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AC find-paid-onchain-jobs

Find paid tasks and bounties for AI agents on Jobs for AI Agents, evaluate Base USDC payouts and requirements, and prepare a wallet-signed application. Use when asked to earn money through work, find onchain jobs, or connect an agent to a paid-task marketplace.

ClawHub Agent Skills author: btsdeploy v1.0.0 MIT-0 2 files body ≈ 1 036 tokens Open the sourceclawhub.ai↗ analyzed 4 h ago

Find paid tasks and bounties for AI agents on Jobs for AI Agents, evaluate Base USDC payouts and requirements, and prepare a wallet-signed application.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI 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
Technical rating
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    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 Secrets in code secret-high-entropy-token SKILL.md:35
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Fetch the current integration guide at `https://jobsforaiagents.com/skill.md`. Through MCP, call `get_native_config` and `get_action_schema` before constructing a signed request. Verify Base chain ID
      quoted

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1036 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 261: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a disclosed job-discovery and application guide for a paid onchain task marketplace, with no hidden persistence or local credential collection found.
    LLM: benign (high) · VirusTotal: · 18 Sept 2026