SKILLEMALL.ai
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A issue-finder

Discover valuable GitHub issues with smart positive-label detection and analyze bug fix feasibility. Use when: (1) Finding good issues to contribute, (2) Auto-detecting positive contribution signals via labels, (3) Analyzing issue quality and fix difficulty, (4) Prioritizing high-value issues by maintainer signals.

ClawHub Agent Skills author: Andy Tien v1.6.0 MIT-0 5 files body ≈ 2 175 tokens open source ↗ analyzed 26 h ago
AnalyzerGitHubInfrastructuretype 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
96/100
Overall score
Safety 60%
100
Quality 40%
89
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Lint

✓ Lint: no remarks

Process maturity 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 85Steps. 76 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2175 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

  • +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
  • -229 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 316: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 76 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

External checks

ClawHub: clean
This skill is a straightforward GitHub issue discovery and analysis helper with disclosed GitHub CLI usage and no hidden persistence or destructive behavior.
LLM: benign (high) · VirusTotal: · 21 Jun 2026