SKILLEMALL.ai
GitHub ↗ RU Sign in

BC post-job

Post free job ads to 20+ job boards such as LinkedIn, Indeed, Ziprecruiter etc. to receive applicant resumes via email.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 4 090 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Post free job ads to 20+ job boards such as LinkedIn, Indeed, Ziprecruiter etc.

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, running it twice

ProcedureGmailWriting and documentsAI and agentsPeople and hiringtype 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
83/100
safety, quality, tests
Safety 60%
93
Quality 40%
67
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
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 · 7

✓ No critical or high findings

Medium and low: 7
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:24
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…9dM/mwVgvbZJaSNaRk+bshk…Kbz+IoId…W0Q==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:34
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…SDq+2kAA…MOe/+5cdoEdg==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:49
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…FrF+LTRo…W3g==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:53
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…A6j+hAmM…GbS+kf5c…csw==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:57
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…5bm+c2gQ…aG5+esrLODihIorn+Pe6F…dXA==}
  • low Secrets in code secret-high-entropy-token scripts/monitor_linkedin.js:70
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const NUMBER = "job-…aF0";
    quoted
  • low Secrets in code secret-high-entropy-token scripts/post_job.js:76
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const NUMBER = "job-…aF0";
    quoted

Files scanned: 6. 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")

Process rating: all ten parameters 58/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 10 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 4090 tokens
  • 85Steps. 92 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 14 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)
  • +3Description length 119: 120–800 characters recommended
  • -231 emoji in the instructions: noise for the model
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 47 headings
  • +3Step-by-step instructions: 92 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.