BC ai-agent-bounty-factory
Autonomous bounty discovery and submission system for earning passive income through freelance AI agent task markets. Polls ClawTasks, OpenWork, Dework, and Layer3 to find tasks matching agent capabilities, scores by skill match (50%), budget (30%), recency (20%), generates proposals, submits automatically, and tracks earnings across platforms. Commands: - bounty_factory.py discover Find matching bounties - bounty_factory.py submit <id> Submit proposal for specific bounty - bounty_factory.py proposal <id> Preview generated proposal - bounty_factory.py submit-all Auto-submit all qualifying bounties - bounty_factory.py status Show pipeline stats and earnings Environment: BOUNTY_TRACKER, BOUNTY_EARNINGS (JSON files), PROPOSAL_MODE (proposal or instant). Python 3.9+, zero deps, optional SQLite for persistence. Proposal mode: no stake required, lower acceptance rate but zero risk. Instant mode: requires staking, higher visibility, risk of stake loss. Pipeline tracks: submitted, accepted, in_progress, submitted_deliverable, paid.
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Shorten the description to 1024 characters.
- 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
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash Read
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1051 chars, limit 1024 - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 59/100
- 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
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 392 tokens
- 100Running it twice. No mutating operations
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)
- +3Description length 1050: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 6 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 47.