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 skills add muggl3mind/career-manager --skill cv-tailorgit clone --depth 1 https://github.com/muggl3mind/career-managerWrote 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/muggl3mind/career-manager/cv-tailor)<a href="https://agentmods.dev/skills/muggl3mind/career-manager/cv-tailor"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/cv-tailor/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.
<a href="https://agentmods.dev/skills/muggl3mind/career-manager/cv-tailor"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/cv-tailor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00062 | $0.01128 |
| Opus 5 | $0.00031 | $0.00564 |
| Sonnet 5 | $0.00012 | $0.00226 |
| Haiku 4.5 | $0.00006 | $0.00113 |
Grade A, and why
cv-tailor 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 10d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CV Tailor
Generate tailored application materials in a repeatable, review-first workflow.
Required Inputs
- Company
- Role title
- Job description URL or pasted JD text
If JD text is thin, fetch it via URL or request pasted text before continuing.
Execution Standard
Follow references/repeatable-sop.md.
Key defaults:
- Two-phase pipeline: Python prep → Claude analysis → Python apply.
- Select base CV via
scripts/select_base_cv.py(closest prior or Master CV fallback). - Validate analysis schema before generation (hard fail on invalid input).
- Prefer formatting-safe in-place edits; do not full-rebuild resume by default.
- Preserve dates/company names and core structure unless user asks otherwise.
- Output artifacts + explicit change summary every run.
Workflow
Phase 1 -- Prep (Python)
Get the job description from the user. If they provide a URL, fetch it via WebFetch. If they paste text, use it directly. Write the JD text to a temp file yourself. The user should never create temp files.
# Write JD to temp file (you do this, not the user)
# Then run prep:
uv run cv-tailor/scripts/run_pipeline.py --phase prep --company "Acme" --role "AI PM" --jd-path /path/you/created.txt
Selects base CV, reads it, writes data/pending-analysis.json.
Phase 2 -- Analysis (Claude)
Report progress: "Reading JD... Analyzing against your resume..."
Read data/pending-analysis.json. It contains:
base_cv_paragraphs-- exact strings from the base resumejd_text-- job descriptioninstructions-- what to dooutput_schema-- required output format
Produce targeted edits where old is an exact match to a paragraph in base_cv_paragraphs.
Write results to data/analysis.json.
Report: "Planning X bullet edits, summary rewrite, Y-paragraph cover letter."
Preview Before Apply
Before running the apply phase, present a summary to the user:
"I'll edit X bullets, rewrite your summary, and write a Y-paragraph cover letter. Here's what changes:
- [Brief summary of each bullet edit]
- [Summary change]
- [Cover letter approach]
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- data/CV/Master CV/.gitkeep 0 B
- references/example-output.md 172 B
- references/repeatable-sop.md 2.3 KB
- references/tailoring-lessons.md 4.5 KB
- references/terminology-map.md 2.9 KB
- schemas/analysis.schema.json 1.4 KB
- scripts/build_analysis.py 4.7 KB runs code
- scripts/docx_safe_patch.py 2.1 KB runs code
- scripts/generate_redline.py 1.2 KB runs code
- scripts/index_store.py 3.5 KB runs code
- scripts/quality_gate.py 1.4 KB runs code
- scripts/reindex_cv_assets.py 285 B runs code
- scripts/run_pipeline.py 9.2 KB runs code
- scripts/select_base_cv.py 2.6 KB runs code
- scripts/validate_analysis.py 1.8 KB runs code
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.
- 10d ago First seen · 121 lines · 62 tokens per session scan A bea2f4389644
cv-tailor is a skill published in the GitHub repository muggl3mind/career-manager (24 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 1,128 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 2026-08-30.
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