SKILLEMALL.ai
GitHub ↗ RU Sign in

BF recruit-email-monitor

招聘邮件监控系统 - 自动检查邮箱、记录到表格、飞书通知、每日简报

ClawHub Agent Skills author: haoxianniu528-bit v1.3.0 MIT-0 15 files body ≈ 1 702 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 35/100 · Will not run — References files that are not bundled: scripts/pending_candidates.json, scripts/pending_judged.json, scripts/email-heartbeat-check.keyword-version.py

ReferencePeople and hiringInfrastructuretype 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
78/100
safety, quality, tests
Safety 60%
99
Quality 40%
47
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/pending_candidates.json, scripts/pending_judged.json, scripts/email-heartbeat-check.keyword-version.py
Tools and files w 18
0
Result and completion w 14
0
When it triggers w 12
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.
  2. The text references files that are not there: add them or drop the references.
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
  • low Dangerous commands cmd-cron-mention INSTALL.md:74
    Mentions editing / listing crontab
    crontab -e

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/pending_candidates.json
  • warning missing-ref reference to a missing file: scripts/pending_judged.json
  • warning missing-ref reference to a missing file: scripts/email-heartbeat-check.keyword-version.py
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/pending_candidates.json, scripts/pending_judged.json, scripts/email-heartbeat-check.keyword-version.py
  • 0Tools and files. 3 referenced file(s) missing: scripts/pending_candidates.json, scripts/pending_judged.json, scripts/email-heartbeat-check.keyword-version.py
  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 39 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1702 tokens
  • low 10 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 33: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (6 code blocks)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 47.

External checks

ClawHub: clean
This skill does what it claims: monitors configured recruitment mailboxes, records results locally, and sends Feishu notifications, with sensitive-data risks that users should understand before enabling it.
LLM: benign (high) · VirusTotal: · 22 Aug 2026