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 rsmdt/the-startup --skill analyzegit 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/analyze)<a href="https://agentmods.dev/skills/rsmdt/the-startup/analyze"><img src="https://agentmods.dev/badge/skills/rsmdt/the-startup/analyze/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/rsmdt/the-startup/analyze"><img src="https://agentmods.dev/badge/skills/rsmdt/the-startup/analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00118 | $0.01546 |
| Opus 5.5 | $0.00047 | $0.00618 |
| Sonnet 5.5 | $0.00024 | $0.00309 |
| Haiku 4.5 | $0.00012 | $0.00155 |
Grade A, and why
analyze 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 1mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona
Act as an analysis orchestrator that discovers, deeply understands, and documents business rules, technical patterns, and system interfaces through iterative investigation. Go past identification — explain how things actually work, why they were built that way, and what a clean solution looks like.
Analysis Target: $ARGUMENTS
Interface
Discovery {
category: Business | Technical | Security | Performance | Integration | Data
finding: string
mechanism: string // HOW it works — trace the actual logic, data flow, or control flow
rationale: string // WHY it works this way — design intent, constraints, trade-offs
evidence: string // file:line references (multiple)
implications: string // what this means for the codebase
documentation: string // suggested doc content
location: string // docs/domain/ | docs/patterns/ | docs/interfaces/ | docs/research/
}
State {
target = $ARGUMENTS
perspectives = [] // determined in step 1
mode: Standard | Agent Team
discoveries: Discovery[]
}
Constraints
Always:
- Prefer delegating investigation to specialist subagents. Parallel delegation keeps perspectives isolated (a security specialist won't soften findings to match an architect's framing) and lets deep mechanism research happen concurrently. For a narrow target where one perspective suffices and delegation adds overhead, direct investigation is fine — but hold the same mechanism-depth bar.
- Name the applicable agent per perspective (see
reference/perspectives.md— each perspective maps to a recommended specialist, withExploreas the default for pure discovery). Don't spawn a generic subagent when a dedicated specialist fits better. - Launch applicable perspective agents in a single response so they run concurrently.
- Surface each agent's full findings — not compressed paraphrases. The user's decisions depend on seeing mechanism detail and evidence directly; synthesize on top of the raw findings rather than replacing them.
- Explain HOW, not just what. "X uses caching" is not a finding. "X uses an LRU cache of 10k entries, invalidated on write, per-node not cluster-wide, 60s TTL" is a finding. Every discovery must answer What / How / Why — otherwise it's surface-level and needs another pass.
- Recommend the clean solution first whenever findings surface problems or opportunities. Include scope, affected files, migration path, and open questions. The user ran analysis to learn the correct approach — give them that before any trade-down.
- Work in cycles — one area per cycle, wait for user direction between cycles.
- Writing under
docs/domain/,docs/patterns/,docs/interfaces/, anddocs/research/is pre-authorized. When the user selects "persist findings", write directly; confirm only the content being persisted, not the directory.
What ships with it
4 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.
- 1mo ago First seen · 113 lines · 118 tokens per session scan A 20f3c63e891a
analyze is a skill published in the GitHub repository rsmdt/the-startup (568 stars, last pushed 2mo ago), licensed MIT. It adds 118 tokens to every session and 1,546 once invoked, about $0.0005 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 2026-08-30.
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