SKILLEMALL.ai
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A github-issue-resolver

Autonomous GitHub Issue Resolver Agent with guardrails. Use when the user wants to discover, analyze, and fix open issues in GitHub repositories. Triggers on requests like "fix GitHub issues", "resolve issues in repo", "work on GitHub bugs", or when the user provides a GitHub repository URL and asks for issue resolution. Supports the full workflow from issue discovery to PR submission with safety guardrails preventing scope creep, unauthorized access, and dangerous operations.

ClawHub Agent Skills author: Ashwinhegde19 v1.0.0 12 files body ≈ 1 450 tokens open source ↗ analyzed 25 h ago
AnalyzerGitHubSoftware developmentInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
A
98/100
Overall score
Safety 60%
100
Quality 40%
96
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 · 0

✓ No critical or high findings

Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

Lint

✓ Lint: no remarks

Process maturity 57/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 11 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 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. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1450 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 3 example trigger phrases
  • +3Description length 481: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

External checks

ClawHub: suspicious
This is a legitimate GitHub issue-fixing skill, but it needs review because an included helper can push code and open PRs through the user's GitHub login without the approval and draft safeguards the instructions promise.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026