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AC oss-contribution-system

End-to-end open-source GitHub contribution automation system. Discover high-value issues, implement fixes, open quality PRs, monitor CI/review status, and learn from each contribution. Uses gh CLI for GitHub API access, git for version control, and stores workspace-local data in ./oss-pilot-data/ for profiles and PR context. Triggers on "oss discover", "oss auto", "oss pr", "oss check", "find issues", "what should I work on", "next PR", "auto PR", "review PR", "check PRs", "morning check".

ClawHub Agent Skills author: Cypher v2.0.2 MIT-0 9 files body ≈ 1 187 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, failures and branches, consistency

IntegrationGitHubSoftware developmentInfrastructuretype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 0

✓ No critical or high findings

Files scanned: 9. 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")
  • note frontmatter-key unknown frontmatter key "user_invocable"

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (oss-contribution-system) differs from the folder (oss-pilot)
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 37 steps
  • 100Execution cost. Instruction body is 1187 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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 11 example trigger phrases
  • +3Description length 494: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This is a coherent GitHub contribution automation skill, but it can modify local repos and make public GitHub changes from broad status-style prompts.
LLM: suspicious (high) · VirusTotal: · 29 May 2026