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update-refine

update-refine is a skill for Claude Code from dcassil/resume-kit. It costs 93 tokens per session (1,124 once invoked), scanned A, original, Apache-2.0.

A job-independent wording pass that improves a resume using best-practice checks. It applies safe suggestions automatically and asks the user for facts when a rewrite needs real information.

In plain words
What is it for?
Use it after the resume has been put into standard sections and checked for best practices, before tailoring it to a specific job.
Why use it?
It improves clarity and consistency without inventing achievements, skills, or other claims.

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 2mo ago A scan 93 tokens original Apache-2.0

Good fit Use it after the resume has been put into standard sections and checked for best practices, before tailoring it to a specific job.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dcassil/resume-kit/update-refine
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 update-refine
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 update-refine

README.md
[![agentmods](https://agentmods.dev/badge/skills/dcassil/resume-kit/update-refine/github.svg)](https://agentmods.dev/skills/dcassil/resume-kit/update-refine)
Your own site
<a href="https://agentmods.dev/skills/dcassil/resume-kit/update-refine"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/update-refine/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.

agentmods 80×15 button for update-refine

Your own site · 80×15
<a href="https://agentmods.dev/skills/dcassil/resume-kit/update-refine"><img src="https://agentmods.dev/badge/skills/dcassil/resume-kit/update-refine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,124 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 $0.00093 $0.01124
Opus 5.5 $0.00037 $0.00450
Sonnet 5.5 $0.00019 $0.00225
Haiku 4.5 $0.00009 $0.00112

Measured 1mo ago against content hash 09c5b0183572, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

update-refine 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.

plugins/resume-intelligence/skills/update-refine/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

update-refine — structure/base → refine (best-practices wording pass)

Formerly update-best-practices (the standard pass); renamed in RIT-I-0020.

Baselining step 3 of original → base → structure → refine (RIT-I-0016). Turns the best-practices findings from check-best-practices into the refine version: auto_suggestible rewrites are applied deterministically, and needs_user_input items are resolved by asking the user for the real fact — never by inventing one. Drives the build-refine capability, which writes <name>-refine.json behind the claim-preservation gate and sets the refine pointer that all downstream tailoring then prefers.

Prerequisites

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

  • Required inputs: a structure ResumeDocument JSON (run update-shape first; falls back to base only with a recorded shape-pass override), and the best-practices report from check-best-practices.
  • Does NOT need a job. The refine pass is job-independent.
  • If no structure exists: run update-shape first, or record the explicit override before using base.

The walkthrough

  1. Score. Run check-best-practices on structure (or the overridden base) to get the findings, split into auto_suggestible and needs_user_input.
  2. Elicit facts for needs_user_input items. For each such finding, show its elicitation_prompt and ask the user for the real fact (e.g. an actual metric). Collect answers into an answers map keyed by the finding key (resume_kit_scoring.finding_key). If the user cannot supply a fact, leave that finding unanswered — it will be reported as deferred, never fabricated.
  3. Build refine. Call build-refine with the answers map. It applies the auto_suggestible rewrites plus the user-supplied answers, enforces the claim-preservation gate (the wording pass may reword but must not add, drop, or alter an employer/title/degree/skill claim), writes resume-kit/resumes/<name>-refine.json, and records the refine pointer.
  4. Report. Show applied (edits made) and deferred (needs_user_input findings left unanswered). Optionally record a user's declined suggestion via learn-change so future runs weight it.

Read the full file on GitHub · 91 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. 1mo ago First seen · 91 lines · 93 tokens per session scan A 09c5b0183572

Subscribe to this mod's changes

update-refine is a skill published in the GitHub repository dcassil/resume-kit (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,124 once invoked, about $0.0004 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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