AC GitHub Workflow
Professional GitHub workflow skill for AI agents. Covers full project lifecycle: repo setup, Git Flow branching, atomic commits, pull requests, code review, CI/CD monitoring, semantic versioning, releases, secrets management, and security rules. Includes mandatory agent behavior directives for skill installation, new projects, and returning to existing work. Features built-in work log system for task state and session continuity. Trigger on: GitHub, git, gh CLI, repo, PR, branch, merge, commit, issue, release, CI, GitHub Actions, tag, secret.
As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens)
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 40Consistency. Frontmatter name (GitHub Workflow) differs from the folder (github-project-workflow)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 5 branches
- 100Steps. 52 steps
- 100Execution cost. Instruction body is 3094 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (4 tags): a typed call is more reliable
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 548: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 52 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.