kata-research-phase

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<objective> Research how to implement a phase. Spawns kata-phase-researcher agent with phase context.

Note: This is a standalone research command. For most workflows, use

/kata-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>

<context> Phase number: $ARGUMENTS (required)

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

<objective> 研究如何实现某个阶段。基于阶段上下文生成kata-phase-researcher Agent。

注意： 这是一个独立的研究命令。对于大多数工作流，请使用

/kata-plan-phase

命令，它会自动集成研究环节。

在以下场景使用此命令：

你希望先开展研究，暂不进行规划
你希望在规划完成后重新开展研究
你需要先调研，再判断某个阶段是否可行

编排器角色： 解析阶段信息，对照路线图验证，检查已有研究，收集上下文，生成研究Agent，呈现结果。

为何使用子Agent： 研究过程会快速消耗上下文（WebSearch、Context7查询、来源验证）。新生成的Agent拥有200k的独立上下文用于调研，主上下文则保持精简，专注于与用户交互。 </objective>

<context> 阶段编号：$ARGUMENTS（必填）

在进行任何目录查找前，第一步先标准化阶段输入。 </context>

0. Resolve Model Profile

0. 解析模型配置文件

Read model profile for agent spawning:

bash

MODEL_PROFILE=$(cat .planning/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")

Default to "balanced" if not set.

Model lookup table:

Agent	quality	balanced	budget
kata-phase-researcher	opus	sonnet	haiku

Store resolved model for use in Task calls below.

读取用于生成Agent的模型配置文件：

bash

MODEL_PROFILE=$(cat .planning/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")

如果未设置，则默认使用“balanced”。

模型查找表：

Agent	quality	balanced	budget
kata-phase-researcher	opus	sonnet	haiku

存储解析后的模型，供后续Task调用使用。

1. Normalize and Validate Phase

1. 标准化并验证阶段信息

bash

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bash

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Normalize phase number (8 → 08, but preserve decimals like 2.1 → 02.1)

标准化阶段编号（8 → 08，但保留小数格式如2.1 → 02.1）

if [[ "$ARGUMENTS" =~ ^[0-9]+$ ]]; then PHASE=$(printf "%02d" "$ARGUMENTS") elif [[ "$ARGUMENTS" =~ ^([0-9]+).([0-9]+)$ ]]; then PHASE=$(printf "%02d.%s" "${BASH_REMATCH[1]}" "${BASH_REMATCH[2]}") else PHASE="$ARGUMENTS" fi

grep -A5 "Phase ${PHASE}:" .planning/ROADMAP.md 2>/dev/null


**If not found:** Error and exit. **If found:** Extract phase number, name, description.

grep -A5 "Phase ${PHASE}:" .planning/ROADMAP.md 2>/dev/null


**如果未找到：** 报错并退出。**如果找到：** 提取阶段编号、名称和描述。

2. Find Phase Directory and Check Existing Research

2. 查找阶段目录并检查已有研究

bash

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bash

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Universal phase discovery

通用阶段发现逻辑

PADDED=$(printf "%02d" "$PHASE" 2>/dev/null || echo "$PHASE") PHASE_DIR="" for state in active pending completed; do PHASE_DIR=$(find .planning/phases/${state} -maxdepth 1 -type d -name "${PADDED}-" 2>/dev/null | head -1) [ -z "$PHASE_DIR" ] && PHASE_DIR=$(find .planning/phases/${state} -maxdepth 1 -type d -name "${PHASE}-" 2>/dev/null | head -1) [ -n "$PHASE_DIR" ] && break done

Fallback: flat directory (backward compatibility)

回退方案：扁平化目录（向后兼容）

if [ -z "$PHASE_DIR" ]; then PHASE_DIR=$(find .planning/phases -maxdepth 1 -type d -name "${PADDED}-" 2>/dev/null | head -1) [ -z "$PHASE_DIR" ] && PHASE_DIR=$(find .planning/phases -maxdepth 1 -type d -name "${PHASE}-" 2>/dev/null | head -1) fi

ls ${PHASE_DIR}/RESEARCH.md 2>/dev/null


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

**If doesn't exist:** Continue.

ls ${PHASE_DIR}/RESEARCH.md 2>/dev/null


**如果已存在：** 提供选项：1) 更新研究内容，2) 查看已有研究，3) 跳过。等待用户响应。

**如果不存在：** 继续执行后续步骤。

3. Gather Phase Context

3. 收集阶段上下文

bash

grep -A20 "Phase ${PHASE}:" .planning/ROADMAP.md
cat .planning/REQUIREMENTS.md 2>/dev/null
cat ${PHASE_DIR}/${PHASE}-CONTEXT.md 2>/dev/null
grep -A30 "### Decisions Made" .planning/STATE.md 2>/dev/null

Present summary with phase description, requirements, prior decisions.

bash

grep -A20 "Phase ${PHASE}:" .planning/ROADMAP.md
cat .planning/REQUIREMENTS.md 2>/dev/null
cat ${PHASE_DIR}/${PHASE}-CONTEXT.md 2>/dev/null
grep -A30 "### Decisions Made" .planning/STATE.md 2>/dev/null

呈现阶段描述、需求、过往决策的汇总信息。

3.5. Load Phase-Researcher Instructions

3.5. 加载阶段研究Agent指令

Read the phase-researcher agent instructions for inlining into Task() calls:

bash

phase_researcher_instructions_content=$(cat references/phase-researcher-instructions.md)

读取phase-researcher Agent的指令，用于嵌入到Task()调用中：

bash

phase_researcher_instructions_content=$(cat references/phase-researcher-instructions.md)

4. Spawn kata-phase-researcher Agent

4. 生成kata-phase-researcher Agent

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

markdown

<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 Claude'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>

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

<downstream_consumer>
Your RESEARCH.md will be loaded by `/kata-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 phase-plan expects
</quality_gate>

<output>
Write to: ${PHASE_DIR}/${PHASE}-RESEARCH.md
</output>

Task(
  prompt="<agent-instructions>\n{phase_researcher_instructions_content}\n</agent-instructions>\n\n" + filled_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Research Phase {phase}"
)

研究模式：生态系统（默认）、可行性、实现方案、对比分析。

markdown

<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 Claude'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>

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

<downstream_consumer>
Your RESEARCH.md will be loaded by `/kata-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 phase-plan expects
</quality_gate>

<output>
Write to: ${PHASE_DIR}/${PHASE}-RESEARCH.md
</output>

Task(
  prompt="<agent-instructions>\n{phase_researcher_instructions_content}\n</agent-instructions>\n\n" + filled_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Research Phase {phase}"
)

5. Handle Agent Return

5. 处理Agent返回结果

## 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.

## RESEARCH COMPLETE
：显示汇总信息，提供选项：规划阶段、深入调研、查看完整内容、结束。

## CHECKPOINT REACHED
：将内容呈现给用户，获取响应后生成后续Agent。

## RESEARCH INCONCLUSIVE
：展示已尝试的操作，提供选项：补充上下文、更换研究模式、手动处理。

6. Spawn Continuation Agent

6. 生成后续Agent

markdown

<objective>
Continue research for Phase {phase_number}: {phase_name}
</objective>

<prior_state>
Research file: @${PHASE_DIR}/${PHASE}-RESEARCH.md
</prior_state>

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

Task(
  prompt="<agent-instructions>\n{phase_researcher_instructions_content}\n</agent-instructions>\n\n" + continuation_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Continue research Phase {phase}"
)

</process>

<success_criteria>

Phase validated against roadmap
Existing research checked
kata-phase-researcher spawned with context
Checkpoints handled correctly
User knows next steps </success_criteria>

markdown

<objective>
Continue research for Phase {phase_number}: {phase_name}
</objective>

<prior_state>
Research file: @${PHASE_DIR}/${PHASE}-RESEARCH.md
</prior_state>

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

Task(
  prompt="<agent-instructions>\n{phase_researcher_instructions_content}\n</agent-instructions>\n\n" + continuation_prompt,
  subagent_type="general-purpose",
  model="{researcher_model}",
  description="Continue research Phase {phase}"
)

</process>

<success_criteria>

阶段信息已对照路线图验证
已检查已有研究内容
已基于上下文生成kata-phase-researcher Agent
检查点已正确处理
用户了解后续操作步骤 </success_criteria>