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BD jd-truth-detector

Reverse-engineer job descriptions: translate jargon ("5 years" → 3), infer company culture (red flags, vibes), match to your resume, detect negotiation signals. Outputs Markdown + shareable HTML report. Works with any OpenAI-compatible LLM (Ollama, DeepSeek, OpenAI, etc.). Supports text paste (primary), URL scraping (BOSS/拉勾/LinkedIn), and file input (.txt/.md/.docx/.pdf).

ClawHub Agent Skills author: ucsdzehualiu v1.0.0 MIT-0 17 files body ≈ 255 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Reverse-engineer job descriptions: translate jargon ("5 years" → 3), infer company culture (red flags, vibes), match to your resume, detect negotiation signals.

As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerWordPDFAI and agentstype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
D
38/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
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 · 0

✓ No critical or high findings

Files scanned: 12. 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")
  • note frontmatter-key unknown frontmatter key "trigger_keywords"

Process rating: all ten parameters 38/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 255 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -36 of 7 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 5 headings
  • +4Has examples (3 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This is a straightforward job-description analysis skill that uses user-provided inputs and a configured LLM endpoint, with privacy and dependency hygiene caveats.
LLM: benign (high) · VirusTotal: · 24 Jun 2026