Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dcassil/resume-kit --skill parse-jobgit clone --depth 1 https://github.com/dcassil/resume-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dcassil/resume-kit/parse-job)<a href="https://agentmods.dev/skills/dcassil/resume-kit/parse-job"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/parse-job/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dcassil/resume-kit/parse-job"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/parse-job.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00140 | $0.02563 |
| Opus 5.5 | $0.00056 | $0.01025 |
| Sonnet 5.5 | $0.00028 | $0.00513 |
| Haiku 4.5 | $0.00014 | $0.00256 |
Grade A, and why
parse-job scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Renamed:
parse-jobwasjob-to-jsonbefore v1.0.0 (see RIT-A-0005).
parse-job — build a JobDescription from a posting
Prerequisites
Run the shared Prerequisites gate first — see
_shared/prerequisites.md.
- Required input: the job posting as pasted text, a URL's content, or a PDF/DOCX/MD/text file — provided by the caller.
- If it is missing: STOP and ask the caller for the posting text, URL, or
file. There is no upstream skill — this skill is itself the first step that
produces the
JobDescriptionJSON others depend on.
Purpose
check-structure, check-keywords, and check-gaps score a
resume against a JobDescription. Deterministic text extraction only captures
the raw text — it does NOT populate the structured requirements and keywords,
so skills_coverage comes back 0. This skill fixes that: a confined
interpretation subagent extracts the structured skills/requirements from the
posting so scoring is meaningful — no LLM provider required.
Same shape as parse-resume: extraction is deterministic, interpretation is
confined to a subagent, and the pointer is recorded by code (set-active), not by
hand-editing config.json.
Which gate applies
resume-tool validate-faithfulness targets a ResumeDocument — it is the
authoritative machine gate for parse-resume, not for job postings. Do NOT
force the resume faithfulness gate on a JobDescription. For jobs, the
faithfulness discipline is enforced by the prose Extraction gates below, which
the interpretation subagent must follow (chiefly: raw_text is verbatim and no
skills/requirements are invented). If a JSON-schema round-trip is available
(JobDescription.model_validate(...)), run it — that is a schema check, still not
the resume faithfulness gate.
Before you start: read prior learnings
Read resume-kit/learning/parse-job.md (if present) first, and append any new
gotcha you hit so future runs benefit.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 227 lines · 140 tokens per session scan A a291bccdb776
parse-job is a skill published in the GitHub repository dcassil/resume-kit (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 140 tokens to every session and 2,563 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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