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 agents/ledq/resumery/resume-fixgit clone --depth 1 https://github.com/ledq/resumeryWrote 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/agents/ledq/resumery/resume-fix)<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>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 | $0.00080 | $0.01694 |
| Opus 5 | $0.00040 | $0.00847 |
| Sonnet 5 | $0.00016 | $0.00339 |
| Haiku 4.5 | $0.00008 | $0.00169 |
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.
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 ingaps.mdunder a## Fixer disagreementsheading, 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.mdWork Experience headers: each role's Role ID and Acceptable titles (the only validtitle_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
-
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.
-
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.
-
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. Keepresume.jsonvalid against the schema (correctrole_ids,title_choiceonly canonical-or-allowlisted, plain-text bullets with no LaTeX/escaping).
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.
- 5d ago First seen · 114 lines · 80 tokens per session scan A 0f46a6a9a750
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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