resume-job-match-lab
v1.0.0Tailor resumes and project bullets to a target role, quantify gaps, and prepare an interview-ready evidence map.
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byvx：17605205782@52yuanchangxing
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The skill's name/description (tailor resumes, rewrite bullets, gap analysis, interview evidence map) align with the included materials. The bundled script, however, only computes a keyword-based match score, top keywords, and missing keywords — it does not itself rewrite bullets or produce a full evidence map. That gap is acceptable if the LLM is intended to perform the more sophisticated text transformations using the script's structured output, but the script alone does not implement all promised outputs.
Instruction Scope
SKILL.md instructs the agent to use the local script and resource file and to ask for minimal user inputs. Runtime instructions do not request reading unrelated files, environment variables, or network endpoints. The helper script reads only the two user-supplied text files and writes a JSON result.
Install Mechanism
No install spec is provided (instruction-only skill). The only declared runtime dependency is python3. There are no downloads, remote installers, or archive extraction steps in the package.
Credentials
The skill declares no required environment variables, no credentials, and no config paths. The code does not reference environment variables or external secrets. Requested inputs (resume text, job description, etc.) are appropriate for the stated purpose.
Persistence & Privilege
The skill is not force-included (always: false) and does not request persistent privileges or modify other skills or system settings. It uses local, auditable files only.
Assessment
This skill appears safe and coherent: it is local, needs only python3, and asks for the resume and job description you provide. Note the included script is a small keyword matcher that outputs a score and missing keywords — the LLM is expected to generate rewritten bullets, gap analysis, and an evidence map using that output. Before using generated bullets in real applications, review them carefully (do not accept fabricated metrics or claims). If you care about privacy, avoid pasting highly sensitive PII into the tool or run the script locally on your machine (it is auditable). If you need the script to do more (e.g., automated file edits or bulk processing), verify those actions explicitly before granting permission.Like a lobster shell, security has layers — review code before you run it.
latestvk972v58p09wrh7kjqzp4bvsnvd82tfvm
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🧰 Clawdis
Binspython3