Borrowing it
Nothing to install: this file belongs to wbern/agent-instructions. 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/wbern/agent-instructions/main/.claude/commands/research.mdgit clone --depth 1 https://github.com/wbern/agent-instructionsWrote 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/commands/wbern/agent-instructions/research)<a href="https://agentmods.dev/commands/wbern/agent-instructions/research"><img src="https://agentmods.dev/badge/commands/wbern/agent-instructions/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/commands/wbern/agent-instructions/research"><img src="https://agentmods.dev/badge/commands/wbern/agent-instructions/research.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 | $0.00021 | $0.00727 |
| Opus 5.5 | $0.00008 | $0.00291 |
| Sonnet 5.5 | $0.00004 | $0.00145 |
| Haiku 4.5 | $0.00002 | $0.00073 |
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 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
General Guidelines
Output Style
- Never explicitly mention TDD in code, comments, commits, PRs, or issues
- Write natural, descriptive code without meta-commentary about the development process
- The code should speak for itself - TDD is the process, not the product
Beads is available for task tracking. Use mcp__beads__* tools to manage issues (the user interacts via bd commands).
User arguments:
Research: $ARGUMENTS
End of user arguments
Research the following problem or question thoroughly, like a senior developer would.
Step 1: Launch Parallel Research Agents
Use the Task tool to spawn these subagents in parallel (all in a single message):
-
Web Documentation Agent (subagent_type: general-purpose)
- Search official documentation for the topic
- Find best practices and recommended patterns
- Locate relevant GitHub issues or discussions
-
Web Search Agent (subagent_type: general-purpose)
- Perform broad web searches for solutions and discussions
- Find Stack Overflow answers, blog posts, and tutorials
- Note common pitfalls and gotchas
-
Codebase Explorer Agent (subagent_type: Explore)
- Search the codebase for related patterns
- Find existing solutions to similar problems
- Identify relevant files, functions, or components
Step 2: Library Documentation (Optional)
If the research involves specific frameworks or libraries:
- Use Context7 MCP tools (mcp__context7__resolve-library-id, then get-library-docs)
- Get up-to-date API references and code examples
- If Context7 is unavailable, note this in findings so user knows library docs were harder to obtain
Step 3: Deep Analysis
With all gathered context, perform extended reasoning (ultrathink) to:
- Analyze the problem from first principles
- Consider edge cases and trade-offs
- Synthesize insights across all sources
- Identify conflicts between sources
Step 4: Present Findings
Present a structured summary to the user:
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 · 108 lines · 21 tokens per session scan A bbbd0fff1b8d
research is a command published in the GitHub repository wbern/agent-instructions (168 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 727 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.