AD git-manager
This skill should be used when the user needs to perform Git repository operations, including cloning, pulling, merging, rebasing, committing, staging, Git LFS management, reflog recovery, worktree management, grep search, cherry-pick, revert, bisect, and batch operations from GitHub, GitLab, Gitea, Bitbucket, or Azure DevOps platforms. It supports bulk cloning by group ID, project ID, user ID, workspace, or organization, and full-featured Git workflow including commit operations, LFS, bisect debugging, and worktree multi-branch support. Use this skill for any multi-repo management, synchronization, or Git workflow automation tasks.
As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "url"
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (git-manager) differs from the folder (git-manager-skill)
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 45 steps
- 100Execution cost. Instruction body is 3489 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (13 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 640: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (31 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.