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 agentmods add skills/dcassil/resume-kit/finalize-resumenpx skills add dcassil/resume-kit --skill finalize-resumegit 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/finalize-resume)<a href="https://agentmods.dev/skills/dcassil/resume-kit/finalize-resume"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/finalize-resume.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.00695 |
| Opus 5 | $0.00022 | $0.00347 |
| Sonnet 5 | $0.00009 | $0.00139 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
finalize-resume 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 4d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
finalize-resume — tailored resume → fitted export
Flow 4 of the composable resume workflow. Run this after tailor-resume has already produced a tailored resume for the active job. This flow owns the final fit pass through perfect, then the rendered artifact pass through export-resume.
It does not prepare the master resume, ingest a job, learn terminology, or apply tailoring updates. It only fits and exports the already-tailored resume.
Prerequisites
Run the shared Prerequisites gate —
../_shared/prerequisites.md.
- Required inputs: a tailored
ResumeDocumentJSON and anactive_jobJobDescriptionJSON. - If no tailored resume exists: STOP and run tailor-resume first.
- If no active job exists: STOP and run ingest-job first.
- If the resume JSON is missing: STOP and run parse-resume, then the preparation and tailoring flows before finalizing.
The walkthrough
perfect. Run perfect withresume-tool fitagainst the tailored resume and active job. Use either the decision-driven path or explicit--auto-fit. For decision-driven trims, preserve the explicit decision/accounting behavior: removals must be accepted by the user or ranked-budget-accounted automated drops, compressions must pass the claim gate, and deferred items remain reported rather than invented or forced.export-resume. Run export-resume after perfect writes the fitted resume. Export enforces the renderedmax_pagesHARD gate. Fit is not submission-ready until export passes: rendered output is authoritative; perfect may warn or fit to budgets, but it does not replace the export page gate.
How to invoke
CLI
resume-tool fit --root . [--job <jobs/job.json>] [--output {json,text,md}]
resume-tool fit --root . [--job <jobs/job.json>] --auto-fit [--output {json,text,md}]
resume-tool export --format {pdf,docx} [--out PATH] [--resume <resume.json>]
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.
- 4d ago First seen · 69 lines · 43 tokens per session scan A 9056034a0ff6
finalize-resume is a skill published in the GitHub repository dcassil/resume-kit (0 stars, last pushed 24d ago), licensed Apache-2.0. It adds 43 tokens to every session and 695 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
baoyu-youtube-transcript
Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面"…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
gog-slides
Google Slides operations through gog.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…