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"
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
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: $ARGUMENTS (required)
Normalize objective input in step 1 before any directory lookups.
Present summary with objective description, requirements, prior decisions.
4. Spawn df-objective-researcher Agent
Research modes: ecosystem (default), feasibility, implementation, comparison.
<research_type>
Objective Research — investigating HOW to implement a specific objective well.
</research_type><key_insight>
The question is NOT "which library should I use?"
The question is: "What do I not know that I don't know?"
For this objective, discover:
- What's the established architecture pattern?
- What libraries form the standard stack?
- What problems do people commonly hit?
- What's SOTA vs what Claude's training thinks is SOTA?
- What should NOT be hand-rolled?
</key_insight><objective>
Research implementation approach for Objective {objective_number}: {objective_name}
Mode: ecosystem
</objective><context>**Objective description:** {phase_description}
**Requirements:** {requirements_list}
**Prior decisions:** {decisions_if_any}
**Objective context:** {context_md_content}
</context><downstream_consumer>
Your RESEARCH.md will be loaded by `/df:plan-objective` which uses specific sections:
-`## Standard Stack` → Plans use these libraries
-`## Architecture Patterns` → Task structure follows these
-`## Don't Hand-Roll` → Tasks NEVER build custom solutions for listed problems
-`## Common Pitfalls` → Verification steps check for these
-`## Code Examples` → Task actions reference these patterns
Be prescriptive, not exploratory. "Use X" not "Consider X or Y."
</downstream_consumer><quality_gate>
Before declaring complete, verify:
- [ ] All domains investigated (not just some)
- [ ] Negative claims verified with official docs
- [ ] Multiple sources for critical claims
- [ ] Confidence levels assigned honestly
- [ ] Section names match what plan-objective expects
</quality_gate><output>
Write to: .planning/objectives/${OBJECTIVE}-{slug}/${OBJECTIVE}-RESEARCH.md
</output>
Task(
prompt="First, read ~/.claude/agents/df-objective-researcher.md for your role and instructions.\n\n" + filled_prompt,
subagent_type="general-purpose",
model="{researcher_model}",
description="Research Objective {objective}"
)
5. Handle Agent Return
## RESEARCH COMPLETE: Display summary, offer: Plan objective, Dig deeper, Review full, Done.
## CHECKPOINT REACHED: Present to user, get response, spawn continuation.
## RESEARCH INCONCLUSIVE: Show what was attempted, offer: Add context, Try different mode, Manual.
6. Spawn Continuation Agent
<objective>
Continue research for Objective {objective_number}: {objective_name}
</objective><prior_state>
Research file: @.planning/objectives/${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}"
)
