chart.png
  • 0Tools and files. 1 referenced file(s) missing: assets/performance-chart.png
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 7 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4848 tokens
  • 85Steps. 116 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill

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)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 328: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 116 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This is a disclosed Obsidian lecture-note generator that writes note files and optional charts as part of its stated purpose, with manageable risks around broad triggers and local file creation.
LLM: benign (high) · VirusTotal: · 29 May 2026