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.
git clone --depth 1 https://github.com/cloudnative-co/claude-code-starter-kitWrote 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/cloudnative-co/claude-code-starter-kit/research)<a href="https://agentmods.dev/commands/cloudnative-co/claude-code-starter-kit/research"><img src="https://agentmods.dev/badge/commands/cloudnative-co/claude-code-starter-kit/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/cloudnative-co/claude-code-starter-kit/research"><img src="https://agentmods.dev/badge/commands/cloudnative-co/claude-code-starter-kit/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 | $0.00000 | $0.00407 |
| Opus 5.5 | $0.00000 | $0.00163 |
| Sonnet 5.5 | $0.00000 | $0.00081 |
| Haiku 4.5 | $0.00000 | $0.00041 |
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 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.
What it actually says
/research - Deep Codebase Investigation
Perform a thorough investigation of the codebase before making any changes.
Instructions
You are entering Research Phase. Your goal is to deeply understand the relevant parts of the codebase before any planning or implementation begins.
Steps
-
Identify scope: Based on the user's request, determine which parts of the codebase are relevant.
-
Deep read: Read all relevant files thoroughly. Don't skim - understand the actual logic, edge cases, and design decisions.
-
Map dependencies: Trace imports, function calls, and data flow across files. Document the dependency graph.
-
Identify patterns: Note existing patterns, conventions, abstractions, and architectural decisions already in the codebase.
-
Find constraints: Identify tests, type contracts, API boundaries, and invariants that must be preserved.
-
Generate research.md: Save your findings as
research.mdin the project root (or a designated docs directory) with the following structure:
# Research: [Topic]
## Date: [YYYY-MM-DD]
## Scope
## Key Files
## Architecture & Patterns
## Dependencies & Data Flow
## Constraints & Invariants
## Risks & Considerations
## Recommendations for Planning Phase
Rules
- DO NOT write any code during this phase.
- DO NOT create a plan during this phase. That is for
/plan. - Focus exclusively on understanding and documenting.
- If the codebase is large, use Subagents to investigate different areas in parallel.
- After generating research.md, suggest the user review it and then proceed to
/plan.
After Research
Recommend:
- Review
research.mdand add inline annotations for anything unclear or needing correction. - When satisfied, run
/planto create an implementation plan based on the research. - Consider running
/compactbefore/planto start planning with fresh context (FIC).
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 · 49 lines · 0 tokens per session scan A 28f05c9b588a
research is a command published in the GitHub repository cloudnative-co/claude-code-starter-kit (153 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 407 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 2026-08-30.
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.