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/ngocsangyem/meowkit/researchnpx skills add ngocsangyem/MeowKit --skill researchgit clone --depth 1 https://github.com/ngocsangyem/MeowKitWrote 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/ngocsangyem/meowkit/research)<a href="https://agentmods.dev/skills/ngocsangyem/meowkit/research"><img src="https://agentmods.dev/badge/skills/ngocsangyem/meowkit/research.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 5.1 | $0.00165 | $0.01631 |
| Opus 5 | $0.00082 | $0.00816 |
| Sonnet 5 | $0.00033 | $0.00326 |
| Haiku 4.5 | $0.00016 | $0.00163 |
Grade A, and why
mk:research 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 3d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Front door for deep, multi-source technical research. Wraps a research subagent, keeps the main session unblocked, and returns one cited markdown report. Report-only — it never edits source code.
Differentiator: one library or API question is not research — that is mk:docs-finder. Research answers decisions that need evidence from several independent sources ("evaluate X vs Y", "how do others solve Z", "what does the spec actually say").
Process
Outcome-oriented, not a fixed script.
- Scope the question in one sentence. Confirm the budget only if the user hinted at depth; otherwise use the default below.
- Internal bridge (optional) — when the topic touches the current codebase, run
mk:scoutfirst and feed a short summary into the research prompt. - Spawn the researcher. Default: one researcher subagent.
--deep: two parallel researcher subagents on independent sub-topics with zero file overlap. Give each an inline delegation prompt carrying: work context, report save path, acceptance criteria, constraints, budget, and the Source Discipline block below. - Assemble the returned findings into one report at the save path (see Save Paths). Never fetch a URL yourself — the researcher owns retrieval.
Source Discipline
Inject this block into every researcher prompt:
- Primary sources. Follow every claim back to the source that owns it — cite the owner, not a summary of it.
- Require ≥2 independent sources for any load-bearing claim; attach a confidence level (high / medium / low) to each finding.
- Prefer material from the last 12 months; mark anything older as potentially stale.
- Retrieval chain:
mk:docs-finderfor library / framework / API docs →mk:web-to-markdown --wtm-accept-risk --caller mk:researchfor arbitrary pages →WebSearchas last resort. Never fetch a URL directly; do NOT call WebFetch even if the subagent has it granted — delegate every fetch through the chain. - Budget: max 5 retrieval calls per researcher. Hard cap. Raise only when the user explicitly asks for more depth.
What ships with it
1 file 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.
- 3d ago First seen · 126 lines · 165 tokens per session scan A 3aed3ac087c9
mk:research is a skill published in the GitHub repository ngocsangyem/MeowKit (14 stars, last pushed 1mo ago), licensed MIT. It adds 165 tokens to every session and 1,631 once invoked, about $0.0008 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 2026-09-03.
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research-ops
Use when someone hands you an open question whose answer must survive scrutiny — a tech choice, a regulation, a what-is-actually-true-about-X — with every non-obvious claim dated and sourced, source disagreements surfaced rather than averaged away, and a cited memo as the deliverable. Also for refreshing a stale…
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用证据链和 X/Y/Z 立体分析法研究产品、公司、技术、概念、人物、行业、市场或复杂事件，交付可追溯的深度研究报告。用户要求 deep research、系统调研、竞品或市场研究、尽职调查、来龙去脉分析、证据链或正式研究报告时使用。简单名词解释、新闻摘要、短篇观点、仿写，以及 3D 建模、渲染、CAD 或图形设计不使用。.
karpathy-coding-principles
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workplace-message-writer
目标不是把话写得更标准，而是帮助用户把真实意思说清楚，让对方知道重点以及接下来要做什么，同时保留用户本人的说话方式。.