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prepare-base-resume

prepare-base-resume is a skill for Claude Code from dcassil/resume-kit. It costs 56 tokens per session (1,160 once invoked), scanned A, original, Apache-2.0.

A job-independent workflow that turns one source resume into a reusable, ATS-ready base resume. An ATS is software employers use to scan and sort applications, and this workflow also preserves source content as evidence.

In plain words
What is it for?
Use it to parse a PDF, DOCX, Markdown, or text resume, create its canonical versions, and prepare the default resume for later job-specific tailoring.
Why use it?
It creates a consistent starting point before tailoring the resume to individual jobs. It keeps information that is left out of the prepared version available for later proof and review.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the resume-intelligence plugin — 31 skills, 1 command, 1 hook shipped together

not rated 0repo 27d ago A scan 56 tokens original Apache-2.0

Good fit Use it to parse a PDF, DOCX, Markdown, or text resume, create its canonical versions, and prepare the default resume for later job-specific tailoring.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dcassil/resume-kit/prepare-base-resume
View source ↗ dcassil/resume-kit
Install

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.

Any agent
npx skills add dcassil/resume-kit --skill prepare-base-resume
Clone the repo
git clone --depth 1 https://github.com/dcassil/resume-kit

Made for: Claude Code.

Or install resume-intelligence, the plugin that ships this one along with the rest of its 31 skills, 1 command, 1 hook.

Wrote 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.

agentmods badge for prepare-base-resume

README.md
[![agentmods](https://agentmods.dev/badge/skills/dcassil/resume-kit/prepare-base-resume.svg)](https://agentmods.dev/skills/dcassil/resume-kit/prepare-base-resume)
Your own site
<a href="https://agentmods.dev/skills/dcassil/resume-kit/prepare-base-resume"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/prepare-base-resume.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,160 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.01160
Opus 5 $0.00028 $0.00580
Sonnet 5 $0.00011 $0.00232
Haiku 4.5 $0.00006 $0.00116

Measured 7d ago against content hash 6a883ed24bbb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

prepare-base-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 7d 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.

plugins/resume-intelligence/skills/prepare-base-resume/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

prepare-base-resume - reusable Flow 1 baseline

Flow 1 prepares a single resume before any job-specific work. It turns a source resume into the normal original -> base -> structure -> refine lineage, keeps refine as the default downstream tailoring input, and seeds durable evidence from the full source resume before the prepared no-custom projection removes custom or unmapped sections from the canonical artifact.

Prerequisites

Run the shared Prerequisites gate - ../_shared/prerequisites.md.

  • Required input: one source resume file or one already-parsed ResumeDocument JSON.
  • Does NOT need a job. This flow is entirely job-independent.
  • Project state: resume-kit/config.json may already point at other resumes, jobs, aliases, or learning files. This flow must update only the active resume lineage and evidence pointers needed for the resume being prepared.

The walkthrough

  1. Parse the resume. Run parse-resume if the source is a PDF, DOCX, Markdown, or text file. Save the faithful <name>-original.json and set it active with resume-tool set-active --resume resumes/<name>-original.json plus --resume-source when a source file exists. The original must retain all customSections, including user-authored headings and visible Skills sections; no-custom projection is not allowed during parse.
  2. Seed full-resume learning before projection. Call seed-full-resume-evidence while active_resume still points at the full source resume. This captures all source content, including custom and unmapped sections, into durable learning evidence before the no-custom prepared artifact omits those sections.
  3. Build base. Run update-structure (build-base) to apply the resume-only ATS structural fixes behind the claim-preservation gate. This writes resume-kit/resumes/<name>-base.json.
  4. Build canonical no-custom structure. Run update-shape (build-structure) as the no-custom Flow 1 projection. build-structure auto-seeds full-resume evidence idempotently before omitting custom sections, then accounts for every source token through the content ledger, marks omitted custom content as preserved in learning evidence rather than retaining a canonical custom holding section, and writes resume-kit/resumes/<name>-structure.json only when its hard gates pass.
  5. Score best practices. Run check-best-practices on the structure artifact. Split findings into auto_suggestible and needs_user_input.
  6. Build refine. Run update-refine (build-refine) with truthful answers for any needs_user_input findings the user can actually support. Unanswered findings remain deferred; they are never fabricated. This writes resume-kit/resumes/<name>-refine.json.
  7. Optionally inspect ATS view. Run check-ats-view on refine when the user wants the read-only parser view before tailoring starts.

Read the full file on GitHub · 104 lines

Changes

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.

  1. 7d ago First seen · 104 lines · 56 tokens per session scan A 6a883ed24bbb

Subscribe to this mod's changes

prepare-base-resume is a skill published in the GitHub repository dcassil/resume-kit (0 stars, last pushed 27d ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,160 once invoked, about $0.0003 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.

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