deep-research

# Deep Research — Disciplined Meta-Research

Turn "research this topic" into an auditable, reusable investigation instead of a one-shot wall of text. The output is a folder you can return to in a month: every claim traces to a specific source file, the plan documents *why* each choice was made, and a refresh protocol lets you update it later without re-running everything.

**This is the heavy, methodical end of research.** It is not a fast overview — it is the workflow you reach for when getting the answer *wrong* costs more than the tokens spent getting it right.

## How it differs from a quick research router

A router-style research skill (keyword-classify → delegate → short sequential search → markdown brief) is optimal when you need an answer fast and the decision risk is low. `deep-research` is the opposite trade: it pays for rigor. Use it when the answer feeds a strategy, an irreversible decision, a published artifact, or a hypothesis you need to actually test — situations where a shallow fallback would be a liability.

Concretely, `deep-research` adds what a fast overview does not: falsifiable hypotheses up front, parallel sub-agent fan-out across many channels, triangulation with explicit source-type diversity, a mandatory adversarial pass, per-source files with verbatim quotes, and a `refresh_targets.md` for delta-updates later.

## The pipeline (9 phases)

Depth scales with the task — `shallow` runs the core phases inline; `medium`/`deep` add capability discovery, verification, and refresh targets.

| # | Phase | What it does |
|---|-------|--------------|
| 1 | **Reframe** | Rewrite the question, fix the underlying decision, state 2–4 *falsifiable* hypotheses |
| 2 | **Genre & blocks** | Pick the report genre (qa / explainer / decision / landscape / validation / custom) and its building blocks |
| 3 | **Plan** | Write `plan.md`: scope, structure, sourcing strategy, opposition queries, risk register, stop-criteria |
| 3.5 | **Capability discovery** | Audit available API keys/channels in the environment; map subtopics to sources; fall back to HTML where needed |
| 4 | **Search** (loop) | Dispatch sources → launch sub-agents in parallel → fetch & dedup → save each to `sources/NN.md`; re-evaluate between rounds |
| 5 | **Score & triangulate** | Rate every source on Credibility / Recency / Bias; require ≥3 independent, differently-typed sources per thesis |
| 6 | **Synthesize + adversarial** | Assemble the report from blocks, run 4 self-critique questions, add steel-manned counter-arguments |
| 6.5 | **Verify** | Lightweight citation check before closing |
| 7 | **Refresh targets** | Extract entities / numbers / hypotheses into `refresh_targets.md` — the entry point for future updates |

## Core mechanisms

These are what separate a documented investigation from a confident guess:

- **Triangulation.** Every thesis must be backed by ≥3 independent sources of *different types* (primary / academic / industry / discussion). A claim with fewer is flagged "insufficient evidence," not stated as fact.
- **Source-grounding.** Each source becomes its own `sources/NN_slug.md` with metadata, verbatim quotes, and scores. No dangling claim — every assertion links back to a specific file. An empty fetch produces an empty claim, never a fabricated citation.
- **Adversarial pass.** Phase 6 always runs the strongest available reasoning: 4 self-critique questions plus an active search for counter-arguments and disconfirming evidence.
- **Falsifiable hypotheses.** Phase 1 commits to 2–4 hypotheses; Phases 5–6 explicitly confirm or refute each against the evidence, or mark it under-determined.
- **Parallel sub-agents.** Phase 4 launches search sub-agents concurrently (cheap models for broad web sweeps, stronger ones for reasoning-heavy subtopics) — never one-at-a-time.
- **Refresh protocol.** Phase 7 emits `refresh_targets.md`; an `update <slug>` run produces a delta (new entrants, entity changes, refreshed numbers, adversarial triggers) instead of replaying the whole investigation.
- **Atomic findings.** Reusable theses in `findings/FN.md` plus a `sources.csv` index — research compounds across questions instead of starting from zero each time.

## Output structure

```
<root>/<slug>/
├── plan.md                  # scope, sourcing strategy, risk register, changelog
├── sources.csv              # index of every source with scores
├── sources/
│   ├── 01_<slug>.md         # one file = one source (metadata + verbatim quotes)
│   └── ...
├── findings/                # atomic, reusable theses (larger investigations)
│   └── F1_<short>.md
├── refresh_targets.md       # what to watch on update (medium/deep)
├── diffs/
│   └── YYYY-MM-DD_delta.md   # delta from an `update <slug>` run
└── YYYY-MM-DD_<genre>.md     # final report
```

## When to use

- A low-quality answer is expensive: strategy, business plan, report, or article groundwork.
- Comparing N institutions, products, methodologies, or markets and you need defensible reasoning.
- Validating a hypothesis or a decision against external data.
- Meta-research: "understand how X works," "map the landscape of Y," answering a connected series of questions.

## Anti-Patterns

- **Don't skip the existing-work check.** Before searching, see whether the answer is already in the project or in a prior research folder — you risk re-researching something you already have.
- **Don't skip reframing**, even when the request "seems clear." The decision behind the question usually changes the search.
- **Don't output to chat only.** Always persist sources and the report to files — the reuse value is in the folder, not the transcript.
- **Don't fabricate citations.** If a fetch returns nothing, the claim is empty — never invent a plausible URL. Bind every claim to a saved verbatim quote.
- **Don't build conclusions on a thin corpus.** Too few sources, or sources that all share one type, means triangulation hasn't happened — say so rather than overstating confidence.
- **Don't skip the adversarial pass** on medium/deep investigations. Confirmation-only research is the failure mode this skill exists to prevent.
- **Don't run sub-agents sequentially.** Fan-out in parallel; serial search wastes the wall-clock advantage.
- **Don't collapse `sources/` into one file.** Per-source files are what make findings searchable and reusable across investigations.
- **Don't pick the heaviest model for everything.** Match model to subtask — cheap for broad sweeps, strong for synthesis and the adversarial pass.

## Cross-References

- **research router** — for fast topic overviews where decision risk is low; `deep-research` is the heavyweight alternative when rigor matters more than speed.
- **competitive-teardown** — for comparing N competitors on a structured 12-dimension matrix.
- **litreview / dossier / patent** — domain specialists when the investigation is narrowly academic, person/company-focused, or patent-focused.

{
  "format": "aiskill/v1",
  "skill": {
    "name": "deep-research",
    "slug": "deep-research",
    "version": "1.0.0",
    "description": "Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel sub-agents, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work, comparing N products/methods/markets, validating a hypothesis with external data, or mapping how a field works. NOT for quick fact-checks (answer directly), structured 12-dimension competitor scoring (use competitive-teardown), or fast topic overviews where the decision risk is low (use the research router instead).",
    "category": "Writing",
    "tags": [
      "writing-content",
      "agents",
      "research"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# Deep Research — Disciplined Meta-Research\r\n\r\nTurn \"research this topic\" into an auditable, reusable investigation instead of a one-shot wall of text. The output is a folder you can return to in a month: every claim traces to a specific source file, the plan documents *why* each choice was made, and a refresh protocol lets you update it later without re-running everything.\r\n\r\n**This is the heavy, methodical end of research.** It is not a fast overview — it is the workflow you reach for when getting the answer *wrong* costs more than the tokens spent getting it right.\r\n\r\n## How it differs from a quick research router\r\n\r\nA router-style research skill (keyword-classify → delegate → short sequential search → markdown brief) is optimal when you need an answer fast and the decision risk is low. `deep-research` is the opposite trade: it pays for rigor. Use it when the answer feeds a strategy, an irreversible decision, a published artifact, or a hypothesis you need to actually test — situations where a shallow fallback would be a liability.\r\n\r\nConcretely, `deep-research` adds what a fast overview does not: falsifiable hypotheses up front, parallel sub-agent fan-out across many channels, triangulation with explicit source-type diversity, a mandatory adversarial pass, per-source files with verbatim quotes, and a `refresh_targets.md` for delta-updates later.\r\n\r\n## The pipeline (9 phases)\r\n\r\nDepth scales with the task — `shallow` runs the core phases inline; `medium`/`deep` add capability discovery, verification, and refresh targets.\r\n\r\n| # | Phase | What it does |\r\n|---|-------|--------------|\r\n| 1 | **Reframe** | Rewrite the question, fix the underlying decision, state 2–4 *falsifiable* hypotheses |\r\n| 2 | **Genre & blocks** | Pick the report genre (qa / explainer / decision / landscape / validation / custom) and its building blocks |\r\n| 3 | **Plan** | Write `plan.md`: scope, structure, sourcing strategy, opposition queries, risk register, stop-criteria |\r\n| 3.5 | **Capability discovery** | Audit available API keys/channels in the environment; map subtopics to sources; fall back to HTML where needed |\r\n| 4 | **Search** (loop) | Dispatch sources → launch sub-agents in parallel → fetch & dedup → save each to `sources/NN.md`; re-evaluate between rounds |\r\n| 5 | **Score & triangulate** | Rate every source on Credibility / Recency / Bias; require ≥3 independent, differently-typed sources per thesis |\r\n| 6 | **Synthesize + adversarial** | Assemble the report from blocks, run 4 self-critique questions, add steel-manned counter-arguments |\r\n| 6.5 | **Verify** | Lightweight citation check before closing |\r\n| 7 | **Refresh targets** | Extract entities / numbers / hypotheses into `refresh_targets.md` — the entry point for future updates |\r\n\r\n## Core mechanisms\r\n\r\nThese are what separate a documented investigation from a confident guess:\r\n\r\n- **Triangulation.** Every thesis must be backed by ≥3 independent sources of *different types* (primary / academic / industry / discussion). A claim with fewer is flagged \"insufficient evidence,\" not stated as fact.\r\n- **Source-grounding.** Each source becomes its own `sources/NN_slug.md` with metadata, verbatim quotes, and scores. No dangling claim — every assertion links back to a specific file. An empty fetch produces an empty claim, never a fabricated citation.\r\n- **Adversarial pass.** Phase 6 always runs the strongest available reasoning: 4 self-critique questions plus an active search for counter-arguments and disconfirming evidence.\r\n- **Falsifiable hypotheses.** Phase 1 commits to 2–4 hypotheses; Phases 5–6 explicitly confirm or refute each against the evidence, or mark it under-determined.\r\n- **Parallel sub-agents.** Phase 4 launches search sub-agents concurrently (cheap models for broad web sweeps, stronger ones for reasoning-heavy subtopics) — never one-at-a-time.\r\n- **Refresh protocol.** Phase 7 emits `refresh_targets.md`; an `update <slug>` run produces a delta (new entrants, entity changes, refreshed numbers, adversarial triggers) instead of replaying the whole investigation.\r\n- **Atomic findings.** Reusable theses in `findings/FN.md` plus a `sources.csv` index — research compounds across questions instead of starting from zero each time.\r\n\r\n## Output structure\r\n\r\n```\r\n<root>/<slug>/\r\n├── plan.md                  # scope, sourcing strategy, risk register, changelog\r\n├── sources.csv              # index of every source with scores\r\n├── sources/\r\n│   ├── 01_<slug>.md         # one file = one source (metadata + verbatim quotes)\r\n│   └── ...\r\n├── findings/                # atomic, reusable theses (larger investigations)\r\n│   └── F1_<short>.md\r\n├── refresh_targets.md       # what to watch on update (medium/deep)\r\n├── diffs/\r\n│   └── YYYY-MM-DD_delta.md   # delta from an `update <slug>` run\r\n└── YYYY-MM-DD_<genre>.md     # final report\r\n```\r\n\r\n## When to use\r\n\r\n- A low-quality answer is expensive: strategy, business plan, report, or article groundwork.\r\n- Comparing N institutions, products, methodologies, or markets and you need defensible reasoning.\r\n- Validating a hypothesis or a decision against external data.\r\n- Meta-research: \"understand how X works,\" \"map the landscape of Y,\" answering a connected series of questions.\r\n\r\n## Anti-Patterns\r\n\r\n- **Don't skip the existing-work check.** Before searching, see whether the answer is already in the project or in a prior research folder — you risk re-researching something you already have.\r\n- **Don't skip reframing**, even when the request \"seems clear.\" The decision behind the question usually changes the search.\r\n- **Don't output to chat only.** Always persist sources and the report to files — the reuse value is in the folder, not the transcript.\r\n- **Don't fabricate citations.** If a fetch returns nothing, the claim is empty — never invent a plausible URL. Bind every claim to a saved verbatim quote.\r\n- **Don't build conclusions on a thin corpus.** Too few sources, or sources that all share one type, means triangulation hasn't happened — say so rather than overstating confidence.\r\n- **Don't skip the adversarial pass** on medium/deep investigations. Confirmation-only research is the failure mode this skill exists to prevent.\r\n- **Don't run sub-agents sequentially.** Fan-out in parallel; serial search wastes the wall-clock advantage.\r\n- **Don't collapse `sources/` into one file.** Per-source files are what make findings searchable and reusable across investigations.\r\n- **Don't pick the heaviest model for everything.** Match model to subtask — cheap for broad sweeps, strong for synthesis and the adversarial pass.\r\n\r\n## Cross-References\r\n\r\n- **research router** — for fast topic overviews where decision risk is low; `deep-research` is the heavyweight alternative when rigor matters more than speed.\r\n- **competitive-teardown** — for comparing N competitors on a structured 12-dimension matrix.\r\n- **litreview / dossier / patent** — domain specialists when the investigation is narrowly academic, person/company-focused, or patent-focused.",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The deep-research/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/deep-research",
    "exported_at": "2026-11-03T07:43:36.699Z"
  }
}

format: aiskill/v1
skill:
  name: deep-research
  slug: deep-research
  version: 1.0.0
  description: >-
    Run a disciplined, multi-source research investigation for a high-stakes question or decision —
    fan-out web search across many channels, parallel sub-agents, source triangulation (each claim
    backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own
    file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work,
    comparing N products/methods/markets, validating a hypothesis with external data, or mapping how
    a field works. NOT for quick fact-checks (answer directly), structured 12-dimension competitor
    scoring (use competitive-teardown), or fast topic overviews where the decision risk is low (use
    the research router instead).
  category: Writing
  tags:
    - writing-content
    - agents
    - research
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# Deep Research — Disciplined Meta-Research\r\n\r\nTurn \"research this topic\" into an auditable, reusable investigation instead of a one-shot wall of text. The output is a folder you can return to in a month: every claim traces to a specific source file, the plan documents *why* each choice was made, and a refresh protocol lets you update it later without re-running everything.\r\n\r\n**This is the heavy, methodical end of research.** It is not a fast overview — it is the workflow you reach for when getting the answer *wrong* costs more than the tokens spent getting it right.\r\n\r\n## How it differs from a quick research router\r\n\r\nA router-style research skill (keyword-classify → delegate → short sequential search → markdown brief) is optimal when you need an answer fast and the decision risk is low. `deep-research` is the opposite trade: it pays for rigor. Use it when the answer feeds a strategy, an irreversible decision, a published artifact, or a hypothesis you need to actually test — situations where a shallow fallback would be a liability.\r\n\r\nConcretely, `deep-research` adds what a fast overview does not: falsifiable hypotheses up front, parallel sub-agent fan-out across many channels, triangulation with explicit source-type diversity, a mandatory adversarial pass, per-source files with verbatim quotes, and a `refresh_targets.md` for delta-updates later.\r\n\r\n## The pipeline (9 phases)\r\n\r\nDepth scales with the task — `shallow` runs the core phases inline; `medium`/`deep` add capability discovery, verification, and refresh targets.\r\n\r\n| # | Phase | What it does |\r\n|---|-------|--------------|\r\n| 1 | **Reframe** | Rewrite the question, fix the underlying decision, state 2–4 *falsifiable* hypotheses |\r\n| 2 | **Genre & blocks** | Pick the report genre (qa / explainer / decision / landscape / validation / custom) and its building blocks |\r\n| 3 | **Plan** | Write `plan.md`: scope, structure, sourcing strategy, opposition queries, risk register, stop-criteria |\r\n| 3.5 | **Capability discovery** | Audit available API keys/channels in the environment; map subtopics to sources; fall back to HTML where needed |\r\n| 4 | **Search** (loop) | Dispatch sources → launch sub-agents in parallel → fetch & dedup → save each to `sources/NN.md`; re-evaluate between rounds |\r\n| 5 | **Score & triangulate** | Rate every source on Credibility / Recency / Bias; require ≥3 independent, differently-typed sources per thesis |\r\n| 6 | **Synthesize + adversarial** | Assemble the report from blocks, run 4 self-critique questions, add steel-manned counter-arguments |\r\n| 6.5 | **Verify** | Lightweight citation check before closing |\r\n| 7 | **Refresh targets** | Extract entities / numbers / hypotheses into `refresh_targets.md` — the entry point for future updates |\r\n\r\n## Core mechanisms\r\n\r\nThese are what separate a documented investigation from a confident guess:\r\n\r\n- **Triangulation.** Every thesis must be backed by ≥3 independent sources of *different types* (primary / academic / industry / discussion). A claim with fewer is flagged \"insufficient evidence,\" not stated as fact.\r\n- **Source-grounding.** Each source becomes its own `sources/NN_slug.md` with metadata, verbatim quotes, and scores. No dangling claim — every assertion links back to a specific file. An empty fetch produces an empty claim, never a fabricated citation.\r\n- **Adversarial pass.** Phase 6 always runs the strongest available reasoning: 4 self-critique questions plus an active search for counter-arguments and disconfirming evidence.\r\n- **Falsifiable hypotheses.** Phase 1 commits to 2–4 hypotheses; Phases 5–6 explicitly confirm or refute each against the evidence, or mark it under-determined.\r\n- **Parallel sub-agents.** Phase 4 launches search sub-agents concurrently (cheap models for broad web sweeps, stronger ones for reasoning-heavy subtopics) — never one-at-a-time.\r\n- **Refresh protocol.** Phase 7 emits `refresh_targets.md`; an `update <slug>` run produces a delta (new entrants, entity changes, refreshed numbers, adversarial triggers) instead of replaying the whole investigation.\r\n- **Atomic findings.** Reusable theses in `findings/FN.md` plus a `sources.csv` index — research compounds across questions instead of starting from zero each time.\r\n\r\n## Output structure\r\n\r\n```\r\n<root>/<slug>/\r\n├── plan.md                  # scope, sourcing strategy, risk register, changelog\r\n├── sources.csv              # index of every source with scores\r\n├── sources/\r\n│   ├── 01_<slug>.md         # one file = one source (metadata + verbatim quotes)\r\n│   └── ...\r\n├── findings/                # atomic, reusable theses (larger investigations)\r\n│   └── F1_<short>.md\r\n├── refresh_targets.md       # what to watch on update (medium/deep)\r\n├── diffs/\r\n│   └── YYYY-MM-DD_delta.md   # delta from an `update <slug>` run\r\n└── YYYY-MM-DD_<genre>.md     # final report\r\n```\r\n\r\n## When to use\r\n\r\n- A low-quality answer is expensive: strategy, business plan, report, or article groundwork.\r\n- Comparing N institutions, products, methodologies, or markets and you need defensible reasoning.\r\n- Validating a hypothesis or a decision against external data.\r\n- Meta-research: \"understand how X works,\" \"map the landscape of Y,\" answering a connected series of questions.\r\n\r\n## Anti-Patterns\r\n\r\n- **Don't skip the existing-work check.** Before searching, see whether the answer is already in the project or in a prior research folder — you risk re-researching something you already have.\r\n- **Don't skip reframing**, even when the request \"seems clear.\" The decision behind the question usually changes the search.\r\n- **Don't output to chat only.** Always persist sources and the report to files — the reuse value is in the folder, not the transcript.\r\n- **Don't fabricate citations.** If a fetch returns nothing, the claim is empty — never invent a plausible URL. Bind every claim to a saved verbatim quote.\r\n- **Don't build conclusions on a thin corpus.** Too few sources, or sources that all share one type, means triangulation hasn't happened — say so rather than overstating confidence.\r\n- **Don't skip the adversarial pass** on medium/deep investigations. Confirmation-only research is the failure mode this skill exists to prevent.\r\n- **Don't run sub-agents sequentially.** Fan-out in parallel; serial search wastes the wall-clock advantage.\r\n- **Don't collapse `sources/` into one file.** Per-source files are what make findings searchable and reusable across investigations.\r\n- **Don't pick the heaviest model for everything.** Match model to subtask — cheap for broad sweeps, strong for synthesis and the adversarial pass.\r\n\r\n## Cross-References\r\n\r\n- **research router** — for fast topic overviews where decision risk is low; `deep-research` is the heavyweight alternative when rigor matters more than speed.\r\n- **competitive-teardown** — for comparing N competitors on a structured 12-dimension matrix.\r\n- **litreview / dossier / patent** — domain specialists when the investigation is narrowly academic, person/company-focused, or patent-focused."
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The deep-research/ folder contains
        SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/deep-research
  exported_at: "2026-11-03T07:43:36.699Z"