Deep Research

deep-research

Investigate a question by walking it through three explicit stages with a persisted state file. Use this when a single-pass summarize would lose too much, or when the user asks for a "research report" / "literature review". The host agent does the web fetching; this skill structures the work and keeps a paper trail.

Decide if this is the right tool

Stages

Scope → Plan → Iterate (×N) → Compile → Deliver

State persists in a single JSON file you pass between stages. The file is the contract; if you can describe the file, you can resume the research at any point.

Stage 1: Plan

python {baseDir}/scripts/plan.py \
    --question "How did Manus differentiate from competing AI agents in 2025?" \
    --depth thorough \
    --out plan.json

--depth choices:

• overview — 3-5 sub-questions, target 1 source per sub-question
• thorough — 6-10 sub-questions, target 2-3 sources per sub-question
• exhaustive — 12-20 sub-questions, target 5+ sources per sub-question

The plan is a pydantic model serialized to JSON; see references/methodology.md for the schema and the system-review approach the depth choices implement.

Stage 2: Iterate

Each round: read the plan, decide which sub-questions need attention, print the fetch list for the host agent to execute, and (after the agent returns results) record evidence back into the plan.

# Show the host what to fetch this round
python {baseDir}/scripts/iterate.py --plan plan.json --round 1 --print-fetches

# After the host fetches, record results back
python {baseDir}/scripts/iterate.py --plan plan.json --round 1 \
    --record evidence_round_1.json

evidence_round_1.json:

[
  {
    "subquestion_id": "sq-002",
    "url": "https://...",
    "title": "...",
    "excerpt": "...",
    "relevance": 0.85,
    "fetched_at": "2026-05-06T10:14:00Z"
  }
]

The script updates per-sub-question coverage estimates. When all sub-questions reach the depth-target coverage, the plan's done flag flips to true and the iteration loop terminates.

See references/sources.md for the 5-axis source evaluation (Authority, Recency, Evidence, Bias, Corroboration) you should apply when judging relevance.

Stage 3: Compile

python {baseDir}/scripts/compile.py --plan plan.json --out report.md

Output is markdown with:

1. Executive summary (5-8 lines)
2. Methodology block (depth, rounds, source count)
3. Per-sub-question section with embedded citations [^N]
4. References block listing every source with URL + fetched_at + relevance
5. "What this report does not cover" — explicit gaps from low-coverage sub-questions

Citations link to the references block. The compile step never invents sources — every [^N] in the body must correspond to an entry recorded in stage 2.

Boundaries

• This skill does not fetch the web itself. It is a methodology + state manager. Pair it with the host agent's web search/fetch tools.
• It does not resolve contradictions among sources automatically. The compile step will note conflicting evidence in the report; the user decides which side wins.
• It is not a fact-checker. Source quality scoring is heuristic; treat the output as a starting point, not a verdict.
• For ongoing monitoring (daily digests, RSS-style updates) build a cron skill that calls this one with a fresh question each cycle.

Differentiation from summarize

summarize takes one document and produces a shorter version. This skill takes one question and produces a researched report drawing on many documents, with explicit evidence tracking. They share no trigger words by design — summarize triggers on "summarize", "shorten", "tl;dr"; this skill triggers on "research", "investigate", "literature review", "deep dive".