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 skills/rsmdt/the-startup/reviewnpx skills add rsmdt/the-startup --skill reviewgit clone --depth 1 https://github.com/rsmdt/the-startupWrote 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/rsmdt/the-startup/review)<a href="https://agentmods.dev/skills/rsmdt/the-startup/review"><img src="https://agentmods.dev/badge/skills/rsmdt/the-startup/review.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 | $0.00021 | $0.01044 |
| Opus 5 | $0.00010 | $0.00522 |
| Sonnet 5 | $0.00004 | $0.00209 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
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 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona
Act as a code review orchestrator that coordinates comprehensive review feedback across multiple specialized perspectives.
Review Target: $ARGUMENTS
Interface
Finding { severity: CRITICAL | HIGH | MEDIUM | LOW confidence: HIGH | MEDIUM | LOW title: string // max 40 chars location: string // shortest unique path + line issue: string // one sentence fix: string // actionable recommendation code_example?: string // required for CRITICAL, optional for HIGH breaking?: { // set when the change alters an external contract consumers: string // what depends on the contract being changed migration: string // what consumers must do to adapt } }
State { target = $ARGUMENTS perspectives = [] // from reference/perspectives.md mode: Standard | Agent Team findings: Finding[] }
Constraints
Always:
- Describe what needs review; the system routes to specialists.
- Launch ALL applicable review activities simultaneously in a single response.
- Provide full file context to reviewers, not just diffs.
- Highlight what's done well in a strengths section.
- Lead the report with breaking changes when any exist. They affect people outside the repo, so they get their own section above every severity table - never one row among a dozen.
- Only surface the lead's synthesized output to the user; do not forward raw reviewer messages.
Never:
- Review code yourself — always delegate to specialist agents.
- Present findings without actionable fix recommendations.
- Launch reviewers without full file context.
Reference Materials
- reference/perspectives.md — perspective definitions, intent, activation rules
- reference/output-format.md — table guidelines, severity rules, verdict-based next steps
- examples/output-example.md — concrete example of expected output format
- reference/checklists.md — security, performance, quality, test coverage checklists
- reference/classification.md — severity/confidence definitions, classification matrix, example findings
What ships with it
5 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.
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 · 134 lines · 21 tokens per session scan A 00def3d51564
review is a skill published in the GitHub repository rsmdt/the-startup (511 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,044 once invoked, about $0.0001 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.
Other skills, from other repositories
job-interview-meeting-preparation
Prepare a user for a high-stakes professional meeting (interview, advisory or consulting meeting, partnership or BD meeting, or sales discovery call) with a specific stakeholder at a specific company. The skill handles input capture, web research on the company and any secondary company, stakeholder analysis from a…
comparative-landscape-brief
Produce a structured comparative briefing document for a third-party audience, across a named set of 3 to 8 entities for a named audience (investors, board, exec team, M&A committee, partners, advisory council, customer advisory board). The skill captures inputs, researches each entity, analyzes the last 90 days of…
pre-call-briefing
Produce an executive-grade pre-call briefing on a target company for a commercial conversation: prospect discovery, partnership exploration, account expansion, renewal or QBR, or analyst call. Runs a short clarifying interview first, then a seven-source research protocol, and delivers a briefing whose every section…
signal-watch
Produce a decision-ready executive briefing on ONE company: what they do, who runs it, what they have been saying in public over a chosen window, and what the gap between the two signals about where they are going. Covers company fundamentals, an executive leadership map, public messaging analysis, and a…
people-integration
Design the first 90 days for acquired product talent so they stay, contribute, and feel ownership. Produces decision map session template, mentor pairing matrix, 30/60/90 ownership plan, retention risk review (purpose and influence), and 1:1 coaching question library.
ai-readiness-survey
Generate an employee survey tuned to a company's size, vertical, and role mix, designed to surface the AI use already happening informally rather than to measure enthusiasm. Produces the questions, the distribution plan, the anonymity commitment, and a rubric for reading the results, including which answers mean act…