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/petekp/claude-code-setup/deep-researchnpx skills add petekp/claude-code-setup --skill deep-researchgit clone --depth 1 https://github.com/petekp/claude-code-setupWrote 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/petekp/claude-code-setup/deep-research)<a href="https://agentmods.dev/skills/petekp/claude-code-setup/deep-research"><img src="https://agentmods.dev/badge/skills/petekp/claude-code-setup/deep-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 | $0.00119 | $0.01979 |
| Opus 5 | $0.00060 | $0.00989 |
| Sonnet 5 | $0.00024 | $0.00396 |
| Haiku 4.5 | $0.00012 | $0.00198 |
Grade A, and why
deep-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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Systematic methodology for conducting exhaustive, accurate research using all available tools. Prioritizes correctness over speed.
Core Principles
- Multiple sources required — Never rely on a single source for important claims
- Cross-reference everything — Verify facts appear consistently across independent sources
- Citation mandatory — Every claim must have a source; no unsourced assertions
- Acknowledge uncertainty — When sources conflict or are weak, say so explicitly
- Prefer primary sources — Official docs > blog posts > forum answers > AI-generated content
Available Research Tools
Use these tools in combination based on the research topic:
| Tool | Best For | Limitations |
|---|---|---|
| WebSearch | Current events, recent information, broad topic discovery | Results may be outdated, SEO-influenced |
| WebFetch | Reading specific URLs, extracting detailed content | Requires known URL |
| Playwright browser | Interactive sites, paywalled content (if logged in), complex navigation | Slower, requires more tokens |
| Context7/MCP docs | Library/framework documentation | Only indexed libraries |
| OpenAI docs MCP | OpenAI API specifics | OpenAI only |
| Grep/Glob/Read | Codebase research, finding implementations | Local files only |
Research Workflow
Phase 1: Scope Definition
Before researching, clarify:
- Core question — What specific question(s) need answering?
- Required depth — Surface overview or exhaustive deep-dive?
- Recency requirements — Is timeliness critical? (API versions, current events, etc.)
- Authoritative sources — What would count as a definitive answer?
Ask clarifying questions if scope is ambiguous. Use AskUserQuestion for structured choices when multiple research directions are possible.
Phase 2: Source Discovery
Cast a wide net to find relevant sources:
1. WebSearch with multiple query variations
- Try 3-5 different phrasings of the core question
- Include technical terms AND plain language
- Search for "[topic] official documentation"
- Search for "[topic] research paper" or "[topic] study"
2. Identify authoritative sources from results
- Official documentation sites
- Academic papers / research institutions
- Industry standards bodies
- Recognized experts in the field
3. Check specialized tools
- Context7 for library/framework docs
- OpenAI docs MCP for OpenAI-specific topics
- GitHub/codebase for implementation details
What ships with it
2 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.
- 3d ago First seen · 243 lines · 119 tokens per session scan A f9334538bf23
deep-research is a skill published in the GitHub repository petekp/claude-code-setup (45 stars, last pushed 27d ago), licensed MIT. It adds 119 tokens to every session and 1,979 once invoked, about $0.0006 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 2026-08-31.
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