Deepdive

A structured meta-research skill for Claude Code

Stop ad-hoc Googling. Start documented investigation.

Docs · Install · How it works · Contribute

You: investigate the trade-offs between Postgres logical replication and CDC tooling

Claude:  ✓ Reframed your question (3 hypotheses)
         ✓ Picked genre: decision (comparison + validation)
         ✓ Wrote plan.md (17 sections)
         ✓ Checked your env: 4 APIs available, 2 fallback to HTML
         ✓ Launched 4 sub-agents across 12 channels
         ✓ Saved 23 sources to sources/ with quotes
         ✓ Ran adversarial pass (3 counter-arguments)
         ✓ Report ready: research/postgres-replication-vs-cdc/2026-05-21_decision.md

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What this is

A Claude Code skill that turns "research this topic" into a 9-phase pipeline with hypothesis testing, parallel sub-agent search, source triangulation, and adversarial review.

The output is a folder you can return to in a month. Every claim traces to a specific source file. The plan documents why you made every choice. No re-research needed.

New here? Start with the Quickstart — install → invoke → first result in ~5 min.

Without this	With this
One-shot prompt → wall of text Sources lost in chat history No way to detect bias No reuse next time Generic Google results Sources include... (vague)	17-section plan.md documents every choice Each source = file with verbatim quotes Mandatory adversarial pass + opposition queries Atomic theses in findings/FN.md reusable 29 named channels + 460+ stat sources Every claim → [s12] link → specific quote

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Install in 30 seconds

For Claude Code (CLI)

git clone https://github.com/Socialpranker/deepdive.git \
  ~/.claude/skills/deepdive

That's it. Now type any of these in a Claude Code session:

• "Investigate X"
• "Изучи тему"
• "Validate this hypothesis"

For Claude Desktop (Skills enabled)

# Clone
git clone https://github.com/Socialpranker/deepdive.git
cd deepdive

# Package as .skill bundle
zip -r ../deepdive.skill . -x ".*" -x "*.zip"

# Upload via Claude.app → Settings → Skills → Add Skill

For other LLMs (Codex, Gemini, local)

The 9-phase methodology is portable. Load SKILL.md + references/*.md into the LLM's context manually. Skip the sub-agent parts and use separate chat sessions per subtopic.

Full instructions →

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How it works

The skill runs 9 phases in order:

Phase	Name	What happens

| 1 | Reframing | opus / high | | 2 | Genre & block selection | sonnet / medium | | 3 | Plan | opus / medium | | 3.5 | Capability Discovery | sonnet / low | | 4 | Search | sonnet / medium | | 5 | Claims-ledger + triangulation | haiku / low | | 6 | Synthesis + multi-angle red team | opus / high | | 6.5 | Verify | haiku / low | | 7 | Refresh targets | sonnet / medium |

Each phase runs on a model matched to its task — Opus where reasoning multiplies (1/3/6), Haiku for the parallel fan-out (4). The skill announces the routing and an estimated cost up front, once.

Every phase is transparent: you see what's happening, you confirm key decisions, and you get a folder you can return to. Want to compare models head-to-head? The eval harness scores any run on 6 axes.

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What's inside

105 Report Blocks 10 categories: FRAME · EXPLAIN · COMPARE · MAP · VALIDATE · ANALYZE · CLOSE · PEOPLE · NUMBERS · CONTEXT Each block has its own template, anti-patterns, and composition rules. Block library →	29 Search Channels Named strategies with query patterns + paywall fallbacks: web-general · academic · preprint-servers · code-github · forum-discussion · news-current · industry-reports · regulatory-legal · competitive-signals · data-statistical-gov · product-analytics · crypto-analytics · api-direct · and more Channels catalog →	460+ Stat Sources 14 cross-industry + 19 industry categories. Each entry: URL · Type · Access · Quality · Limitations · Combine-with · Fallback. Categories: gov_macro · companies_public · crypto · health · education · climate_env · science · 19 industries Sources catalog →
6 Report Genres Genre When qa Open meta-research explainer "How does X work" decision "X or Y" landscape "Who's in this space" validation "Is X true" custom Hybrid, assembled per question Genres →	39+ API Endpoints Free no-auth APIs prioritized: Semantic Scholar · OpenAlex · CrossRef · arXiv · DefiLlama · CoinGecko · Reddit JSON · HN Algolia · World Bank · SEC EDGAR · ClinicalTrials.gov · PubMed · GDELT Auth via env vars only — skill never asks for keys inline. API catalog →	Weekly Auto-Sync GitHub Actions cron validates all endpoints + discovers upstream additions: HEAD-check 39+ APIs weekly Scan public-apis & awesome-public-datasets Auto-PR for dead endpoints Reports committed to reports/ branch Workflow →
Model Routing Per-phase model selection — quality where it multiplies, cheap where it parallelizes: Reframing / plan / adversarial → Opus Sub-agent fan-out (search) → Haiku (cheap × N) Synthesis → Sonnet/high ~$2 instead of ~$8 on a deep run, and higher quality on critical phases. Override with with all on opus / with cheap mode. Routing →	Eval Harness Compare research quality across models. Same question, different configs, scored on 6 axes: Deterministic (script): citation integrity, source diversity, cost Semantic (LLM-judge): accuracy, coverage, adversarial honesty Weighted sum with a citation floor — hallucinated sources can't win on depth. Verdict = quality per dollar. Eval →	Citation Check check_citations.py resolves every source URL — dead OPEN links flagged as likely hallucinations; transport flaps marked UNKNOWN, not penalized. Ignores env proxies (trust_env=False). --strict for CI. Check →

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Example folder

Sample output for a typical decision-genre research:

research/<topic-slug>/
├── plan.md                              # 17-section plan
├── sources.csv                          # Index with C/R/B scoring
├── sources/                             # One file per source
│   ├── 01_vendor-docs.md                # Primary, total=14
│   ├── 02_benchmark-paper.md            # Academic, total=12
│   ├── 03_industry-report.md            # Industry, total=13
│   ├── 04_forum-thread.md               # Forum, total=9 (opposition)
│   └── ... (19 more)
├── findings/
│   ├── F1_<atomic-thesis>.md            # confidence: high
│   └── F2_<atomic-thesis>.md            # confidence: medium
└── 2026-05-21_decision.md               # Final report

Final report structure (assembled from the blocks chosen in plan.md):

## TL;DR
- Claim A holds under condition X [confidence: high]
- Claim B holds conditionally on threshold Y [confidence: medium]
- Claim C is disputed by opposition sources [confidence: low]

## Mental model
[How the underlying mechanism works...]

## Falsification criteria
What would disprove H1, H2, H3...

## Verdict conditional
Recommendation IF: <conditions met>
Different recommendation OTHERWISE: <conditions broken>

## Counter-arguments (steel-man)
CA1: "<the strongest opposing claim>" [source: s09]
     → Our answer: <conditions under which CA1 fails>
CA2: ...

Every claim is clickable to its source. A month later, you don't re-research — you read.

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Contribute

The catalog is most valuable when it grows. Easy contributions:

Time	Type	Example
15 min	Add a stat source	Add SimilarWeb Pro to consumer_digital
15 min	Improve a query pattern	Better arxiv channel queries for biology
30 min	New search channel	Add patent-search with USPTO+EPO fallback
1-2h	New industry category	Add industries/aerospace.md
2-4h	New report block	Add decision-tree to compare.md
Half-day	LLM adapter	Add codex/ folder with adapted protocols

Full contributing guide →

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FAQ

How is this different from ChatGPT Deep Research / Perplexity?

Those are products — closed UI, fixed flow, opaque source selection. This is open methodology — you control every step, the protocol is markdown you can fork, the source catalog is yours to extend.

They also don't separate sources into files, don't do explicit triangulation, don't run adversarial passes, and don't produce reusable atomic theses.

Does it work without Claude Code CLI?

Yes — on Claude Desktop with Skills enabled. Also works manually with any LLM by loading the markdown files into context (see "Use with other LLMs" below).

What's a research output look like?

See the example folder above. TL;DR: a folder with plan.md + sources/NN.md per source + findings/FN.md atomic theses + final <date>_<genre>.md report.

Every claim in the final report links to a specific sources/NN.md file.

Why so many files? Isn't this overkill?

For a 5-minute "what's the latest X" question — yes. That's why shallow mode exists (5-7 sources, no sub-agents, ~15 min). The full machinery is for medium (1 hour) and deep (3 hours) when you need to actually use the output for a decision.

The file-per-source structure is the key reuse mechanism. A single research often informs 3-5 future researches because you can cite individual sources/NN.md directly.

Is this just prompt engineering?

It's structured methodology + curated catalog + reusable templates + automation.

• The 9-phase workflow forces discipline
• 460+ stat sources catalog is curated knowledge
• 105 reusable blocks compose any report shape
• Weekly auto-validation keeps the catalog alive
• 25+ upstream awesome-lists give infinite discovery layer

Prompts are an implementation detail, not the value.

Can I use this commercially?

Yes — MIT licensed. Use it, modify it, integrate it into products. Attribution appreciated but not required.

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Use with other LLMs (Codex, Gemini, etc.)

The methodology is portable. ~70% of content is LLM-agnostic markdown templates.

Component	Claude-specific	Universal
SKILL.md frontmatter	✓	—
Sub-agent Explore type	✓	—
9-phase workflow	—	✓
105 report blocks	—	✓
29 search channels	—	✓
460+ stat sources	—	✓

To adapt:

1. Load SKILL.md + relevant references/*.md into the LLM's context
2. Replace sub-agent parallelism with separate chat sessions per subtopic
3. Manage source files (sources/NN.md) externally — LLM produces content
4. PRs welcome for codex/, gemini/, local/ adapters

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Star History

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На русском

Deepdive — скилл для Claude Code, превращающий «загугли это» в дисциплинированный 9-фазный процесс.

Что внутри

• 9 фаз workflow: Reframing → Genre & block selection → Plan → Capability Discovery → Поиск → Claims-ledger + триангуляция → Синтез + multi-angle red team → Verify → Refresh targets
• 6 жанров отчёта: qa / explainer / decision / landscape / validation / custom
• 105 блоков в 10 категориях — переиспользуемые секции с шаблонами и анти-паттернами
• 29 каналов поиска с paywall fallback протоколом (включая api-direct)
• 460+ статистических источников в 14 cross-industry + 19 отраслевых категориях
• 39+ API endpoints для programmatic доступа (free no-auth приоритетны)
• plan.md с 17 секциями для прозрачности
• Multi-angle red team из враждебных ролей (Skeptic/Contrarian/Gap-hunter) с триажем severity (обязателен для medium/deep)
• Weekly auto-validation через GitHub Actions

Установка

git clone https://github.com/Socialpranker/deepdive.git ~/.claude/skills/deepdive

Триггеры: «проведи ресёрч», «изучи тему», «копни глубоко», «deep dive»

Вклад

Каталог растёт через PRs. Самые ценные — новые источники в stat_sources/ и api_sources/. См. CONTRIBUTING.md.

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Built by Socialpranker · MIT License · Roadmap

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