Borrowing it
Nothing to install: this file belongs to buildonspark/spark. 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/buildonspark/spark/main/.claude/commands/research_codebase.mdgit clone --depth 1 https://github.com/buildonspark/sparkWrote 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/buildonspark/spark/research_codebase)<a href="https://agentmods.dev/commands/buildonspark/spark/research_codebase"><img src="https://agentmods.dev/badge/commands/buildonspark/spark/research_codebase/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/buildonspark/spark/research_codebase"><img src="https://agentmods.dev/badge/commands/buildonspark/spark/research_codebase.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 | $0.00010 | $0.01884 |
| Opus 5.5 | $0.00004 | $0.00754 |
| Sonnet 5.5 | $0.00002 | $0.00377 |
| Haiku 4.5 | $0.00001 | $0.00188 |
Grade A, and why
research_codebase 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research_codebase_generic — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Codebase
You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
Initial Setup:
When this command is invoked, respond with:
I'm ready to research the codebase. Please provide your research question or area of interest, and I'll analyze it thoroughly by exploring relevant components and connections.
Then wait for the user's research query.
Steps to follow after receiving the research query:
-
Read any directly mentioned files first:
- If the user mentions specific files (tickets, docs, JSON), read them FULLY first
- IMPORTANT: Use the Read tool WITHOUT limit/offset parameters to read entire files
- CRITICAL: Read these files yourself in the main context before spawning any sub-tasks
- This ensures you have full context before decomposing the research
-
Analyze and decompose the research question:
- Break down the user's query into composable research areas
- Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
- Identify specific components, patterns, or concepts to investigate
- Create a research plan using TodoWrite to track all subtasks
- Consider which directories, files, or architectural patterns are relevant
-
Spawn parallel sub-agent tasks for comprehensive research:
- Create multiple Task agents to research different aspects concurrently
The key is to use these agents intelligently:
- Start with locator agents to find what exists
- Then use analyzer agents on the most promising findings
- Run multiple agents in parallel when they're searching for different things
- Each agent knows its job - just tell it what you're looking for
- Don't write detailed prompts about HOW to search - the agents already know
-
Wait for all sub-agents to complete and synthesize findings:
- IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
- Compile all sub-agent results (both codebase and thoughts findings)
- Prioritize live codebase findings as primary source of truth
- Use thoughts/ findings as supplementary historical context
- Connect findings across different components
- Include specific file paths and line numbers for reference
- Verify all thoughts/ paths are correct (e.g., thoughts/allison/ not thoughts/shared/ for personal files)
- Highlight patterns, connections, and architectural decisions
- Answer the user's specific questions with concrete evidence
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 First seen · 180 lines · 10 tokens per session scan A 646663b5c408
research_codebase is a command published in the GitHub repository buildonspark/spark (113 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 1,884 once invoked, about $0.0000 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 2026-09-03.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.