edit-resume
Edit existing resume/CV or cover letter from critique feedback and user suggestions
Re-critique existing resume/CV output files against a JD
Claude Code
$ npx -y skills add ARPeeketi/claude-resume-kit --skill critique --agent claude-code
Codex
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a codex -y
Cursor
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a cursor -y
Gemini CLI
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a gemini-cli -y
GitHub Copilot
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a github-copilot -y
OpenCode
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a opencode -y
Windsurf
$ npx skills add ARPeeketi/claude-resume-kit --skill critique -a windsurf -y
description: Re-critique existing resume/CV output files against a JD user-invocable: true
**User input:** `$ARGUMENTS`
Parse `$ARGUMENTS`:
If no CL .tex provided or found in session file, critique resume/CV alone (Part 7 adjustments noted below).
---
**Accuracy > Relevance > Impact > ATS > Brevity**
Read `config.md` Provenance Flags. Verify every claim against that table. Check `config.md` KB Corrections Log — do not flag corrected items as errors. Use the email from `config.md` Personal Info — flag if a different email appears in output. FIXED sections (from `config.md` FIXED Sections) are template-locked — do not flag for editing. Flag only VARIABLE sections.
---
If the user provides feedback, corrections, or suggestions at any point: 1. Acknowledge the input immediately 2. If it changes scoring criteria or focus: adjust the critique accordingly 3. Never restart — resume from current position
---
Read `resume_builder/reference/shared_ops.md` — Fresh Session Startup + Session File Derivation. Read `CLAUDE.md` — check Active Sessions and KB Corrections. Read `config.md` — load Provenance Flags, FIXED Sections, email. Find and read the session file for the .tex being critiqued (use derivation protocol from shared_ops.md).
**Recovery check:**
---
1. **Read session file** — specifically note:
2. Read `resume_builder/reference/critique_framework.md` 3. Read `resume_builder/support/ai_fingerprint_rules.md` — use Section 6 checklist in Part 7 verification 3. Read the .tex file(s) — derive paths from session file Output Files, or from `$ARGUMENTS` 4. Read the JD (path from `$ARGUMENTS` or session file) 5. Read the relevant bundle (`resume_builder/bundles/bundle_[role_type].md` — from session file) 6. Run char count:
python3 resume_builder/helpers/char_count.py -f [resume|cv] [file.tex]
7. Compile and visually verify:
pdflatex -interaction=nonstopmode -output-directory=output/<FolderName> [file.tex]
Use the Read tool to view the compiled PDF — check orphans, page fill, header wrapping. If compile fails: note "COMPILE FAILED — visual checks could not be verified" in Part 8. 8. If a prior critique exists (`output/<FolderName>/critique_<name>.md`): read it and note previous score. 8b. **Paper Hook Verification:** If the CL cites named papers, PIs, programs, or publications, web-search to verify title, journal, year, and PI affiliation. Flag factual errors as Tier 1 fixes.
9. **Run the full critique per critique_framework.md. The output MUST contain ALL 8 sections** (even if the framework file has partially compacted, produce every section):
1. **Domain-Specialist Lens** — 7 elements: (a) Reviewer persona (b) Company context (c) JD vocabulary extraction (d) Domain vocabulary map (e) Gap ranking (fatal/serious/cosmetic) (f) Methodology transfer test (g) Competitive landscape 2. **Five-Perspective Read-Through** — ATS, Recruiter (10s), HR (30s), HM (2min), Technical (10min) — each with verdict 3. **Eight-Dimension Scoring** — weighted table summing to 100 (ATS 15%, Summary 10%, Skills 10%, Bullets 25%, Publications 10%, Narrative 15%, Visual 5%, Credibility 10%) 4. **Interview Likelihood** — per-reader probability + ceiling analysis 5. **Tiered Improvements** — Tier 1 (>=1pt each), Tier 2 (0.3-0.9), Tier 3 (<0.3) 6. **Interview Bridge Points** — 5-7 resume-to-interview talking points 7. **Cover Letter Critique** — 6 sub-checks (6A anti-patterns, 6B tailoring, 6C context-specific, 6D ATS, 6E structural, 6F package cohesion)
8. **Post-Generation Verification** — mechanical + content + structural checklists
10. Save to `output/<FolderName>/critique_<name>.md` 11. **Update session file** — Critique Summary (score, findings, tier 1 fixes), Status → Critique: CURRENT 12. **Update memory pointer** with new score
Progress: "Reading session file for framing context..." / "Running ATS keyword scan — 16/20 match..." / "Scoring 8 dimensions..." / "Score: 87.0/100"
Present: score table + tier 1 actionable fixes + interview likelihood. **You MUST wait for the user's explicit text response before continuing.** If edits needed, tell user to run `/edit-resume`.
Verify all expected files exist in `output/<FolderName>/`:
Confirm to user: "Package complete in output/<FolderName>/ — [list files]"
Most AI resume tools work the same way: paste resume + paste JD, get a rewrite. They don't know which of your papers is published vs. under review. They don't know you only ran the simulations, not the experiments.
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