gsd-research-phase
Conducts research to support development planning.
Install
mkdir -p .claude/skills/gsd-research-phase && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12682" && unzip -o skill.zip -d .claude/skills/gsd-research-phase && rm skill.zipInstalls to .claude/skills/gsd-research-phase
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Research how to implement a phase (standalone - usually use /gsd-plan-phase instead)Key capabilities
- →Initialize context from GSD tools
- →Validate phase against roadmap
- →Check for existing research
- →Gather phase context from various paths
- →Spawn `gsd-phase-researcher` agent
- →Handle agent return and checkpoints
How it works
The skill initializes context, validates the phase, checks for prior research, gathers relevant documents, and then spawns a `gsd-phase-researcher` agent to conduct the actual research.
Inputs & outputs
When to use gsd-research-phase
- →Researching how to implement a new feature
- →Gathering technical requirements for a phase
- →Exploratory research before planning
About this skill
<codex_skill_adapter>
A. Skill Invocation
- This skill is invoked by mentioning
$gsd-research-phase. - Treat all user text after
$gsd-research-phaseas{{GSD_ARGS}}. - If no arguments are present, treat
{{GSD_ARGS}}as empty.
B. AskUserQuestion → request_user_input Mapping
GSD workflows use AskUserQuestion (Claude Code syntax). Translate to Codex request_user_input:
Parameter mapping:
header→headerquestion→question- Options formatted as
"Label" — description→{label: "Label", description: "description"} - Generate
idfrom header: lowercase, replace spaces with underscores
Batched calls:
AskUserQuestion([q1, q2])→ singlerequest_user_inputwith multiple entries inquestions[]
Multi-select workaround:
- Codex has no
multiSelect. Use sequential single-selects, or present a numbered freeform list asking the user to enter comma-separated numbers.
Execute mode fallback:
- When
request_user_inputis rejected (Execute mode), present a plain-text numbered list and pick a reasonable default.
C. Task() → spawn_agent Mapping
GSD workflows use Task(...) (Claude Code syntax). Translate to Codex collaboration tools:
Direct mapping:
Task(subagent_type="X", prompt="Y")→spawn_agent(agent_type="X", message="Y")Task(model="...")→ omit (Codex uses per-role config, not inline model selection)fork_context: falseby default — GSD agents load their own context via<files_to_read>blocks
Parallel fan-out:
- Spawn multiple agents → collect agent IDs →
wait(ids)for all to complete
Result parsing:
- Look for structured markers in agent output:
CHECKPOINT,PLAN COMPLETE,SUMMARY, etc. close_agent(id)after collecting results from each agent </codex_skill_adapter>
Note: This is a standalone research command. For most workflows, use /gsd-plan-phase 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 a phase is feasible
Orchestrator role: Parse phase, 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>
<available_agent_types> Valid GSD subagent types (use exact names — do not fall back to 'general-purpose'):
- gsd-phase-researcher — Researches technical approaches for a phase </available_agent_types>
Normalize phase input in step 1 before any directory lookups. </context>
<process>0. Initialize Context
INIT=$(node "/Users/wilderherrera/Desktop/convision/.codex/get-shit-done/bin/gsd-tools.cjs" init phase-op "{{GSD_ARGS}}")
if [[ "$INIT" == @file:* ]]; then INIT=$(cat "${INIT#@file:}"); fi
Extract from init JSON: phase_dir, phase_number, phase_name, phase_found, commit_docs, has_research, state_path, requirements_path, context_path, research_path.
Resolve researcher model:
RESEARCHER_MODEL=$(node "/Users/wilderherrera/Desktop/convision/.codex/get-shit-done/bin/gsd-tools.cjs" resolve-model gsd-phase-researcher --raw)
1. Validate Phase
PHASE_INFO=$(node "/Users/wilderherrera/Desktop/convision/.codex/get-shit-done/bin/gsd-tools.cjs" roadmap get-phase "${phase_number}")
If found is false: Error and exit. If found is true: Extract phase_number, phase_name, goal from JSON.
2. Check Existing Research
ls .planning/phases/${PHASE}-*/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 Phase Context
Use paths from INIT (do not inline file contents in orchestrator context):
requirements_pathcontext_pathstate_path
Present summary with phase description and what files the researcher will load.
4. Spawn gsd-phase-researcher Agent
Research modes: ecosystem (default), feasibility, implementation, comparison.
<research_type>
Phase Research — investigating HOW to implement a specific phase 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 phase, discover:
- What's the established architecture pattern?
- What libraries form the standard stack?
- What problems do people commonly hit?
- What's SOTA vs what the agent's training thinks is SOTA?
- What should NOT be hand-rolled?
</key_insight>
<objective>
Research implementation approach for Phase {phase_number}: {phase_name}
Mode: ecosystem
</objective>
<files_to_read>
- {requirements_path} (Requirements)
- {context_path} (Phase context from discuss-phase, if exists)
- {state_path} (Prior project decisions and blockers)
</files_to_read>
<additional_context>
**Phase description:** {phase_description}
</additional_context>
<downstream_consumer>
Your RESEARCH.md will be loaded by `/gsd-plan-phase` 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-phase expects
</quality_gate>
<output>
Write to: .planning/phases/${PHASE}-{slug}/${PHASE}-RESEARCH.md
</output>
Task(
prompt=filled_prompt,
subagent_type="gsd-phase-researcher",
model="{researcher_model}",
description="Research Phase {phase}"
)
5. Handle Agent Return
## RESEARCH COMPLETE: Display summary, offer: Plan phase, Dig deeper, Review full, Done.
## CHECKPOINT REACHED: Present to user, get response, spawn continuation.
## RESEARCH INCONCLUSIVE: Show what was attempted, offer: Add context, Try different mode, Manual.
6. Spawn Continuation Agent
<objective>
Continue research for Phase {phase_number}: {phase_name}
</objective>
<prior_state>
<files_to_read>
- .planning/phases/${PHASE}-{slug}/${PHASE}-RESEARCH.md (Existing research)
</files_to_read>
</prior_state>
<checkpoint_response>
**Type:** {checkpoint_type}
**Response:** {user_response}
</checkpoint_response>
Task(
prompt=continuation_prompt,
subagent_type="gsd-phase-researcher",
model="{researcher_model}",
description="Continue research Phase {phase}"
)
</process>
<success_criteria>
- Phase validated against roadmap
- Existing research checked
- gsd-phase-researcher spawned with context
- Checkpoints handled correctly
- User knows next steps </success_criteria>
When not to use it
- →When planning is the primary goal (use `/gsd-plan-phase` instead)
- →When not needing to investigate feasibility
Limitations
- →Requires a phase number
- →Requires explicit user interaction for multi-select questions
How it compares
This skill provides a dedicated, context-aware research process for a development phase by spawning a specialized sub-agent, offering a focused investigation beyond a general search.
Compared to similar skills
gsd-research-phase side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| gsd-research-phase (this skill) | 0 | 3mo | Review | Advanced |
| meta-pattern-recognition | 10 | 9mo | No flags | Intermediate |
| research-engineer | 2 | 6mo | No flags | Advanced |
| tech-decision | 1 | 7mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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