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 commands/seekayel/claude-plugins/1_research_codebasegit clone --depth 1 https://github.com/seekayel/claude-pluginsWhat 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 | $0.00000 | $0.00790 |
| Opus 5 | $0.00000 | $0.00395 |
| Sonnet 5 | $0.00000 | $0.00158 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
1_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 2d 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 — 97 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, read them FULLY first
- Use the Read tool WITHOUT limit/offset parameters to read entire files
- Read these files yourself in the main context before spawning any sub-tasks
-
Analyze and decompose the research question:
- Break down the user's query into composable research areas
- 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
- Use specialized agents like codebase-locator, codebase-analyzer, pattern-finder
- Run multiple agents in parallel when searching for different things
-
Wait for all sub-agents to complete and synthesize findings:
- Wait for ALL sub-agent tasks to complete before proceeding
- Compile all sub-agent results
- Connect findings across different components
- Include specific file paths and line numbers for reference
- Highlight patterns, connections, and architectural decisions
-
Generate research document: Structure the document with YAML frontmatter followed by content:
--- date: [Current date and time in ISO format] researcher: Claude topic: "[User's Question/Topic]" tags: [research, codebase, relevant-component-names] status: complete --- # Research: [User's Question/Topic] ## Research Question [Original user query] ## Summary [High-level findings answering the user's question] ## Detailed Findings ### [Component/Area 1] - Finding with reference (file.ext:line) - Connection to other components - Implementation details ### [Component/Area 2] ... ## Code References - `path/to/file.py:123` - Description of what's there - `another/file.ts:45-67` - Description of the code block ## Architecture Insights [Patterns, conventions, and design decisions discovered] ## Open Questions [Any areas that need further investigation]
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.
- 2d ago First seen · 97 lines · 0 tokens per session scan A 957308aa05d7
1_research_codebase is a command published in the GitHub repository seekayel/claude-plugins (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 790 tokens. 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.