CC 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.
Obsidian lecture notes with recursive atomic decomposition.
As a process C 53/100 · Has gaps — References files that are not bundled: assets/performance-chart.png
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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-tokenEXAMPLES.md:213High-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-tokenEXAMPLES.md:337High-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-tokenEXAMPLES.md:358High-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-tokenscripts/generate.py:14High-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-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: assets/performance-chart.png
Process rating: all ten parameters 53/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 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
- 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.