AB sprint-release-notes
Automatically generate sprint release notes from a GitHub Project Board and publish to their respective repositories. Groups completed items by repository, generates per-repo markdown, and publishes it as each repo's GitHub Release description (create or update by tag), not as committed files under docs/. Optionally posts a summary comment to a designated issue. Use this skill whenever the user mentions sprint release notes, sprint summary, sprint review, project board summary, GitHub project board, release notes from sprints, "what shipped this sprint", "generate release notes", "sprint report", or wants to compile documentation from completed sprint items. Also trigger when the user wants to publish release notes to their respective repos. This skill requires a GitHub PAT token and a GitHub Project Board URL.
As a process B 70/100 · Nearly there — weak spots: result and completion
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 · 6
✓ No critical or high findings
Medium and low: 6
-
low Secrets in code
secret-high-entropy-tokenreferences/github-queries.md:240High-entropy token-like string (may be an id, hash or a credential)... on Proj…lue { -
low Secrets in code
secret-high-entropy-tokenreferences/github-queries.md:251High-entropy token-like string (may be an id, hash or a credential)... on Proj…lue { -
low Secrets in code
secret-high-entropy-tokenreferences/github-queries.md:259High-entropy token-like string (may be an id, hash or a credential)... on Proj…lue { -
low Secrets in code
secret-high-entropy-tokenscripts/generate_release_notes.py:267High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)... on Proj…lue {quoted -
low Secrets in code
secret-high-entropy-tokenscripts/generate_release_notes.py:271High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)... on Proj…lue {quoted -
low Secrets in code
secret-high-entropy-tokenscripts/generate_release_notes.py:275High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)... on Proj…lue {quoted
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 70/100
- 0Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (web, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 74 steps, 1 vague phrases
- 100Failures and branches. 17 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2854 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 11 top-level sections: this looks like several domains in one skill
- high The skill tells the model to perform an irreversible action with no human approval
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 822: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -32 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 74 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.