SKILLEMALL.ai
RU Sign in

A 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 source ↗ analyzed 13 h ago

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

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
A
95/100
Overall score
Safety 60%
97
Quality 40%
93
Tests bonus
0

How to improve

  1. Add evals/evals.json with 4–6 real requests and expected answers: the full check will then use your cases instead of a model draft.
  2. Add a spec.yaml with triggers and assertions (skilltest init): the behaviour contract for CI.

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.

Lint

✓ Lint: no remarks

Process maturity 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.