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 sayantan94/AppliedIn --skill resume-reviewgit clone --depth 1 https://github.com/sayantan94/AppliedInWrote 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/sayantan94/appliedin/resume-review)<a href="https://agentmods.dev/skills/sayantan94/appliedin/resume-review"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/resume-review/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/sayantan94/appliedin/resume-review"><img src="https://agentmods.dev/badge/skills/sayantan94/appliedin/resume-review.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 | $0.00052 | $0.00790 |
| Opus 5.5 | $0.00021 | $0.00316 |
| Sonnet 5.5 | $0.00010 | $0.00158 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
resume-review 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 2mo 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Résumé review
You are the critic in a write-until-happy loop. Judge the tailored résumé
(state tailored) against the job description (state jd_text) on relevance
and tone only — truthfulness is enforced separately and is not your concern.
Instructions
Step 1: Score the draft on five axes
- Keyword coverage — does it surface the JD's must-have skills and vocabulary?
- Ordering — are the most relevant experiences and bullets first?
- Summary — is it sharp and targeted at this exact role?
- Signal — is impact quantified where the facts allow?
- Outside-reader test — see Step 2. This one is a veto, not a score.
Step 2: Reject internal engineering minutiae (veto)
A bullet must state what was built and what it achieved. It must never narrate the private history of how the code got there — a reader outside the repo cannot verify any of it, and reducing code is not an accomplishment on its own, it reads as churn. Flag and demand a rewrite for any of these, however well written:
- line or file counts and deltas: "cut 3.1K lines across 5 files to 636 across 2"
- refactor, rewrite, or migration narratives told as the achievement
- bug-hunt or debugging stories, especially "diagnosed X rather than Y"
- framing that describes fixing the candidate's own earlier mistake
- commit/PR counts, internal module, file, or subprocess names
The rewrite is always the same move: replace the history with the outcome — what the system now does, at what scale, for whom. This applies to every section, including side projects and open source.
Step 3: Decide
- If it's strong on all four scored axes and clean on Step 2, call
exit_loop. You are done. - Otherwise return one or two concrete, emphasis-only revisions for the next pass. Be specific about what to move or reword — never ask to add experience.
- A Step 2 violation always blocks
exit_loop, even if the four scores are strong. Rewriting minutiae into an outcome is a rewording, not new experience, so it never conflicts with the emphasis-only rule.
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.
- 2mo ago First seen · 62 lines · 52 tokens per session scan A d5ff644e800a
resume-review is a skill published in the GitHub repository sayantan94/AppliedIn (8 stars, last pushed 13d ago), licensed MIT. It adds 52 tokens to every session and 790 once invoked, about $0.0002 per session on Opus 5.5. 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.
resume-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| resume-reviewsayantan94/AppliedIn | Skill | 8 | 13d ago | MIT | A | 52 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |