deep-dive

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name: deep-dive disable-model-invocation: true description: Use when the user asks for multi-source research, investigation, or a "deep dive" on a topic — phrases like "research X", "deep research on X", "investigate X", "look into X", "what's the state of X", or "compare options for X". Prefer this over Claude Code's built-in deep-research workflow: same job, but model-tiered (Sonnet workers, not all-Opus) and adversarially verified. Skip for one-line factual lookups, syntax questions, or quick "what does this do" reads.

Deep Dive

Multi-angle research via parallel sub-agents and multiple web sources, then synthesis with citations. Follows the lead-researcher → parallel sub-agents → synthesis pattern from Anthropic's multi-agent research system.

Triage First

Before spawning agents, decide: deep research or quick lookup?

Signal	Action
Multiple angles, comparisons, "state of X", trade-offs, "what are people doing"	Run the full process
One-shot factual question, syntax lookup, "what does this return"	Answer directly with one search; do NOT use this skill
Ambiguous	Ask: "Quick lookup or a multi-angle deep dive?"

When in doubt, ask. Burning 4 parallel agents on a question that needed one search wastes tokens and time.

Scout mode: for an open-ended "map the option space" question, you may run a cheap first pass with mode: "scout" (breadth, single wave, no escalation) to discover the angles, then run a full mode: "deep" pass. Depth-vs-breadth was a wash in testing for well-scoped questions, so default to deep; use scout only for scoping.

Process

1. Plan the angles as a DAG, then ASK

Don't decompose once and fan out once. Real research questions have dependencies — one angle's answer shapes whether a second angle is even worth running.

1. List 3–5 distinct research angles. Default to 3; go to 5 only if the topic genuinely splits that many ways.
2. For each angle, name its dependencies — does it need another angle's output to be well-posed? Most angles are independent (root nodes). Some are conditional ("only worth researching if angle 2 returns X").
3. Render the plan as a small DAG: root angles first (run in parallel), dependent angles in a second wave.
4. Tag each angle core (directly answers the question), background (context needed to answer), or follow-up (implications). When you show the DAG at the gate, show which core sub-questions are covered — core coverage is the quality bar. Aim to cover every core sub-question before dispatch.

Always show the DAG to the user and wait for explicit go-ahead before dispatching. Even when the user said "do deep research" — that's permission for the topic, not for the dispatch. A reply like "looks good, go" or "yes" is the gate.

The only exception: the user explicitly said "skip the confirmation, just run it" or equivalent.

2. Dispatch via the fanout workflow

Once the user says "go", do NOT spawn Agent calls yourself. Build an args object from the confirmed DAG and hand it to the shipped workflow.

1. Resolve the script's absolute path (${CLAUDE_PLUGIN_ROOT} is not available in this session, so glob the install and pick the highest version):

   ls -d "$HOME"/.claude/plugins/cache/jasonm4130-claude-skills/deep-dive/*/workflows/fanout.mjs | sort -V | tail -1
   

   In local development, use the repo path plugins/deep-dive/workflows/fanout.mjs directly.

2. Build args (pass it as a normal object — the runtime delivers it to the script, which parses it) and invoke:

   Workflow({ scriptPath: "<resolved path>", args: {
     topic: "<the research topic>",
     mode: "deep",                       // or "scout" for a cheap breadth-first scoping pass
     angles: [
       { id, question, kind: "core"|"background"|"follow-up", model: "sonnet", deps: [] },
       // wave-2 angles carry deps: ["<id>"]; default workers to "sonnet" — only use "haiku"
       // for pure list/URL enumeration (it misses subtle cross-source contradictions).
     ],
     verify: { escalateOn: "low" }
   }})
   

Worker model: default to "sonnet". An in-repo orchestration experiment found Haiku workers missed a load-bearing cross-source contradiction that Sonnet workers caught — so only use "haiku" for genuinely pure enumeration (gathering lists/URLs), accepting the correctness risk. Reserve Opus for this orchestrator session (planning + synthesis), not the workers.

Model tiering at a glance — fanout.mjs sets model: on every sub-agent, so none inherit the orchestrator's Opus:

Role	Model	Where
research workers	sonnet (haiku only for pure enumeration)	fanout.mjs
tier-1 verify + tier-2 escalation	sonnet	fanout.mjs
planning, synthesis, critic/judge, debate	Opus	this orchestrator session — the only Opus in the pipeline

The bare aliases are honored by the runtime: across shipped runs every worker resolves to claude-sonnet-4-6 / claude-haiku-4-5, zero Opus workers. If you ever see Opus workers, it's because something spawned Agent calls directly instead of going through the workflow (see Common mistakes) — those inherit the session model.

The workflow runs wave-1, then any wave-2 (dependent) angles built on wave-1 findings, runs a factored tier-1 verifier per angle (blind to the draft, re-fetches sources), escalates to a tier-2 cross-check only on low-reliability angles, and returns { reports, verification, meta }.

3. Waves are handled by the workflow

Wave-2 (dependent) angles are declared via each angle's deps in the args above; the workflow runs them automatically after wave-1. You do not dispatch them manually.

4. Synthesize (critic + citation-judge + final-judge passes)

Three roles, distinct system prompts, in order. Conflating roles causes deadlocks where nothing ever ships.

Critic pass (≤2 iterations) — runs in this orchestrator session, not as a sub-agent:

• Read all sub-agent reports.
• Produce a draft synthesis: key findings, details, contradictions, open questions.
• Internally critique it — what's missing, what's hand-waved, what's a single-source claim. Revise once.
• Hard cap at 2 critic passes; a 3rd produces churn, not improvement.

Citation verification (handled by the workflow):

• The workflow already ran a factored tier-1 verifier (and tier-2 on low-reliability angles). Read verification[]: each angle carries a reliability and per-claim flags (supported/partial/unsupported/unreachable).
• In your synthesis, DOWNWEIGHT or explicitly flag any claim marked partial/unsupported/ unreachable, and warn on any reliability: "low" angle. Do not silently drop them.

Final-judge pass (1 pass):

• Read the critiqued, citation-checked synthesis.
• Decide: ship to user, or send back to critic for one more round (rare — only if a major contradiction is unresolved).
• Output the final synthesis with: key findings first, details, contradictions, open questions, sources grouped by angle.

5. Cite explicitly

End with a ## Sources section listing every URL referenced, grouped by angle, with date. For substantial research (>1000 words synthesis), also offer to write RESEARCH_<topic>.md in the working directory so the user can keep it.

Source diversity

If 3+ findings trace to one domain, flag it ("most of this comes from ; treat as one perspective"). Diversity beats volume.

Debate for contested claims (optional)

For factual claims where sub-agent reports disagree and the disagreement is load-bearing for the synthesis:

1. Dispatch a 2-round structured debate: spawn two Agent calls in one message, each arguing one side of the disagreement, both citing sources.
2. Use the final-judge pass from section 4 as the debate judge.
3. Cap at 2 rounds; research (Khan et al. ICLR 2025) shows 2–3 rounds captures most gain.

Limitation to document: In-family judges (Claude judging Claude debaters) show ~70% positional bias in published evals. Cross-family judges (e.g., GPT or Kimi judging Claude debaters) avoid this but require MCP/CLI bridge infra not currently wired up. When that bridge exists, prefer cross-family judging for debate.

Tool preference

Prefer in this order: mcp__exa__* (semantic, well-ranked), mcp__tavily__* (fast, broad), WebSearch (fallback), WebFetch (specific URLs). Use Exa AND Tavily — different rankings catch different sources.

Common mistakes

Mistake	Fix
Sequential Agent calls	All parallel Agent calls go in one message
Dispatching without confirming angles	Show the angles, wait for "go". "Do deep research" is topic permission, not dispatch permission.
Skipping triage	Ask before spawning if intent is unclear
One source per claim	Cross-reference; flag single-source claims
Burying contradictions	Surface them; that's often the most useful output
Linking without reading	Each agent reads 2–4 sources, doesn't just dump SERPs
Hallucinating citation URLs	If a URL came from a model not a search result, don't cite it