deep-research

name	deep-research
description	Open-ended deep research with repeated broadening/deepening loops, explicit reframing, falsification, and hypothesis/contradiction tracking to produce a sourced research narrative. Use when the user asks for open-ended research, exploratory investigation, "deep research", "开放式研究", "做假设/证伪/找反例", or wants iterative broadening and deepening until insight stabilizes.

Deep Research

Overview

Use this skill to run open-ended research as an iterative program rather than a one-off lookup: repeatedly broaden the search space, deepen via primary-source verification, reframe the question as understanding improves, and actively stress-test conclusions with counterevidence.

This skill is intentionally heavier than a normal search workflow. It requires maintaining living artifacts (plan, ledger, hypotheses, contradictions, and decisions) so the investigation stays auditable and does not collapse into ad hoc intuition.

How this differs from iterative-web-research

• iterative-web-research is optimized for sourced web research: broad-to-deep search, verify key claims, then write a report.
• deep-research is optimized for open-ended research where:
  • the best framing is unknown at the start,
  • competing explanations exist,
  • contradictions and boundary conditions matter,
  • you must iterate until insight stabilizes (not just until you have “some sources”).

Use This Workflow

Use this skill when the request has most of these properties:

• The question is ambiguous, ill-posed, or likely to need reframing.
• The user wants an exploratory investigation rather than a quick answer.
• Multiple competing explanations/hypotheses are plausible.
• Contradictions, caveats, and boundary conditions are central to correctness.
• The investigation needs multiple loops of:
  • broadening (new terms/adjacent fields/benchmarks)
  • deepening (fetch + verify)
  • reframing (split/merge questions)
  • falsification (actively seek counterevidence)

Do not use this skill for simple single-fact lookups, casual chat, or tasks that can be answered from local repo context alone.

Required Intermediate Artifacts

Create working copies from templates/ and maintain them throughout the research. Update them after each loop.

1. research-plan.md — evolving question backlog (priority, status, next actions, mapping to hypotheses)
2. evidence-ledger.md — audited source ledger (verification state, settings/conditions, findings, caveats)
3. iteration-log.md — wave-by-wave narrative (what changed, what was learned, next loop)
4. term-map.md — user terms to research terms, synonyms, definitions, query expansions
5. hypothesis-map.md — competing hypotheses, predictions, evidence for/against, confidence, next tests
6. contradiction-matrix.md — conflicting findings, differing conditions, reconciliation status
7. decision-log.md — explicit reframing decisions (split/merge/drop questions, retire hypotheses, stop decisions)

Do not keep the evolving framing only in working memory.

Workflow

1. Ground the search toolchain

Start with the WebSearch MCP flow:

1. Run mcp__websearch__doctor
2. Run mcp__websearch__get_usage_guide
3. Run mcp__websearch__recommend_command with the actual research task
4. Execute the recommended uv run --project ... web-search-cli ... command from shell

Prefer the MCP-guided repository workflow over ad hoc search.

2. Initialize the research workspace

Create a working copy of the 7 templates and fill in:

• research date
• topic/title
• requested output format and audience
• constraints/budget (time, number of loops, or must-cover sources)
• current framing (initial)
• key unknowns and risks

3. Build an initial framing and term map

Before searching, build an initial “framing” that makes the problem researchable:

• define the key terms the user used (in term-map.md)
• list 5–15 research synonyms/keywords per key term
• identify adjacent communities/keywords that might use different language
• write 2–4 alternative framings (ways to ask the question)
• select one as Current framing and record the choice in decision-log.md

4. Create a living research plan

Translate the current framing into a research plan. For each research question/claim, record:

• why it matters
• priority: high / medium / low
• what evidence is needed
• which hypothesis IDs it tests (if applicable)
• current status and next action

Recommended statuses:

• Open
• Searching
• Partially answered
• Supported
• Refuted
• Inconclusive but bounded
• Deprioritized

5. Seed competing hypotheses

Create 2–6 competing hypotheses in hypothesis-map.md.

For each hypothesis, write:

• a clear statement
• 2–5 predictions or observable implications
• what evidence would falsify it

Avoid single-hypothesis tunnel vision.

6. Run the Deep Research loop (repeat)

Repeat the loop below until the stop criteria are met.

Loop A — Broadening (discovery)

Broaden the search space based on the current plan and term map:

• expand synonyms, alternative terms, benchmark names, and adjacent fields
• search multiple facets in parallel (core claims, mechanisms, mitigations, boundaries)
• treat search output as source discovery, not final evidence

Loop B — Deepening (verification)

Fetch and verify the highest-value sources before updating conclusions:

• prefer original papers, official technical reports, official benchmarks/leaderboards, and official repos
• extract: task setup, metrics, conditions, quantitative results, and limitations
• record in evidence-ledger.md with Fetched and Verified kept explicit

Loop C — Reframing (plan and hypothesis updates)

Update the artifacts before starting the next loop:

• update question statuses and next actions in research-plan.md
• add/rename terms in term-map.md as you learn the field’s real vocabulary
• update hypothesis confidence and status in hypothesis-map.md
• record major reframes as explicit decisions in decision-log.md

Common reframes:

• split one vague question into 2–4 narrower questions
• replace user language with the field’s canonical terms
• separate “direct evidence” from “strong analogy” from “inference”

Loop D — Falsification / stress-testing

Actively search for counterevidence and contradictions:

• search for negative results, limitations, failure modes, and “does not hold when…”
• look for alternative mechanisms that explain the same observations
• populate contradiction-matrix.md and attempt reconciliation via conditions

Loop bookkeeping

After each loop, append to iteration-log.md:

• what you searched (and why)
• what you fetched/verified
• what changed in the framing/hypotheses
• remaining gaps and the next loop plan

7. Stop Criteria (insight plateau, not wave count)

Stop the loop only when the investigation reaches an insight plateau under the current constraints.

Minimum stop gate (all must hold):

1. Evidence sufficiency: each high-priority conclusion is supported/refuted/inconclusive-bounded with verified sources and logged caveats.
2. Falsification sufficiency: major competing hypotheses have been stress-tested; counterevidence has been actively sought.
3. Framing stability: the framing is stable (no major reframe in the most recent loop), or reframes have converged.
4. Contradictions handled: key contradictions are reconciled by conditions, or explicitly marked unresolved with next checks.
5. Diminishing returns: the most recent broadening pass yields mostly duplicates/low-value sources, and new sources no longer change the hypothesis map materially.

If you must stop due to budget limits, explicitly include:

• what remains unknown
• what evidence is still missing
• what would change your mind

Do not treat “two waves” as automatically sufficient.

Writing the Final Markdown Research Report

Write the report from the artifacts (not from memory). Use this structure by default:

# <clear title>

> Research date: YYYY-MM-DD
> Scope: <1-2 lines>
> Constraints: <if any>

## Executive Summary
- 5-10 bullets with confidence labels

## Current Framing and Definitions
- Key definitions (from term-map)
- Why this framing (from decision-log)

## Hypotheses (and where they stand)
- H1: ...
- H2: ...

## Findings
### 1) <theme or question>
...
Evidence: ...

## Contradictions and Boundary Conditions
- What appears to conflict
- How (or whether) it reconciles

## What remains unknown (and what would change my mind)
- Unknowns:
- Missing evidence:
- If we found X, we would update conclusion Y

## Key Sources
- [Title](URL) — why it matters

Reporting Rules

Make evidence status explicit

Use labels like:

• [Strong evidence] multiple direct primary sources
• [Moderate evidence] one direct source plus supporting mechanism or survey
• [Inference] reasonable synthesis, not directly tested
• [Newer result] useful but not yet broadly replicated
• [Lower weight] withdrawn, informal, or otherwise weaker source

Ensure labels match evidence-ledger.md and the contradiction matrix.

Separate direct evidence from analogy

If direct experiments for the exact question are scarce, say so explicitly, then use nearby evidence with labels:

• “directly shown”
• “supported by adjacent evidence”
• “inferred from related domains”

Cite important claims inline

For any nontrivial claim, include a link in the same paragraph or bullet.

State dates concretely

Use absolute dates for recent papers or reports when time context matters.

Search Prompt Patterns

Keep search prompts narrow and targeted

Do not dump the entire research task into the search or retrieval model and ask it to solve the whole problem in one prompt.

Instead:

• use search prompts for source discovery, claim checking, or gap filling
• keep each prompt focused on one question, claim, mechanism, benchmark, or evidence gap
• break large research tasks into multiple small prompts aligned with the research plan
• save synthesis and judgment for after sources have been fetched and verified

Breadth prompt

Find primary sources on <topic>. Prioritize original papers, arXiv pages, conference pages, official lab blogs, technical reports, benchmarks, and official repositories. Return grouped source links with 1-line relevance notes.

Falsification prompt

Find counterevidence, limitations, negative results, or failure cases for <specific claim>. Prioritize primary sources and benchmark papers that show when the claim does not hold.

Term-expansion prompt

List research synonyms and canonical terms used by the field for <user term>. Provide 10-20 keywords and 3-5 related benchmark or dataset names to use in follow-up searches.

Common Failure Modes

Avoid these mistakes:

• Lock onto the first framing and stop reframing
• Stop after one or two waves without checking stop criteria
• Dump the entire research brief into one giant search prompt and treat the retrieval model as the final analyst
• Avoid counterevidence and never run falsification searches
• Mix direct evidence and analogy without labeling
• Update conclusions without fetching/verifying the most valuable sources
• Keep hypotheses implicit rather than tracked

Minimal Execution Checklist

• Run doctor / usage guide / recommend command
• Initialize the 7 intermediate artifacts from templates
• Build an initial framing and term map
• Seed 2–6 competing hypotheses
• Run repeated loops: broadening → deepening → reframing → falsification
• Stop only when the stop criteria are satisfied or the budget is exhausted
• Draft the final report from the maintained artifacts