Knowledge Synthesis

Quick Summary

Goal: Produce a fully-cited, template-compliant research report by synthesizing the evidence base using the enforced template — whose confidence scores and gaps are honest enough to trust for decisions.

Workflow:

1. Load evidence — Read evidence base from deep-research
2. Load template — Read enforced template from .claude/​templates/
3. Synthesize — Write report following template structure
4. Citation check — Verify every claim has citation
5. Confidence summary — Aggregate scores, flag gaps

Key Rules:

• MUST ATTENTION use enforced template structure — all sections required
• Every factual claim inline-cited: [N] referencing source table
• Knowledge gaps section mandatory

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Knowledge Synthesis

Step 1: Load Evidence

Read .claude/​tmp/​_evidence-{slug}.md and .claude/​tmp/​_sources-{slug}.md.

Inventory:

• Total findings with confidence scores
• Unresolved discrepancies
• Remaining gaps

Step 2: Load Template

Read enforced template: .claude/​templates/​research-report-template.md

Every template section MUST ATTENTION appear in final report.

Step 3: Synthesize Report

Write to docs/​knowledge/​research/​{slug}.md. For each template section:

1. Map relevant findings from evidence base
2. Write content with inline citations [N]
3. Declare confidence per finding
4. Note cross-cutting patterns and contradictions in Analysis section

Step 4: Citation Audit

Verify:

• Every factual claim has 1+ [N] citation
• Every source in Sources table referenced 1+ time
• No orphan citations (referencing non-existent source)

Step 5: Confidence Summary

Calculate overall report confidence:

• Average of all finding confidence scores
• Weight by finding importance
• Flag any <60% findings prominently

Output

Final report: docs/​knowledge/​research/​{descriptive-slug}.md

Clean up working files from .claude/​tmp/ after successful synthesis.

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting.

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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. Keep domain concepts out of generic/​shared/​infrastructure layers. A reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/​customer/​product IDs, business entities, feature rules. The leak compiles and runs, so it passes review silently while coupling the "reusable" layer to one consumer. Push domain fields/​logic down into the consumer via subclass or composition.

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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.

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MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

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MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

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Closing Reminders

IMPORTANT MUST ATTENTION Goal: Produce a fully-cited, template-compliant research report whose confidence scores and gaps are honest enough to trust for decisions. IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting IMPORTANT MUST ATTENTION use enforced template structure — every section required, NEVER omit Knowledge Gaps — why: a missing gaps section creates false confidence in incomplete research IMPORTANT MUST ATTENTION inline-cite every factual claim with [N]; verify no orphan citations and no orphan sources — why: uncited claims are assertions, not findings IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act) IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

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