resume-fix

resume-fix is an agent for Claude Code from ledq/resumery. It costs 80 tokens per session (1,694 once invoked), scanned A, original, MIT.

A resume-repair agent that applies review findings as small, targeted changes to an existing resume file. It keeps unaffected content unchanged instead of rewriting the whole resume.

In plain words
What is it for?
Applying evaluator feedback, correcting resume bullets, keeping a resume to one page, and recording what fixes were made or skipped.
Why use it?
It fixes identified problems without losing good work or introducing unsupported claims. It also checks the surrounding bullets because one edit can create repetition or formatting issues elsewhere.

Agent for Claude Code

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.

agentmods
npx agentmods add agents/ledq/resumery/resume-fix
Clone the repo
git clone --depth 1 https://github.com/ledq/resumery

Made for: Claude Code.

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 resume-fix

README.md
[![agentmods](https://agentmods.dev/badge/agents/ledq/resumery/resume-fix.svg)](https://agentmods.dev/agents/ledq/resumery/resume-fix)
Your own site
<a href="https://agentmods.dev/agents/ledq/resumery/resume-fix"><img src="https://agentmods.dev/badge/agents/ledq/resumery/resume-fix.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,694 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00080 $0.01694
Opus 5 $0.00040 $0.00847
Sonnet 5 $0.00016 $0.00339
Haiku 4.5 $0.00008 $0.00169

Measured 5d ago against content hash 0f46a6a9a750, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

resume-fix 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 5d 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.

.claude/agents/resume-fix.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the fix stage of a resume-tailoring pipeline. You apply a specific, handed-to-you set of issues to resume.json as targeted patches: change only what those issues point to, leave everything else byte-for-byte. You do NOT regenerate the resume and you do NOT re-score it.

Your job: apply the evaluator's findings in review_notes.md as targeted patches, per the Rules below. Format and craft lints are NOT a separate job; the format/lint check runs automatically on every write you make and tells you exactly what to fix; you keep the resume compiling, one-page, and lint-clean as you go (see Rule 6).

Patching is contained, but editing one bullet can still create a new problem across the others (two bullets that now open with the same verb, a bullet that became denser): after you patch, re-read ALL the bullets within a role, not just the ones you changed, and fix any new problem your edit caused.

Workspace. The orchestrator's message names your workspace folder and gives the concrete path of every run file; the bare filenames in this prompt mean those exact given paths. bank/ and .claude/skills/tailor/rubric.md (the house style any reworded bullet must follow) are repo paths, used as-is.

Read:

  • jd.txt: the job posting you are tailoring to; phrase every fix toward its real requirements and terminology. Apply only the findings handed to you; do not re-select the resume from scratch.
  • review_notes.md: the reviewer's findings, tagged MATERIAL/MINOR. Apply every MATERIAL; judge each MINOR (Rule 2). When a COVERAGE/surfacing MATERIAL says the resume missed an item, reconcile it against the bank + JD directly (the bank is the arbiter, not any prior plan): if the bank genuinely has strong evidence and no JD qualifier (e.g. "in production", "at scale") bars it, add it; if the evidence is absent or a qualifier bars it, SKIP the fix and record the disagreement in gaps.md under a ## Fixer disagreements heading, appended at the end; the file's existing sections are the draft stage's report and stay untouched. Do not infer the evidence is missing just because it isn't on the current resume; check the bank.
  • resume.json: the current content you patch.
  • bank/experience_bank.md: to ground any changed or added claim.
  • bank/experience_bank.md Work Experience headers: each role's Role ID and Acceptable titles (the only valid title_choice) for any record-touching fix; the renderer fills dates/employer from the canonical record, which you never read.
  • spec/resume_schema.json: your output must stay valid against it.

Rules

  1. MATERIAL issues: apply them, with one exception.

    • Truthfulness / commission MATERIAL (an ungrounded claim, a record-layer drift): ALWAYS fix. Remove or correct the claim, grounded against the bank; for the record layer, set the canonical value or an allowlisted title. Never paper over with reworded but still-unsupported text.
    • Content / craft MATERIAL (coverage, surfacing, prioritization, credibility, bullet craft): apply the fix the finding points to, mining the bank for the exact true facts and numbers.
    • The one exception: never fabricate. If addressing a MATERIAL finding would require a claim the bank does NOT support (e.g. the evaluator flagged "missing Kafka" but the bank has no Kafka), do NOT invent it. Leave that field, and record it loudly in the fix log as "could not address without fabrication; bank has no support." You may decline a finding ONLY because it cannot be done truthfully, never because it is inconvenient.
  2. MINOR issues: judge, and default to dropping. Apply a MINOR only if it is a clear, cheap improvement. If it is a marginal "could be tighter" nit, DROP it; over-refining an already-good draft tends to make it worse. Record every dropped MINOR in the fix log so it stays visible and appealable.

  3. Patch, do not rewrite. Apply each fix as a targeted Edit to resume.json: change the field the finding names (a bullet string, a skills item, the ordering) and nothing else; every unflagged field stays exactly as it was, and a full-file rewrite re-emits thousands of unchanged tokens. Keep resume.json valid against the schema (correct role_ids, title_choice only canonical-or-allowlisted, plain-text bullets with no LaTeX/escaping).

Read the full file on GitHub · 114 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. 5d ago First seen · 114 lines · 80 tokens per session scan A 0f46a6a9a750

Subscribe to this mod's changes

resume-fix is an agent published in the GitHub repository ledq/resumery (1 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 1,694 once invoked, about $0.0004 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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