AC readme-generator
Auto-generate comprehensive, well-structured README.md files by analyzing your project's codebase, dependencies, configuration, and structure. Scans package.json, pyproject.toml, Cargo.toml, Dockerfile, CI configs, and source code to produce professional documentation. Use when the user wants to: (1) Create a README for a new project, (2) Regenerate an outdated README, (3) Document an open-source project for publishing, (4) Create project documentation from scratch, (5) Standardize README format across repos. Best for open-source maintainers, indie developers, teams standardizing documentation, and anyone who dreads writing README files.
As a process C 56/100 · Has gaps — weak spots: result and completion, failures and branches, consistency
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (readme-generator) differs from the folder (readme-auto-gen)
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 39 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 1014 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 645: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 39 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.