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BD BountySwarm Skill

Create a new bounty with USDC reward locked in escrow.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 453 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Create a new bounty with USDC reward locked in escrow.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentsSoftware developmenttype 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
82/100
safety, quality, tests
Safety 60%
97
Quality 40%
60
Run on models
none yet
Process rating
D
46/100
Unfinished process
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token references/architecture.md:36
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | BountyEscrow | `0x45…Ca2` | USDC-native bounty lifecycle |
    table
  • low Secrets in code secret-high-entropy-token references/architecture.md:37
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | SubContract | `0xAb…0DF` | On-chain delegation + fee splitting |
    table
  • low Secrets in code secret-high-entropy-token references/architecture.md:38
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | QualityOracle | `0x2c…F9A` | Multi-agent consensus + slashing |
    table

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (BountySwarm Skill) differs from the folder (bountyswarm)
  • 100Tools and files. No external tools needed
  • 100Steps. 9 steps
  • 100Execution cost. Instruction body is 453 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)
  • +3Description length 54: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.