spok-create-research

name	spok-create-research
description	research the codebase

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.

CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY

• DO NOT suggest improvements or changes unless the user explicitly asks for them
• DO NOT perform root cause analysis unless the user explicitly asks for them
• DO NOT propose future enhancements unless the user explicitly asks for them
• DO NOT critique the implementation or identify problems
• DO NOT recommend refactoring, optimization, or architectural changes
• ONLY describe what exists, where it exists, how it works, and how components interact
• You are creating a technical map/documentation of the existing system

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:

1. Read any directly mentioned files first:

   • If the user mentions specific files (docs, JSON, research questions), 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
   • DO NOT read ticket files - research must stay objective about the current codebase, not be influenced by what a ticket wants to build. The research questions already capture what needs to be investigated.
   • This ensures you have full context before decomposing the research

2. 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

3. Spawn parallel sub-agent tasks for comprehensive research:

   • Create multiple Task agents to research different aspects concurrently
   • We now have specialized agents that know how to do specific research tasks:

   For codebase research:

   • Use the codebase-locator agent to find WHERE files and components live
   • Use the codebase-analyzer agent to understand HOW specific code works
   • Use the codebase-pattern-finder agent to find examples of existing patterns

   For web research (only if user explicitly asks):

   • Use the web-search-researcher agent for external documentation and resources
   • IF you use web-research agents, instruct them to return LINKS with their findings, and please INCLUDE those links in your final report

   The key is to use these agents intelligently:

   • Combine related questions: Don't necessarily launch one subagent per research question. Group related questions that touch the same area of the codebase into a single subagent prompt. For example, if 3 questions are about how the daemon handles sessions, combine them into one codebase-analyzer call.
   • Aim for 2-6 well-scoped subagents rather than 1:1 question-to-agent mapping - this is not a hard rule, just guidance
   • Start with locator agents to find what exists
   • Then use analyzer agents on the most promising findings to document how they work
   • Run multiple agents in parallel when they're searching for different areas of the codebase
   • 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

4. 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
   • Prioritize live codebase findings as primary source of truth
   • Connect findings across different components
   • Include specific file paths and line numbers for reference
   • Verify path correctness — task-specific files go in <task-dir>/
   • Highlight patterns, connections, and architectural decisions
   • Answer the user's specific questions with concrete evidence

5. Gather metadata for the research document:

   • Output path: <task-dir>/research.md
   • The skill argument is the absolute path to the research-questions file. Derive <task-dir> as its parent directory (the directory already exists — do not create or search for it).
   • Filename is bare: research.md (no date prefix).

6. Generate research document:

   • Use the metadata gathered in step 4
   • Read the research template:

   Read({SKILLBASE}/references/research_template.md)

   • Before writing, delete any sibling files matching <task-dir>/[0-9]{4}-[0-9]{2}-[0-9]{2}-research.md (legacy date-prefixed orphans from pre-fork runs).
   • Write the document to <task-dir>/research.md

7. Note cloud permalinks: Cloud permalinks are automatically provided when you write artifacts. Include them in your final output.

   For code references in the synclayer repo (if on main or pushed):

   • Get repo info: gh repo view --json owner,name
   • Create permalinks: https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}

8. Respond to the user according to the template

   • Read the final output template: Read({SKILLBASE}/references/research_final_answer.md)
   • Respond with a summary following the template, including GitHub permalinks.

9. Handle follow-up questions:

   • If the user has follow-up questions, append to the same research document
   • Update the frontmatter fields last_updated and last_updated_by to reflect the update
   • Add last_updated_note: "Added follow-up research for [brief description]" to frontmatter
   • Add a new section: ## Follow-up Research [timestamp]
   • Spawn new sub-agents as needed for additional investigation
   • Continue updating the document

## Important notes: - Use parallel Task agents to maximize efficiency and minimize context usage - Focus on finding concrete file paths and line numbers for developer reference - Research documents should be self-contained with all necessary context - Each sub-agent prompt should be specific and focused on read-only documentation operations - Document cross-component connections and how systems interact - Link to GitHub when possible for permanent references - Stay focused on synthesis, not deep file reading - Have sub-agents document examples and usage patterns as they exist - **REMEMBER**: Document and Ask about what IS and WHY, not what SHOULD BE - **NO RECOMMENDATIONS OR IMPLEMENTATION SUGGESTIONS**: Only describe the current state of the codebase - **Testing patterns**: For each component area you research, document how it's currently tested (unit, integration, e2e). Include test file locations and patterns. The research template has a "Testing patterns" section under each component - always fill it in. - **File reading**: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks - **Critical ordering**: Follow the numbered steps exactly - ALWAYS read mentioned files first before spawning sub-tasks (step 1) - ALWAYS wait for all sub-agents to complete before synthesizing (step 4) - ALWAYS gather metadata before writing the document (step 5 before step 6) - NEVER write the research document with placeholder values - **Path handling**: Task-specific research goes in `/research.md`

Response

Remember, you must respond to the user according to the output template at {SKILLBASE}/references/research_final_answer.md

Markdown Formatting

When writing markdown files that contain code blocks showing other markdown (like README examples or SKILL.md templates), use 4 backticks (````) for the outer fence so inner 3-backtick code blocks don't prematurely close it:

# Example README
## Installation
```bash
npm install example
```