Deep Research
Use this skill to design and run repeatable research workflows that gather evidence across many sources, preserve provenance, and synthesize results into decision-ready outputs.
This skill covers both native deep-research agents (ChatGPT Deep Research, Gemini Deep Research, Perplexity Deep Research, Claude with web search) and custom agentic research pipelines (planner / searcher / verifier / synthesizer split).
ASCII Flow
textresearch question | v research plan scope + source targets + queries + stop criteria + freshness window | v evidence gathering primary sources first + source ledger + hostile-source checks | v verification isolated verifier checks claims against ledger, not researcher context | v synthesis evidence-tiered answer + citations + contradictions + unknowns
Quick Reference
| Question | Default |
|---|---|
| When to use a native agent vs custom pipeline? | Native for ad-hoc, open-ended questions. Custom for repeatable, auditable, or multi-source workflows. |
| What is the first artifact of any research task? | The source ledger — never the synthesis. |
| When is a source trustworthy? | When it is a primary document with a stable URL, author attribution, and a verifiable date. |
| What stops an unbounded research loop? | An explicit stop criterion defined before the loop starts (saturation condition or max iterations). |
| How to handle contradictory sources? | Separate them into evidence tiers; do not resolve by averaging. |
Use This Skill When
- You need to produce a sourced comparison, brief, memo, or research dossier.
- You need to choose between a native deep-research agent and a custom pipeline.
- You want to build a repeatable, auditable research workflow with provenance.
- You need to detect hostile sources, citation laundering, or model-output-as-source.
- You need to run a verifier subagent that has not seen the researcher's context.
Do Not Use This Skill For
- Single-lookup current facts — use ordinary browsing or direct tools.
- Long-lived retrieval serving — use ai-rag.
- Local note-vault packaging — use docs-notes-retrieval.
Current Stance
The durable pattern is not "send a single query and summarize the top results."
The default architecture (as of last_validated date above):
- Plan before searching: emit a structured research plan with queries, source targets, and success criteria before any tool call.
- Ledger as contract: the source ledger is the primary artifact — synthesis is derived from it, not the other way around.
- Verifier isolation: the verifier subagent must not share context with the researcher — it reads only the ledger and checks claims independently.
- Evidence tier separation: primary sources (original docs, filings, specs) are never mixed with secondary commentary or model-generated summaries in the same evidence bucket.
- Saturation-first synthesis: synthesize only after the research loop has reached saturation (new queries return no novel facts), not on a time budget.
- Freshness windows are explicit: every claim in the final output carries a date anchor and a freshness class (stable / volatile / unknown).
- Hostile source detection is mandatory for public-web research: adversarial SEO, AI-generated content farms, and citation-laundering chains are real threats.
Patterns
Full catalog → references/patterns-catalog.md
| ID | Name | When to reach for it |
|---|---|---|
| P1 | Source-ledger-as-contract | Always — every research task |
| P2 | Plan-then-execute loop | Multi-query or multi-source tasks |
| P3 | Multi-agent research swarm | Large corpora with parallel query paths |
| P4 | Verifier subagent | Any claim that will reach a user or decision |
| P5 | Freshness-window sourcing | Time-sensitive domains (AI, market, regulatory) |
| P6 | Evidence-tier separation | When primary and secondary sources both exist |
| P7 | Citation back-pointer | Every synthesis sentence that makes a factual claim |
| P8 | Stop-criterion-as-eval | To prevent unbounded research loops |
| P9 | Hostile-source detection | Public-web research, vendor comparisons |
| P10 | Synthesis-after-saturation | All multi-pass research tasks |
Anti-Patterns
Full catalog → references/anti-patterns-catalog.md
| ID | Name | Why it matters |
|---|---|---|
| A1 | Pre-synthesizing the answer | Frames all subsequent evidence gathering; produces confirmation bias |
| A2 | Single-pass search | Misses contradictions, minority sources, and laterally relevant evidence |
| A3 | Mixing evidence tiers | Primary docs and blog summaries in the same bucket degrade citation quality |
| A4 | Model-output-as-source | LLM summaries carry hallucination risk; they are working notes, not source truth |
| A5 | Unbounded research loop | Without a stop criterion, cost and time grow unboundedly with no quality signal |
| A6 | Citation laundering | Citing a secondary article that in turn cites the primary — provenance chain breaks |
| A7 | Verifier-with-same-context | Verifier that read the same sources as researcher adds no independence |
| A8 | Vague consensus language | "Multiple sources say" without naming them is unfalsifiable |
| A9 | Date-free volatile claims | Time-sensitive facts without date anchors decay silently |
| A10 | Retrofitting citations | Writing first, citing later consistently inflates confidence in unsupported claims |
| A11 | Overweighting recency | Newest is not most authoritative — prefer canonical primary sources |
| A12 | Storing evidence with synthesis | Mixed buckets cause RAG follow-on to cite derived text as original evidence |
| A13 | Treating the open-access corpus as the field | Paywalled prior work and unpublished negative results are silently absent; output skews toward hypotheses that only look promising |
Decision Matrix: Native Agent vs Custom Pipeline
See full decision guide → references/native-deep-research-agents.md
| Criterion | Native agent | Custom pipeline |
|---|---|---|
| Setup time | Minutes | Hours to days |
| Auditability | Limited (opaque search path) | Full (ledger + traces) |
| Repeatability | Low (non-deterministic) | High (seeded, logged) |
| Source control | None | Full |
| Freshness control | Model-dependent | Explicit per query |
| Multi-step planning | Implicit | Explicit (P2) |
| Verifier isolation | Not available | P4 |
| Cost predictability | Low | High |
| Best for | Ad-hoc exploration, open questions, competitive overview | Repeatable workflows, regulated outputs, source-auditable deliverables |
Decision rule: choose a native agent when speed matters and provenance does not. Choose a custom pipeline when the output will be cited, published, used in a product, or needs to be reproduced.
Native agents comparison
Full table with API surfaces and citation fidelity → references/native-deep-research-agents.md
| Agent | API model ID / tool | Strengths | Limitations |
|---|---|---|---|
| ChatGPT Deep Research (OpenAI) | o3-deep-research (depth) / o4-mini-deep-research (speed/cost) — Responses API | Strongest source diversity, long synthesis, strong multi-step reasoning (RL-post-trained) | Sunset scheduled 2026-07-23 (recommended replacement: gpt-5.5-pro) — verify at developers.openai.com/api/docs/deprecations before starting new work, the feature guide page does not surface this. Slow (5–30 min), opaque search log, citations can go stale |
| Gemini Deep Research (Google) | deep-research-preview-04-2026 / deep-research-max-preview-04-2026 (Interactions API) | Explicit plan review before execution, Google index, async + streaming, MCP support | Preview model IDs include date suffix — will change; thinner synthesis depth than ChatGPT DR |
| Perplexity Deep Research | sonar-deep-research (Chat Completions API) | Fastest (2–5 min), paragraph-level inline citations, real-time index | Citation laundering risk on SEO topics; shorter synthesis; model averages contradictions rather than flagging; multi-component pricing makes budgeting error-prone |
| Claude with web search | web_search_20260318 (current: adds response-inclusion control) / web_search_20260209 (dynamic filtering, GA) / web_search_20250305 (basic) | Full tool-call audit trail, structured output, P2/P4 composable, memory-tool-backed persistence | No dedicated DR mode; needs P2 loop for DR-equivalent depth |
| Grok DeepSearch (xAI) | web_search / x_search server-side tools (documented at docs.x.ai) | Only agent with X/Twitter as a primary indexed source; now has a documented, API-invocable tool surface | No dedicated report-generating deep-research model or endpoint comparable to the other four; citation auditability less mature |
Architecture, Workflow, and Contracts
Full depth — planner / searcher / verifier / synthesizer subagent split, canonical contract shapes (ResearchPlan, SourceLedger, EvidenceTier, VerifierReport, SynthesisArtifact), stop-criterion design, freshness window classes — in references/agentic-research-loop-architecture.md.
Default Workflow
- Frame the research question narrowly enough to be falsifiable.
- Emit a structured research plan — see assets/templates/research-plan.template.md.
- Execute the planner/searcher loop; record every source in the ledger before any synthesis.
- Run the verifier subagent (must not share context with the searcher).
- Separate evidence tiers: primary, secondary, and model-working-notes.
- Check freshness windows; date-stamp all volatile claims.
- Synthesize into the requested artifact; every factual claim carries a citation back-pointer.
- Archive the ledger separately — see assets/templates/source-ledger.template.md.
Hypothesis Generation: The Co-Scientist Role Taxonomy
The planner / searcher / verifier / synthesizer split above answers a question. A different split is needed when the task is to generate and rank candidate ideas — research directions, product hypotheses, competing explanations for an observed effect — where there is no single retrievable answer to converge on.
Google Research's AI co-scientist (2025) decomposes that job into six specialized roles under a supervisor agent, each a separate context:
| Role | Job | Why it is separate |
|---|---|---|
| Generation | Produce initial hypotheses from literature exploration plus simulated scientific debate between personas | Generation optimizes for coverage; mixing in evaluation collapses the candidate space early (see A1) |
| Reflection | Peer-review each hypothesis for correctness, novelty, and quality | A reviewer that also generated the idea cannot reject it — the same independence rule as P4 |
| Ranking | Run an Elo-based tournament: hypotheses compete pairwise, ratings order the field | Pairwise comparison is more reliable than absolute scoring, and the rating is a live signal you can watch converge |
| Evolution | Refine the top-ranked hypotheses — simplify the concept, synthesize two ideas, or push into unconventional reasoning | Refinement is a distinct move from generation; it operates on winners, not on a blank page |
| Proximity | Compute a proximity graph clustering similar ideas | Surfaces where the field is crowded vs unexplored; prevents the tournament from ranking ten paraphrases of one idea |
| Meta-review | Synthesize patterns across all reviews and debates, feed them back as guidance | The only role that improves the system rather than a hypothesis |
The loop is generate → debate → evolve, run iteratively: new hypotheses enter the tournament, top-ranked ones are evolved and re-enter, meta-review tightens the criteria each pass. Termination is the same discipline as P8 — stop on rating saturation (no new hypothesis breaks into the top tier for N rounds), not on a fixed round count.
How this composes with the rest of this skill. Generation and Reflection both need a ledger (P1) — a hypothesis grounded in nothing is a guess, and Reflection's novelty check requires knowing what already exists. Ranking is where evidence tiers (P6) do the work: a hypothesis supported by primary sources should outrank one supported by commentary, so feed the tier into the tournament rather than letting fluency decide. Meta-review is the natural place to catch A1 and A8 across a whole run.
Scope caution. This is a hypothesis-generation architecture. It orders candidate ideas by plausibility; it does not establish that any of them are true. The output of a tournament is a ranked queue for verification, never a finding — and see A13 for the literature bias that shapes what enters the queue at all.
Sources: Google Research, "AI co-scientist" (2025); described in Gulli, Agentic Design Patterns (Springer, 2025), Ch. 21 — Exploration and Discovery.
Known Traps
| Trap | Resolution |
|---|---|
| Starting with synthesis and backfilling sources | Freeze the ledger first; emit no synthesis until the ledger passes a minimum source count and verifier check |
| Using model outputs as sources in the next research pass | Stamp working notes with tier: model-output; the verifier rejects them as primary evidence |
| Verifier reads the same context as researcher | Give the verifier only the ledger JSONL, not the search session |
| Research loop never terminates | Define the stop criterion in the plan: saturation (no novel facts in N iterations) or hard cap (M total queries) |
| Citation laundering through secondary articles | Trace every citation to its primary document; reject chains longer than two hops |
| Mixing old and new evidence for fast-moving topics | Apply P5 freshness-window sourcing; reject sources older than the window for volatile claims |
| Hostile SEO sources polluting vendor comparisons | Apply P9 hostile-source detection before adding any URL to the ledger |
| S2S session-state loss on agentic research re-runs | Persist the ledger and plan to disk before each iteration; resume from ledger state, not from conversation context |
| Trusting a vendor's feature guide as the source of truth for model availability | A provider's "how to use X" guide and its deprecations/sunset page are maintained on different cadences and can disagree — the guide may still recommend a model days before its documented shutdown. Check the deprecations page separately before wiring a pipeline to any specific model ID |
Navigation
References
- references/patterns-catalog.md — P1–P10 named patterns
- references/anti-patterns-catalog.md — A1–A12 with detection signals
- references/native-deep-research-agents.md — Comparison table, ChatGPT DR, Gemini DR, Perplexity DR, xAI Grok DeepSearch, Claude with web search, Anthropic memory tool, URL-health recipe
- references/agentic-research-loop-architecture.md — Planner / searcher / verifier / synthesizer subagent split
- references/research-workflow.md — Staged research loop and source-plan design
- references/evidence-packaging.md — Provenance, citation, and synthesis-packaging rules
Assets / Templates
- assets/templates/research-plan.template.md — Research plan scaffold
- assets/templates/source-ledger.template.md — Source ledger scaffold
Scripts
python3 scripts/citation_verifier.py --input claims.jsonl— Checks JSONL of {claim, source_url, supporting_quote} and reports unsupported claims
Data
- data/sources.json — Curated source repos and tool references
Related Skills
- ../ai-rag/SKILL.md — Retrieval architecture and serving indexes
- ../docs-notes-retrieval/SKILL.md — Local note-vault packaging
- ../agents-mcp/SKILL.md — MCP search, docs, and memory tool surfaces
- ../qa-agent-testing/SKILL.md — Eval harnesses for productized research workflows
- ../ai-agents/SKILL.md — Agent architecture (planner/searcher/verifier split)
Fact-Checking
- Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
- Start from
data/sources.jsonfor workflow and tool references. - Verify native deep-research agent capabilities and output formats before recommending them for specific use cases — features change frequently.
- Check the provider's deprecations/sunset page, not just its feature guide, before committing a pipeline to a specific model ID — the two pages update on different cadences and the guide can lag behind an already-announced shutdown by days or weeks.
- If live verification is unavailable, mark native-agent capability claims as unverified.
Learnings Loop
Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).
After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.
