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BC bounty-automation

Multi-platform bounty automation system for GitHub, Opire, Algora, and OpenTask / 多平台赏金自动化系统，支持 GitHub、Opire、Algora、OpenTask，具有自动扫描、过滤、认领和 PR 提交功能

ClawHub Agent Skills author: mkcash v1.0.0 MIT-0 4 files · 1 script body ≈ 664 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceGitHubInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
75
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token references/DEPLOY_GUIDE.md:155
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    - GitHub PR payment comment: `USDT TRC-20: TMFj…THn / PayPal: lj…@….com`
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:112
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - **USDC TRC-20**: `TMFj…THn`
    quoted

Files scanned: 4. 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 51/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 664 tokens

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 146: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This skill is a real bounty-automation workflow, but it sets up unattended recurring jobs that can use your accounts to bid, claim work, modify code, push branches, open PRs, post payment details, and send QQ notifications with weak scoping controls.
LLM: suspicious (high) · 28 May 2026