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BD polymarket-trading

Complete guide to placing orders on Polymarket with clawearn. Fully functional with automatic USDC approval, dynamic gas pricing, market discovery, price data, automated order execution, deposits, withdrawals, and comprehensive troubleshooting.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 6 330 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Complete guide to placing orders on Polymarket with clawearn.

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsWriting and documentsFinancetype 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
80/100
safety, quality, tests
Safety 60%
95
Quality 40%
58
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

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.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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
  • medium Broad scope meta-agent-memory-dump HEARTBEAT.md
    Agent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokens
    HEARTBEAT.md

Files scanned: 2. 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")
  • warning body-long SKILL.md body ≈ 6330 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "documentation"

Process rating: all ten parameters 44/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 15 mutating operations with no state check
  • 40Consistency. Frontmatter name (polymarket-trading) differs from the folder (polymarket)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6330 tokens
  • 100Steps. 113 steps
  • 100Progress reporting. Reports progress
  • low 15 top-level sections: this looks like several domains in one skill

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
  • -239 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 244: enough signal without eating the budget
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 113 items
  • +4Has examples (59 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.