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 ntaffzii/Skill-Agents --skill repo-tourgit clone --depth 1 https://github.com/ntaffzii/Skill-AgentsWrote 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/ntaffzii/skill-agents/repo-tour)<a href="https://agentmods.dev/skills/ntaffzii/skill-agents/repo-tour"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/repo-tour/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/ntaffzii/skill-agents/repo-tour"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/repo-tour.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 | $0.00146 | $0.02768 |
| Opus 5.5 | $0.00058 | $0.01107 |
| Sonnet 5.5 | $0.00029 | $0.00554 |
| Haiku 4.5 | $0.00015 | $0.00277 |
Grade A, and why
repo-tour 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repo Tour (reads an existing graphify graph)
Overview
graphify already builds a persistent knowledge graph (graphify-out/graph.json) from a codebase — that's the expensive, token-hungry part, and it's already solved. What's missing is three specific consumption modes: a dependency-ordered reading tour, an onboarding doc, and a diff-impact report. This skill supplies those three by reading the existing graph, not by re-scanning the repo — cheap, fast, and doesn't duplicate graphify's own extraction pipeline.
Fast-path rule (check this first, every time): if graphify-out/graph.json does not exist, stop and tell the user to run graphify first (/graphify or the graphify skill). Do not fall back to reading files manually — that's project-discovery's job, not this skill's.
Cost note before suggesting a graphify run: repo-tour itself makes no LLM calls and costs nothing to run. Building the graph in the first place is not free, though — graphify's semantic extraction pass (anything that isn't code: .md, .pdf, images) dispatches an LLM subagent per ~20-25 files, and a real test run in this repo (10 small Markdown files) used ~116k output tokens for that one pass. If no graph exists yet and the corpus is doc-heavy or large, say so explicitly and let the user decide whether to scope the graphify run narrower (a subdirectory, not the whole repo) rather than defaulting to a full-repo build. For a genuine one-off "explain this to me" question with no existing graph and no repeat use planned, project-discovery is usually cheaper — repo-tour's value shows up when a graph already exists (regular graphify use) or the same graph gets reused across multiple onboarding/diff-impact checks, not on a single cold-start question.
When to use
- สร้างเอกสาร onboarding สำหรับทีมใหม่ จากกราฟที่ graphify สร้างไว้แล้ว
- สร้างลำดับการอ่านโค้ดที่แนะนำ (จากโมดูลพื้นฐานไปหาโมดูลที่พึ่งพามัน)
- เช็คว่า diff/PR ปัจจุบันกระทบส่วนไหนของระบบบ้าง ก่อน commit
- Any request to "explain this repo to a new person" or "what breaks if I change X" when a graph already exists
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.
- 2mo ago First seen · 100 lines · 146 tokens per session scan A c5e2b9ff3a3e
repo-tour is a skill published in the GitHub repository ntaffzii/Skill-Agents (4 stars, last pushed 27d ago), licensed MIT. It adds 146 tokens to every session and 2,768 once invoked, about $0.0006 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.
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