AgentSkillsCN

deep-research-synthesizer

给定 5 个研究子代理的输出（按时间切片或分区），验证并将其综合为连贯、有引用支持的 Markdown 深度研究报告，具备去重、矛盾处理，以及在输入缺失或格式错误时显式调试可见性。

SKILL.md
--- frontmatter
name: deep-research-synthesizer
description: Given outputs from 5 research sub-agents (time-sliced or partitioned), validate and synthesize them into a coherent, citation-backed Markdown deep research report with deduplication, contradiction handling, and explicit debug visibility when inputs are missing or malformed.
license: MIT
compatibility: Framework-agnostic; assumes you can run 5 sub-agents and collect their text outputs (Markdown with a JSON packet header recommended).
metadata:
  author: github.com/anthony-maio
  version: "1.0"

Deep Research Synthesizer

Use this skill when you have five sub-agent research results and need to produce a single high-quality Markdown report that:

  • •answers the user’s question directly
  • •preserves evidence + citations
  • •removes duplication
  • •flags contradictions and gaps
  • •never “silently fails” when one agent output is missing or malformed

This skill is designed to fix a common anti-pattern: a coordinator prompts 5 agents, then tries to “just synthesize” without a contract, validation, or observability.

Inputs expected (minimum)

  1. •

    user_question — the user’s original question (verbatim)

  2. •

    agent_outputs[5] — one output per agent.
    Each output MUST follow the Research Packet Contract (see references/AGENT_PACKET_CONTRACT.md).

If you cannot guarantee the contract, run scripts/validate_packets.py and repair nonconforming outputs before synthesis.

The 5-agent pattern that works

Step 1 — Partition the work (pick ONE strategy)

Strategy A: Time slicing (good for “what happened” / “how it evolved”)

  • •Agent 1: earliest timeframe
  • •Agent 2: next window
  • •…
  • •Agent 5: most recent window + “what changed recently”

Strategy B: Source slicing (good for broad topics)

  • •Agent 1: primary sources / official docs
  • •Agent 2: academic papers / standards
  • •Agent 3: practitioner writeups
  • •Agent 4: competing viewpoints / critiques
  • •Agent 5: synthesis + “what you should do” recommendations

Strategy C: Sub-question slicing

  • •Agent 1: definitions / scope
  • •Agent 2: current state
  • •Agent 3: failure modes / risks
  • •Agent 4: options + tradeoffs
  • •Agent 5: recommendations + decision framework

Step 2 — Enforce the Research Packet Contract

Every agent must output:

  • •a machine-parseable JSON “packet header”
  • •a short narrative
  • •a “Run Log” with searches + errors

Contract details: references/AGENT_PACKET_CONTRACT.md.

Step 3 — Validate before you synthesize

Run:

bash
python scripts/validate_packets.py --in packets/

Fix failures immediately:

  • •missing sources
  • •missing claims/evidence mapping
  • •missing run logs
  • •malformed JSON

Step 4 — Normalize + deduplicate

Use:

bash
python scripts/merge_packets.py --in packets/ --out merged.json

Then synthesize using the report template:

  • •references/REPORT_TEMPLATE.md
  • •include contradictions + gaps explicitly

Step 5 — Synthesize the final report (Markdown)

Write the report in this order:

  1. •Executive Summary (5–10 bullets)
  2. •Direct Answer (2–6 paragraphs)
  3. •Key Findings (grouped, deduped)
  4. •Evidence & Citations (trace claims → sources)
  5. •Contradictions / Uncertainties (what doesn’t match)
  6. •Recommendations (actionable, prioritized)
  7. •Appendix
    • •per-agent “Run Log”
    • •per-agent “Raw Findings” (optional)

Step 6 — QA checklist (do not skip)

Use references/QA_CHECKLIST.md.

If you can’t pass the checklist due to missing info, output a partial report and explicitly list:

  • •which agents failed
  • •what is missing
  • •what follow-up tasks would resolve it

Failure-handling requirements (what stops “constant failures”)

1) Never hide missing agent outputs

If any agent output is missing/empty:

  • •produce the report anyway
  • •mark the missing agent as “NO DATA”
  • •do not invent what it “would have found”

2) Surface tool errors verbatim

If an agent hit a tool error (HTTP, MCP, API):

  • •carry the raw error into the appendix
  • •summarize the impact in “Limitations”

3) Preserve traceability

Every major claim must have:

  • •source id(s)
  • •agent id
  • •confidence

This avoids “big report looks plausible but can’t be debugged.”

Files in this skill

References

  • •references/AGENT_PACKET_CONTRACT.md — required output format for sub-agents
  • •references/REPORT_TEMPLATE.md — Markdown structure + guidance
  • •references/QA_CHECKLIST.md — deterministic quality gates
  • •references/FAILURE_MODES.md — common causes of synthesis failure + mitigations

Scripts

  • •scripts/validate_packets.py — validates packet conformance and prints actionable errors
  • •scripts/merge_packets.py — merges packets into a single normalized JSON file
  • •scripts/render_report_skeleton.py — generates a Markdown skeleton from merged JSON