Multi-Agent Research

Wave-based knowledge gathering with deferred synthesis. Mined from 300+ real research dispatches — the pattern that consistently produces actionable intelligence.

Core insight: Research breadth-first, synthesize after. Don't draw conclusions from the first 3 results. Deploy agents in waves, accumulate findings, then synthesize with the full picture.

The Process

digraph research {
    rankdir=TB;
    node [shape=box];

    "1. PRIME" [style=filled, fillcolor="#e8e8ff"];
    "2. WAVE 1: Broad Sweep" [style=filled, fillcolor="#ffe8e8"];
    "3. GAP ANALYSIS" [style=filled, fillcolor="#fff8e0"];
    "4. WAVE 2+: Targeted" [style=filled, fillcolor="#ffe8e8"];
    "5. SYNTHESIZE" [style=filled, fillcolor="#e8ffe8"];
    "6. DECIDE & RECORD" [style=filled, fillcolor="#e8e8ff"];

    "1. PRIME" -> "2. WAVE 1: Broad Sweep";
    "2. WAVE 1: Broad Sweep" -> "3. GAP ANALYSIS";
    "3. GAP ANALYSIS" -> "4. WAVE 2+: Targeted";
    "4. WAVE 2+: Targeted" -> "3. GAP ANALYSIS" [label="still gaps", style=dashed];
    "3. GAP ANALYSIS" -> "5. SYNTHESIZE" [label="coverage sufficient"];
    "5. SYNTHESIZE" -> "6. DECIDE & RECORD";
}

Phase 1: PRIME

Search what you already know before spawning a single agent.

Actions

Search Sibyl first:

sibyl search "<research topic>"
sibyl search "<related technology>"
sibyl search "<prior decision in this area>"

Check for stale knowledge:
- If Sibyl has entries older than 3 months on fast-moving topics (frameworks, models, cloud services) → research anyway, but use existing knowledge as baseline
- If Sibyl has recent entries → present them first, ask if deeper research is needed
Define the research question clearly:
- NOT: "research databases" (too vague)
- YES: "compare PostgreSQL vs CockroachDB for multi-region write-heavy workloads with <10ms p99 latency requirement"

Set the research budget:

Depth	Agents	Time	When
Quick scan	2-3	2-5 min	Known domain, just need latest info
Standard	5-10	10-15 min	Technology evaluation, architecture options
Deep dive	10-30	20-40 min	Greenfield decisions, SOTA analysis
Exhaustive	30-60+	40-90 min	New project inception, competitive landscape

Phase 2: WAVE 1 — Broad Sweep

Deploy the first wave of agents across the full research surface.

Agent Design Principles

Each agent gets:

One specific topic (not "research everything about X")
An output file path (no ambiguity about where to write)
Search hints (include year: "search [topic] 2026")
8-12 numbered coverage items (scope the research precisely)
Source quality guidance ("prefer official docs and GitHub repos over blog posts")

Wave 1 Template

Research [SPECIFIC_TOPIC] for [PROJECT/DECISION].

Create a research doc at docs/research/[filename].md covering:

1. Current state (latest version, recent changes)
2. [Specific capability A relevant to our use case]
3. [Specific capability B]
4. [Integration with our stack: list specific technologies]
5. Performance characteristics / benchmarks
6. Known limitations and gotchas
7. Community health (stars, activity, maintenance)
8. Comparison with alternatives (name 2-3 specific alternatives)

Use WebSearch for current information. Include dates on all facts.
Cite sources with URLs.

Deployment Rules

ALL Wave 1 agents run in background — no dependencies between them
3-4 seconds between dispatches — avoid rate limiting
Each agent writes its own file — no shared outputs
Group by theme: If researching 12 topics, group into 3-4 thematic clusters

Coverage Strategy

For technology evaluations, cover these dimensions:

Dimension	Question
Capability	Does it do what we need?
Performance	Is it fast enough?
Ecosystem	Does it integrate with our stack?
Maturity	Is it production-ready?
Community	Will it be maintained in 2 years?
Cost	What does it cost at our scale?
Migration	How hard is it to adopt/abandon?

Phase 3: GAP ANALYSIS

After Wave 1 completes, identify what's missing before synthesizing.

Actions

Read all Wave 1 outputs — skim each research doc
Identify gaps:
- Dimensions not covered?
- Contradictory findings between agents?
- Questions raised but not answered?
- Missing comparisons?
Check for bias:
- Are all findings positive? (Suspicious — look for failure cases)
- Did agents only find official docs? (Need community/real-world experience)
- Same sources cited by multiple agents? (Need diversity)

Decision Point

Finding	Action
Good coverage, minor gaps	Synthesize now, note gaps
Significant gaps	Deploy Wave 2 targeted agents
Contradictory findings	Deploy verification agents to resolve
Entirely new direction emerged	Deploy Wave 2 in new direction

Phase 4: WAVE 2+ — Targeted Research

Fill specific gaps identified in the analysis.

Wave 2 Agents Are Different

Smaller scope — one specific question per agent
Higher quality bar — "find production experience reports, not just docs"
Cross-reference — "Agent X found [claim]. Verify this against [alternative source]"
Deep reads — "Read the full README and API docs for [library], not just the landing page"

When to Stop

Stop deploying waves when:

The research question can be answered with confidence
Additional agents would produce diminishing returns
You have >= 2 independent sources for key claims
The user signals "enough, let's decide"

Max 3 waves for most research. If 3 waves haven't answered the question, the question needs reframing.

Phase 5: SYNTHESIZE

Combine all findings into actionable intelligence. This is where the magic happens.

Synthesis Structure

## Research: [Topic]

### TL;DR
[2-3 sentences. The answer, not the journey.]

### Recommendation
[Clear choice with justification. Don't hedge — pick one.]

### Options Evaluated

| Option | Fit | Maturity | Perf | Ecosystem | Verdict |
|--------|-----|----------|------|-----------|---------|
| A | ... | ... | ... | ... | Best for [X] |
| B | ... | ... | ... | ... | Best for [Y] |
| C | ... | ... | ... | ... | Avoid: [reason] |

### Key Findings
1. [Most important finding with source]
2. [Second most important]
3. [Third most important]

### Risks & Gotchas
- [Known issue or limitation]
- [Migration complexity]
- [Hidden cost]

### Sources
- [Source 1](url) — [what it contributed]
- [Source 2](url) — [what it contributed]

Synthesis Rules

Lead with the recommendation. Don't make the reader wade through findings to find the answer.
Separate facts from opinions. "PostgreSQL supports JSONB" (fact) vs "PostgreSQL is better for this use case" (opinion backed by evidence).
Include dissenting evidence. If one source contradicts the recommendation, say so. Don't cherry-pick.
Date everything. "As of Feb 2026, [library] is at v4.2" — research spoils fast.
Note confidence level. "High confidence: well-documented. / Low confidence: based on one blog post."

Phase 6: DECIDE & RECORD

Lock in the decision and capture it for future sessions.

Actions

Present the synthesis to the user with a clear recommendation

Record in Sibyl:

sibyl add "Research: [topic]" "Evaluated [options]. Chose [X] because [reasons]. Key risk: [Y]. Sources: [primary URLs]. Date: [today]."

Archive research docs — keep the wave outputs for reference:
- If in a project: docs/research/[topic]/
- If general knowledge: Sibyl learning entry is sufficient

Exit to next action:

Next Step	When
`/hyperskills:brainstorm`	Research surfaced multiple viable approaches
`/hyperskills:plan`	Decision made, ready to decompose implementation
`/hyperskills:orchestrate`	Decision made, work is parallelizable
Direct implementation	Research confirmed a simple path

Quick Research Mode

For focused questions that don't need the full wave protocol:

Search Sibyl (always)
2-3 targeted searches (WebSearch + WebFetch on key URLs)
Synthesize inline (no separate docs)
Record if non-obvious (Sibyl learning)

Use when: "What's the latest version of X?", "Does Y support Z?", "What's the recommended way to do W?"

Research Patterns by Type

Technology Evaluation

Wave 1: Official docs + GitHub README for each option (parallel)
Wave 2: Production experience + benchmarks (parallel)
Synthesize: Comparison matrix + recommendation

Codebase Archaeology

Wave 1: Explore agents mapping each subsystem (parallel)
Wave 2: Grep for specific patterns / usage (parallel)
Synthesize: Architecture diagram + dependency map

SOTA Analysis

Wave 1: WebSearch for latest papers, blog posts, releases (parallel)
Wave 2: Deep read the most relevant 3-5 sources (parallel)
Synthesize: What's genuinely novel vs rehashed + recommendation

Competitive Landscape

Wave 1: Feature matrix for each competitor (parallel)
Wave 2: Pricing, community size, trajectory (parallel)
Synthesize: Positioning matrix + gap analysis

Anti-Patterns

Anti-Pattern	Fix
Synthesizing after Wave 1 only	Wait for gap analysis — premature conclusions miss nuance
50 agents with "research everything"	Specific scope per agent — vague prompts produce vague results
Only official documentation	Include community experience — docs show intent, community shows reality
No dates on findings	Date everything — research spoils faster than produce
No recommendation	Force a decision — "more research needed" is only valid with a specific question
Researching what Sibyl already knows	Always prime first — don't burn tokens re-discovering known patterns

What This Skill is NOT

Not a substitute for reading code. If the answer is in the codebase, read the codebase.
Not an infinite loop. Max 3 waves. If that's not enough, reframe the question.
Not required for known domains. If you already know the answer, just say so and cite your knowledge.
Not a delay tactic. Research serves a decision. If no decision follows, the research was waste.