| name | df:research-objective |
| description | Research how to implement an objective before planning.
Use when the user wants to investigate, research, or explore options before planning an objective.
Triggers on: "research objective", "investigate before planning", "look into how to build", "what's the best approach for objective"
|
| argument-hint | [objective] |
| allowed-tools | ["Read","Bash","Task"] |
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 number: $ARGUMENTS (required)
Normalize objective input in step 1 before any directory lookups.
0. Initialize Context
INIT=$(node ~/.claude/devflow/bin/df-tools.cjs init objective-op "$ARGUMENTS")
Extract from init JSON: objective_dir, objective_number, objective_name, objective_found, commit_docs, has_research.
Resolve researcher model:
RESEARCHER_MODEL=$(node ~/.claude/devflow/bin/df-tools.cjs resolve-model df-objective-researcher --raw)
1. Validate Objective
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
ls .planning/objectives/${OBJECTIVE}-*/RESEARCH.md 2>/dev/null
If exists: Offer: 1) Update research, 2) View existing, 3) Skip. Wait for response.
If doesn't exist: Continue.
3. Gather Objective Context
echo "$OBJECTIVE_INFO" | jq -r '.section'
cat .planning/REQUIREMENTS.md 2>/dev/null
cat .planning/objectives/${OBJECTIVE}-*/*-CONTEXT.md 2>/dev/null
grep -A30 "### Decisions Made" .planning/STATE.md 2>/dev/null
Present summary with objective description, requirements, prior decisions.
4. Spawn df-objective-researcher Agent
Research modes: ecosystem (default), feasibility, implementation, comparison.
<research_type>
Objective Research — investigating HOW to implement a specific objective well.
</research_type>
<key_insight>
The question is NOT "which library should I use?"
The question is: "What do I not know that I don't know?"
For this objective, discover:
- What's the established architecture pattern?
- What libraries form the standard stack?
- What problems do people commonly hit?
- What's SOTA vs what Claude's training thinks is SOTA?
- What should NOT be hand-rolled?
</key_insight>
<objective>
Research implementation approach for Objective {objective_number}: {objective_name}
Mode: ecosystem
</objective>
<context>
**Objective description:** {phase_description}
**Requirements:** {requirements_list}
**Prior decisions:** {decisions_if_any}
**Objective context:** {context_md_content}
</context>
<downstream_consumer>
Your RESEARCH.md will be loaded by `/df:plan-objective` which uses specific sections:
- `## Standard Stack` → Plans use these libraries
- `## Architecture Patterns` → Task structure follows these
- `## Don't Hand-Roll` → Tasks NEVER build custom solutions for listed problems
- → Verification steps check for these
→ Task actions reference these patterns
Be prescriptive, not exploratory. "Use X" not "Consider X or Y."
Before declaring complete, verify:
[ ] All domains investigated (not just some)
[ ] Negative claims verified with official docs
[ ] Multiple sources for critical claims
[ ] Confidence levels assigned honestly
[ ] Section names match what plan-objective expects
Write to: .planning/objectives/${OBJECTIVE}-{slug}/${OBJECTIVE}-RESEARCH.md
Task(
prompt="First, read ~/.claude/agents/df-objective-researcher.md for your role and instructions.\n\n" + filled_prompt,
subagent_type="general-purpose",
model="{researcher_model}",
description="Research Objective {objective}"
)
5. Handle Agent Return
## RESEARCH COMPLETE: Display summary, offer: Plan objective, Dig deeper, Review full, Done.
## CHECKPOINT REACHED: Present to user, get response, spawn continuation.
## RESEARCH INCONCLUSIVE: Show what was attempted, offer: Add context, Try different mode, Manual.
6. Spawn Continuation Agent
<objective>
Continue research for Objective {objective_number}: {objective_name}
</objective>
<prior_state>
Research file: @.planning/objectives/${OBJECTIVE}-{slug}/${OBJECTIVE}-RESEARCH.md
</prior_state>
<checkpoint_response>
**Type:** {checkpoint_type}
**Response:** {user_response}
</checkpoint_response>
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}"
)
<success_criteria>
