<objective> Research how to implement an objective. Spawns df-objective-researcher agent with objective context.

Note: This is a standalone research command. For most workflows, use /df:plan-objective which integrates research automatically.

Use this command when:

•You want to research without planning yet
•You want to re-research after planning is complete
•You need to investigate before deciding if an objective is feasible

Orchestrator role: Parse objective, validate against roadmap, check existing research, gather context, spawn researcher agent, present results.

Why subagent: Research burns context fast (WebSearch, Context7 queries, source verification). Fresh 200k context for investigation. Main context stays lean for user interaction. </objective>

<context> Objective number: $ARGUMENTS (required)

Normalize objective input in step 1 before any directory lookups. </context>

0. Initialize Context

bash

INIT=$(node ~/.claude/devflow/bin/df-tools.cjs init objective-op "$ARGUMENTS")

Extract from init JSON: objective_dir, objective_number, objective_name, objective_found, commit_docs, has_research.

Resolve researcher model:

bash

RESEARCHER_MODEL=$(node ~/.claude/devflow/bin/df-tools.cjs resolve-model df-objective-researcher --raw)

1. Validate Objective

bash

OBJECTIVE_INFO=$(node ~/.claude/devflow/bin/df-tools.cjs roadmap get-objective "${objective_number}")

If found is false: Error and exit. If found is true: Extract objective_number, objective_name, goal from JSON.

2. Check Existing Research

bash

ls .planning/objectives/${OBJECTIVE}-*/RESEARCH.md 2>/dev/null

If exists: Offer: 1) Update research, 2) View existing, 3) Skip. Wait for response.

If doesn't exist: Continue.

3. Gather Objective Context

bash

# Objective section already loaded in OBJECTIVE_INFO
echo "$OBJECTIVE_INFO" | jq -r '.section'
cat .planning/REQUIREMENTS.md 2>/dev/null
cat .planning/objectives/${OBJECTIVE}-*/*-CONTEXT.md 2>/dev/null
grep -A30 "### Decisions Made" .planning/STATE.md 2>/dev/null

Present summary with objective description, requirements, prior decisions.

4. Spawn df-objective-researcher Agent

Research modes: ecosystem (default), feasibility, implementation, comparison.

markdown

<research_type>
Objective Research — investigating HOW to implement a specific objective well.
</research_type>

<key_insight>
The question is NOT "which library should I use?"

The question is: "What do I not know that I don't know?"

For this objective, discover:
- What's the established architecture pattern?
- What libraries form the standard stack?
- What problems do people commonly hit?
- What's SOTA vs what Claude's training thinks is SOTA?
- What should NOT be hand-rolled?
</key_insight>

<objective>
Research implementation approach for Objective {objective_number}: {objective_name}
Mode: ecosystem
</objective>

<context>
**Objective description:** {phase_description}
**Requirements:** {requirements_list}
**Prior decisions:** {decisions_if_any}
**Objective context:** {context_md_content}
</context>

<downstream_consumer>
Your RESEARCH.md will be loaded by `/df:plan-objective` which uses specific sections:
- `## Standard Stack` → Plans use these libraries
- `## Architecture Patterns` → Task structure follows these
- `## Don't Hand-Roll` → Tasks NEVER build custom solutions for listed problems
- `## Common Pitfalls` → Verification steps check for these
- `## Code Examples` → Task actions reference these patterns

Be prescriptive, not exploratory. "Use X" not "Consider X or Y."
</downstream_consumer>

<quality_gate>
Before declaring complete, verify:
- [ ] All domains investigated (not just some)
- [ ] Negative claims verified with official docs
- [ ] Multiple sources for critical claims
- [ ] Confidence levels assigned honestly
- [ ] Section names match what plan-objective expects
</quality_gate>

<output>
Write to: .planning/objectives/${OBJECTIVE}-{slug}/${OBJECTIVE}-RESEARCH.md
</output>

code

Task(
  prompt="First, read ~/.claude/agents/df-objective-researcher.md for your role and instructions.\n\n" + filled_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Research Objective {objective}"
)

5. Handle Agent Return

## RESEARCH COMPLETE: Display summary, offer: Plan objective, Dig deeper, Review full, Done.

## CHECKPOINT REACHED: Present to user, get response, spawn continuation.

## RESEARCH INCONCLUSIVE: Show what was attempted, offer: Add context, Try different mode, Manual.

6. Spawn Continuation Agent

markdown

<objective>
Continue research for Objective {objective_number}: {objective_name}
</objective>

<prior_state>
Research file: @.planning/objectives/${OBJECTIVE}-{slug}/${OBJECTIVE}-RESEARCH.md
</prior_state>

<checkpoint_response>
**Type:** {checkpoint_type}
**Response:** {user_response}
</checkpoint_response>

code

Task(
  prompt="First, read ~/.claude/agents/df-objective-researcher.md for your role and instructions.\n\n" + continuation_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Continue research Objective {objective}"
)

</process>

<success_criteria>

• Objective validated against roadmap
• Existing research checked
• df-objective-researcher spawned with context
• Checkpoints handled correctly
• User knows next steps </success_criteria>