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 vaayne/agent-kit --skill scoutgit clone --depth 1 https://github.com/vaayne/agent-kitWrote 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/vaayne/agent-kit/scout)<a href="https://agentmods.dev/skills/vaayne/agent-kit/scout"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/scout/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/vaayne/agent-kit/scout"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/scout.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 | $0.00122 | $0.00843 |
| Opus 5.5 | $0.00049 | $0.00337 |
| Sonnet 5.5 | $0.00024 | $0.00169 |
| Haiku 4.5 | $0.00012 | $0.00084 |
Grade A, and why
scout 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scout
Distilled from Thariq's A Field Guide to Fable: Finding Your Unknowns.
Premise: the prompt and context are the map; the codebase and its real constraints are the territory. The gap between them is the user's unknowns, and work quality is bottlenecked by clarifying them — before, during, and after implementation. This skill is a toolkit, not a workflow: diagnose, pick one technique, run it inline. Produce no artifact beyond what the technique itself outputs.
Diagnose first
Before picking a technique, collect the user's starting point — experience with the domain, familiarity with this part of the codebase, where they are in their thought process. Every technique below degrades to generic output without it. Then place the unknowns:
| Quadrant | Signal | Technique |
|---|---|---|
| Known knowns | Already in the prompt | Just work |
| Known unknowns | "I haven't decided X yet" | Interview |
| Unknown knowns | "I'll know it when I see it" | Prototypes, References |
| Unknown unknowns | Unfamiliar domain or code area | Blindspot pass first |
Before implementation
- Blindspot pass — the user doesn't know what questions to ask. Survey the territory for them: what good looks like, prior art in the repo, common potholes, the domain vocabulary. Teach, don't list — the output should upgrade their next prompt, not just enumerate gaps.
- Brainstorm + prototypes — criteria they'd only recognize on sight. Show several genuinely different directions with fake data before wiring anything up (HTML artifact for anything visual). Also works for scope: brainstorm interventions from cheapest to most ambitious and let them react.
- Interview — one question at a time, prioritizing questions whose answers would change the architecture. The
grillskill is the heavy-duty version. - References — when pointing beats describing. Source code is the best reference: read the implementation they like (any language) and reimplement its semantics, not its surface.
- Decision-first plan — order the plan by what the user is most likely to tweak: data models, type interfaces, user-facing flows on top; mechanical refactors at the bottom.
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 Changed fa015a0aedc1
- 1mo ago First seen · 53 lines · 122 tokens per session scan A 4896ad78ffe0
scout is a skill published in the GitHub repository vaayne/agent-kit (53 stars, last pushed 4d ago), licensed MIT. It adds 122 tokens to every session and 843 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…