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AC github-issues

Create, update, and manage GitHub issues using MCP tools. Use this skill when users want to create bug reports, feature requests, or task issues, update existing issues, add labels/assignees/milestones, manage repository labels, set issue fields (dates, priority, custom fields), set issue types, manage issue workflows, link issues, add dependencies, or track blocked-by/blocking relationships. Triggers on requests like "create an issue", "file a bug", "request a feature", "update issue X", "set the priority", "set the start date", "create a label", "rename a label", "list repo labels", "link issues", "add dependency", "blocked by", "blocking", or any GitHub issue management task.

github/awesome-copilot Agent Skills author: github MIT 11 files body ≈ 2 116 tokens Open the sourcegithub.com↗ analyzed 22 h ago

Create, update, and manage GitHub issues using MCP tools.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorGitHubAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
97
Quality 40%
93
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token references/projects.md:139
      High-entropy token-like string (may be an id, hash or a credential)
      ... on Proj…lue {
    • low Secrets in code secret-high-entropy-token references/projects.md:199
      High-entropy token-like string (may be an id, hash or a credential)
      upda…lue(input: {
    • low Secrets in code secret-high-entropy-token references/projects.md:265
      High-entropy token-like string (may be an id, hash or a credential)
      upda…lue(input: {

    Files scanned: 11. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 16 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2116 tokens
    • 100Running it twice. Mutating operations check current state

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 13 example trigger phrases
    • +3Description length 687: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (10 of 10)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.