workflow-research（工作流程研究）

IMPORTANT MANDATORY Steps: /web-research -> /deep-research -> /knowledge-synthesis -> /knowledge-review -> /workflow-end

[BLOCKING] Each step MUST ATTENTION invoke its Skill tool — marking a task completed without skill invocation is a workflow violation. NEVER batch-complete validation gates.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task: - Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. - Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. - Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. - Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. - When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. - Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. - Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. - Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. - Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. - Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Incremental Result Persistence — MANDATORY for all sub-agents or heavy inline steps processing >3 files. 1. Before starting: Create report file plans/reports/{skill}-{date}-{slug}.md 2. After each file/section reviewed: Append findings to report immediately — never hold in memory 3. Return to main agent: Summary only (per SYNC:subagent-return-contract) with Full report: path 4. Main agent: Reads report file only when resolving specific blockers Why: Context cutoff mid-execution loses ALL in-memory findings. Each disk write survives compaction. Partial results are better than no results. Report naming: plans/reports/{skill-name}-{YYMMDD}-{HHmm}-{slug}.md

Sub-Agent Return Contract — When this skill spawns a sub-agent, the sub-agent MUST return ONLY this structure. Main agent reads only this summary — NEVER requests full sub-agent output inline. ``markdown ## Sub-Agent Result: [skill-name] Status: ✅ PASS | ⚠️ PARTIAL | ❌ FAIL Confidence: [0-100]% ### Findings (Critical/High only — max 10 bullets) - [severity] [file:line] [finding] ### Actions Taken - [file changed] [what changed] ### Blockers (if any) - [blocker description] Full report: plans/reports/[skill-name]-[date]-[slug].md ` Main agent reads Full report` file ONLY when: (a) resolving a specific blocker, or (b) building a fix plan. Sub-agent writes full report incrementally (per SYNC:incremental-persistence) — not held in memory.

Activate the research workflow. Run /workflow-start research with the user's prompt as context.

Steps: /web-research → /deep-research → /knowledge-synthesis → /knowledge-review → /workflow-end

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

IMPORTANT MANDATORY Steps: /web-research -> /deep-research -> /knowledge-synthesis -> /knowledge-review -> /workflow-end