Borrowing it
Nothing to install: this file belongs to warpdotdev/common-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/warpdotdev/common-skills/main/.agents/skills/research/SKILL.mdgit clone --depth 1 https://github.com/warpdotdev/common-skillsWrote 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/warpdotdev/common-skills/research)<a href="https://agentmods.dev/skills/warpdotdev/common-skills/research"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/research/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/warpdotdev/common-skills/research"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.01139 |
| Opus 5.5 | $0.00047 | $0.00456 |
| Sonnet 5.5 | $0.00024 | $0.00228 |
| Haiku 4.5 | $0.00012 | $0.00114 |
Grade A, and why
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 21d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Use this skill to answer a question by delegating the work of finding the answer to a subagent, so that the byproducts of that work — file contents, log noise, dead-end reads — never enter your own context. You get back a distilled answer plus the evidence that supports it, and you stay sharp for the actual task.
Why this matters
Your context window is your most valuable and limited resource. Reading twenty files to discover that three of them mattered permanently pollutes your context with seventeen files of noise, degrading every subsequent decision you make. A subagent absorbs that noise on your behalf and hands you only the signal. Think of it as asking a colleague to dig through the archives and report back, rather than dumping the whole archive on your desk.
When to use it
Reach for research delegation when the cost of producing the answer is far greater than the answer itself. Strong signals:
- You'd need to read many files to find the few that are relevant.
- You'd need to wade through long test output, CI logs, or stack traces to extract a failure.
- You'd need to survey how a pattern, API, or symbol is used across the whole repo.
- You'd need to read and summarize a large diff or PR.
- The question has several independent sub-parts that could be investigated separately.
Examples — good fits:
- "What's the root cause of this failing test?" (the subagent reads the logs and traces the code; you get the cause)
- "How is
SessionManagerused across the codebase?" (the subagent greps and reads; you get a summary with call sites) - "Summarize what this 4,000-line PR changes and why." (the subagent reads the diff; you get the shape of it)
Examples — do NOT delegate:
- Reading 2–3 files you already know you need. Just read them directly; delegation adds latency for no context savings.
- A single
grepor one-line lookup. Do it yourself. - Anything where you need the raw material for your next step. If you're about to edit the files you'd be reading, delegating is counterproductive — you'd just have to re-read them yourself to make the change. Research delegation pays off when the output is a conclusion, not when it's material you'll work on directly.
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.
- 21d ago Changed 85fc0d0d3130
- 1mo ago First seen · 72 lines · 118 tokens per session scan A e059166ff4ef
research is a skill published in the GitHub repository warpdotdev/common-skills (636 stars, last pushed today), licensed MIT. It adds 118 tokens to every session and 1,139 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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…