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.
git clone --depth 1 https://github.com/MadeByTokens/claude-code-plugins-madebytokensWrote 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/commands/madebytokens/claude-code-plugins-madebytokens/resume-loop)<a href="https://agentmods.dev/commands/madebytokens/claude-code-plugins-madebytokens/resume-loop"><img src="https://agentmods.dev/badge/commands/madebytokens/claude-code-plugins-madebytokens/resume-loop.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 5.1 | $0.00009 | $0.03719 |
| Opus 5 | $0.00005 | $0.01860 |
| Sonnet 5 | $0.00002 | $0.00744 |
| Haiku 4.5 | $0.00001 | $0.00372 |
Grade A, and why
resume-loop 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 7d 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.
This is a copy
100% identical to resume-loop — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Development Loop (Thin Orchestrator)
You are orchestrating an adversarial four-agent loop to develop a compelling AND honest resume.
CRITICAL: This orchestrator uses FILE-BASED message passing to minimize context usage.
- Agents read inputs from files and write outputs to files
- You only read small verdict/status files, NOT full outputs
- You pass file paths to agents, NOT file contents
Agents
- Resume Writer (Advocate): Creates compelling resume content
- Fact Checker (Gatekeeper): Catches hallucinations before Interviewer
- Interviewer (Skeptic): Reviews from hiring manager perspective
- Coach (Mediator): Synthesizes feedback, ensures honesty
Working Directory Structure
All agents read and write to a working/ directory:
working/
├── inputs/ # Read-only after setup
│ ├── experience.md # Original candidate experience
│ ├── job_description.md # JD (optional)
│ └── candidate_additions.md # User answers (append-only)
├── writer/
│ ├── output.md # Resume draft
│ ├── notes.md # Writer notes
│ └── status.md # "DONE" or "BLOCKED: reason"
├── fact_checker/
│ ├── report.md # Full verification report
│ └── verdict.md # "PASS" or "FAIL"
├── interviewer/
│ ├── review.md # Full review
│ └── verdict.md # "STRONG_CANDIDATE/NEEDS_WORK/RED_FLAGS"
├── analysis/
│ ├── vague_claims.md
│ ├── buzzwords.md
│ ├── ats_compatibility.md
│ └── quantification.md
├── coach/
│ ├── assessment.md # Full assessment
│ ├── feedback.md # Feedback for Writer
│ ├── questions.md # Questions for user
│ └── verdict.md # "READY/NEEDS_STRENGTHENING/etc"
├── output/
│ ├── resume_final.md
│ └── interview_prep.md
└── state.json # Loop state
Step 1: Parse User Input
Extract from user's command:
experience_path: Path to candidate's experience/background file (REQUIRED)--jobor--jd: Path to job description file (OPTIONAL)--max-iterations: Maximum loop iterations (DEFAULT: 5)--max-pages: Maximum page length - 1, 2, or 3 (DEFAULT: 1, requires confirmation)--output: Output path for final resume (DEFAULT: ./resume_final.md)--premium: Use Opus model for Coach agent for higher quality synthesis (DEFAULT: false)
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.
- 7d ago First seen · 394 lines · 9 tokens per session scan A c093fb5e851b
resume-loop is a command published in the GitHub repository MadeByTokens/claude-code-plugins-madebytokens (2 stars, last pushed 7mo ago), licensed MIT. It adds 9 tokens to every session and 3,719 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to resume-loop, differing in 0 lines, and is treated as a copy.
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specify
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converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
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analyze
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