Research:resume

Research Resume Skill

Resume work on a research plan. Handles three states: spawning agents, checking progress, and synthesizing findings.

Process

1. Scan for research plans:

   • Look for research/NNNN-*/research-plan.md files (exclude research/archive/)
   • A research plan is active if its .research-state.json has status other than "complete" or "archived"
   • If no state file exists, treat as active if the research-plan.md exists

2. Handle different scenarios:

   No active research found:

   No active research plans found in `research/`.
   
   To create a new research plan, use `/research:create <topic description>`.
   

   Multiple active research plans: List all and ask user to choose:

   Found multiple active research plans:
   
   1. `research/0001-edge-routing/` - Status: in_progress (3/5 questions complete)
   2. `research/0002-layout-algo/` - Status: planned (not started)
   
   Which research plan would you like to resume?
   

   Single plan found: Proceed to state-based handling below.

3. Read .research-state.json and handle based on status:

State: planned (agents not yet spawned)

1. Display the research plan summary
2. Ask user to confirm they want to start the research
3. Spawn parallel subagents using the Task tool:
   • Use subagent_type=Explore for codebase investigation questions
   • Use subagent_type=general-purpose for questions requiring web research or multi-source analysis
   • Launch all agents in a single message with multiple Task tool calls for true parallelism
   • Each agent's prompt should include:
     • The specific question to investigate
     • The where/what/how/why framework from the research plan
     • The sources to consult
     • Instructions to write findings to the output file using the findings template
     • The full path to the output file: research/NNNN-topic/qN-filename.md
   • Run agents in the background using run_in_background: true so they execute in parallel
4. Collect agent IDs from all Task results
5. Update .research-state.json:

   {
     "status": "in_progress",
     "updated_at": "...",
     "agent_ids": ["agent-1-id", "agent-2-id", "agent-3-id"],
     ...
   }
   

6. Update research-plan.md status to IN PROGRESS and update the Expected Outputs table statuses
7. Display:

   **Research started:** `research/NNNN-topic-name/`
   **Agents spawned:** N parallel investigations
   **Agent IDs:** `id1`, `id2`, `id3`
   
   Agents are running in the background. Run `/research:resume` to check progress and synthesize findings when complete.
   

State: in_progress (agents spawned, awaiting completion)

1. Check each agent's output file — if the file exists and has content, that question is complete

2. Read completed findings files to verify they have substantive content

3. Update research-plan.md Expected Outputs table with current status

4. Display progress:

   **Research progress:** `research/NNNN-topic-name/`
   **Questions:** X/N complete
   
   **Complete:**
   - Q1: [Title] -> `q1-file.md`
   - Q3: [Title] -> `q3-file.md`
   
   **Pending:**
   - Q2: [Title] -> `q2-file.md` (agent: `id2`)
   

5. If all questions complete, proceed to synthesis (see below)

6. If some are pending, offer to:

   • Wait and check again later (/research:resume)
   • Proceed to partial synthesis with available findings
   • Re-spawn failed agents

State: in_progress with all questions complete -> Synthesis

1. Read all findings files

2. Synthesize findings into synthesis.md:

   # Research Synthesis: Topic Name
   
   ## Summary
   
   [3-5 sentence executive summary of all findings]
   
   ## Key Findings
   
   ### [Finding 1 title]
   [Cross-cutting finding that draws from multiple questions]
   
   ### [Finding 2 title]
   [Another cross-cutting finding]
   
   ## Recommendations
   
   1. **[Recommendation]** — [Rationale based on findings]
   2. **[Recommendation]** — [Rationale]
   
   ## Where/What/How/Why Summary
   
   | Aspect | Key Points |
   |--------|------------|
   | **Where** | [Key locations/sources identified] |
   | **What** | [Core facts discovered] |
   | **How** | [Key mechanisms understood] |
   | **Why** | [Design rationale and tradeoffs] |
   
   ## Open Questions
   
   - [Questions that emerged and may warrant deeper research]
   
   ## Next Steps
   
   - [ ] [Suggested follow-up action]
   - [ ] [Suggested follow-up action]
   
   ## Source Files
   
   | File | Question |
   |------|----------|
   | `q1-file.md` | Q1: Title |
   | `q2-file.md` | Q2: Title |
   

3. Update .research-state.json:

   {
     "status": "synthesized",
     "updated_at": "...",
     "synthesis_agent_id": null,
     "last_session_notes": "Synthesis complete. N findings, M recommendations.",
     ...
   }
   

   Note: synthesis_agent_id is null when synthesis is done inline. Set it to an agent ID if a separate agent performed synthesis.

4. Update research-plan.md status to SYNTHESIZED and mark synthesis as complete in Expected Outputs

5. Display:

   **Research synthesized:** `research/NNNN-topic-name/`
   **Findings:** N questions answered
   **Synthesis:** `synthesis.md`
   
   **Key findings:**
   - [Finding 1]
   - [Finding 2]
   
   **Recommendations:**
   - [Recommendation 1]
   - [Recommendation 2]
   
   To create an implementation plan based on this research, run `/plan:create` and reference `research/NNNN-topic-name/`.
   
   To archive this research, run `/research:archive NNNN`.
   

State: synthesized (synthesis complete)

1. Display the synthesis summary
2. Offer options:
   • Create a deeper investigation on a subtopic (hierarchical research)
   • Create an implementation plan based on findings
   • Archive the research

Deeper Investigation (Hierarchical Research)

If the user wants to investigate a subtopic further:

1. Create research/NNNN-topic/subtopic-name/ subdirectory
2. Create a new research-plan.md and .research-state.json inside it
3. The parent research's synthesis should note the child investigation
4. Follow the same create/resume lifecycle for the child

Session Notes

Before ending any session, update .research-state.json with:

• updated_at: current UTC timestamp
• last_session_notes: summary of what happened and what to do next