SKILLEMALL.ai
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Autonomous GitHub contribution agent using the Architect-Builder pattern. Buck (main agent) handles all git/network I/O; subagents handle focused cognitive work only. Scouts issues, implements fixes, and submits PRs. Supports three difficulty levels. Triggers on: open source, github, contribution, PR, pull request, issue fix, OSS, contributor.

ClawHub Agent Skills author: Wahaj Ahmed v3.0.0 MIT-0 11 files body ≈ 2 640 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ReferenceGitHubSoftware developmentAI and agentsInfrastructuretype 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
Technical rating
B
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
69
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-cron-mention scripts/setup.py:281
    Mentions editing / listing crontab (string literal in code, not executed)
    print(f"  3. Enable cron: crontab -e")
    code literal

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 58/100

  • 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. 15 mutating operations with no state check
  • 60Tools and files. Uses tools (git) that frontmatter does not declare
  • 65Failures and branches. 3 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2640 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill

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
  • -36 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 345: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 69 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This skill is a disclosed GitHub automation tool, but it needs review because it can autonomously use your GitHub token to fork repos, push branches, and open public PRs while handling credentials unsafely.
LLM: suspicious (high) · VirusTotal: · 29 May 2026