SKILLEMALL.ai
GitHub ↗ RU Sign in

AC changelog-writer

Turn a list of changes, commits, or PRs into clean release notes / a changelog entry. Use when asked to write release notes, a changelog, or a version announcement from raw changes. Produces a Keep-a-Changelog-style entry grouped by type (Added/Changed/Fixed/etc.), written for users — surfacing breaking changes and upgrade notes up top. To go straight from a raw git log use changelog-generator instead.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 627 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Turn a list of changes, commits, or PRs into clean release notes / a changelog entry.

As a process C 64/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

GeneratorWriting and documentstype 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
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
64/100
Has gaps
Failures and branches w 10
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: changelog-writer (mohitagw15856/pm-claude-skills)

How to improve

    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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 27): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 64/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 14 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 627 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 405: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 14 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.