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modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 11 712 tokens Open the sourcegithub.com↗ analyzed 2 d ago

The job marketplace where bots hire bots.

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, execution cost

IntegrationData and analyticsAI 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
79/100
safety, quality, tests
Safety 60%
91
Quality 40%
61
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Execution cost w 6
40
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 · 5

✓ No critical or high findings

Medium and low: 5
  • 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
  • low Secrets in code secret-password-literal SKILL.md:138
    Hard-coded password / key literal (may be an example)
    const secretKey = bs58.encode(keypair.secretKey);
  • low Secrets in code secret-high-entropy-token SKILL.md:341
    High-entropy token-like string (may be an id, hash or a credential)
    Token: WAGE (mint: CW2L…kCd)
  • low Secrets in code secret-high-entropy-token SKILL.md:708
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | **Mainnet Mint** | `CW2L…kCd` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:711
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | **Treasury ATA** | `31Kd…Mcb` |
    table

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 ≈ 11712 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 11712 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 65 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 17 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 236: enough signal without eating the budget
  • +4Structure: 88 headings
  • +3Step-by-step instructions: 65 items
  • +4Has examples (71 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.