/cs-research

/cs:research — Hybrid Research Router + Fallback

Command: /cs:research <research question>

The cs-research persona is the default entry point for any research request. Routes to a specialist or runs fallback. Always transparent about the routing decision.

Distinct from engineering/autoresearch-agent

These share the word "research" but serve different use cases:

• /cs:research (this command) — research-query routing + fallback workflow
• engineering/autoresearch-agent — autonomous file-optimization experiment loop (Karpathy pattern)

No overlap. Don't confuse them.

When to Run

• Default for ANY research request — let the router pick the right tool
• You're not sure which specialist applies
• You want fallback if no specialist fits
• You want one consistent entry point for research work

When NOT to Run

• You already know which specialist applies — invoke it directly (/cs:litreview, /cs:grants, etc.) and skip the routing step
• You want file-optimization experiments — use engineering/autoresearch-agent

The 6 Routing Targets

Specialist	Routes when question mentions
pulse	reddit / hn / x / buzz / sentiment / trending / "pulse on"
grants	NIH / grant / R01 / K-award / RePORTER / "grants for"
litreview	literature review / PICO / SPIDER / systematic review
syllabus	syllabus attached / course outline / reading list
patent	prior art / FTO / freedom to operate / patent / novelty
dossier	"dossier on" / due diligence / background check / "prep me for"

Minimal Intake (2-4 Questions)

Q	Asks	When
Q1	Research question (1-2 sentences, specific)	Always
Q2	Output: quick chat brief OR standalone .docx	Always
Q3	Domain disambiguation (7-option pick-list, with a recommended answer when one signal matched)	When classification is ambiguous OR a single bare-noun signal matched
Q4	Time horizon for general research (quick 5 vs thorough 15)	Only when Q3 was needed AND user picked "none of the above"

Most invocations exit at Q2.

Routing Transparency (Mandatory)

After classification, the skill always:

1. States the decision in one sentence: "Routing to litreview because you mentioned PICO and systematic review (2 signals)."
2. Offers override: "If you want general research instead or a different specialist, say so."
3. Proceeds with the recommended route if the user doesn't object (no timers).
4. If user overrides → accepts, re-routes, logs the override.

Never delegates silently. This is the trust-building property that makes the hybrid pattern work.

What You Get

If delegated to specialist: the specialist's full output (markdown briefing OR .docx, depending on specialist). Tagged with [Delegated to: research → {specialist}].

If fallback: the skill runs its own 8-step workflow and produces:

# [Research Question] — Briefing
*Generated: [DATE] | Routed: fallback*

## TL;DR
[2-3 sentences]

## Findings
### [Sub-question 1]
[2-4 paragraphs with inline citations]
### [Sub-question 2]
...

## Cross-Cutting Patterns
[1-2 paragraphs]

## Sources
[Numbered + hyperlinked + reliability tier per source]

## Audit
[Three counts + per-source tier + failures]

DOCX version uses same structure with research-pack styling.

Discipline

• Deterministic classification (NOT LLM-reasoned) — keyword signal matching via classifier.py
• Routing transparency mandatory — never silent
• Specialist delegation is pass-through — don't pre-answer specialist questions
• Fallback after Q3 when no specialist matches
• Refuse generic "research [topic]" to a specialist without paired specialist-noun
• Three-count tracking in fallback mode
• Source discipline — cite only this-session tool calls

Workflow

# Phase 1 intake (Q1 + Q2 minimum)

# Phase 2 classification
python ../skills/research/scripts/classifier.py --question "<Q1>"
# Returns: {route_to: "litreview", confidence: "high (2 signals)", matched: [...]}

# Phase 3a delegation (if specialist matched at ≥2 signals)
python ../skills/research/scripts/routing_transparency_logger.py \
  --action record_delegation --session NAME --target litreview --signals "..."
# Pass question to /cs:litreview verbatim; let it run its own intake

# Phase 3b fallback (if no specialist matched)
python ../skills/research/scripts/fallback_decomposer.py --question "<Q1>"
# Returns 3-5 sub-questions
# Run 8-step fallback workflow: source-select → search → read+extract → synthesize → cross-cut → output → audit

Stop Conditions

• Specialist delegated → specialist's stop condition applies
• Fallback complete → markdown brief or DOCX delivered
• Q3 picked but no clear specialist → ask Q4 (time horizon), then run fallback
• User says "stop" → produce partial result with what's been collected

Trigger Phrases

• "research [topic]"
• "look into [topic]"
• "what do we know about [topic]"
• "investigate [topic]"
• "find me information on [topic]"
• "do some research on [topic]"
• "I need to understand [topic]"
• Plus: any research request that doesn't obviously match a more-specific specialist

Anti-Patterns Rejected

• LLM-reasoned classification (must be deterministic keyword matching)
• Silent delegation (always surface routing decision)
• Refusing to route to a specialist when ≥2 signals match
• Silent-routing on a single bare-noun signal (e.g., "funding", "fda") — ask Q3 with a recommended answer instead
• Pre-answering the specialist's grill-me intake
• Running fallback when a specialist would clearly do better
• Fabricating sources in fallback when search is thin
• Skipping audit log in fallback mode
• Treating "dossier on [company]" as fallback when dossier is the right specialist
• Treating "what are people saying about X" as fallback when pulse matches
• Auto-routing generic "research [topic]" without paired specialist-noun (ask Q3 instead)

Related

• Agent: cs-research
• Skill: research
• Source spec: megaprompts/13-research-megaprompt.md
• Routing targets: /cs:pulse, /cs:litreview, /cs:grants, /cs:dossier, /cs:patent, /cs:syllabus
• Adjacent (NOT a routing target): /cs:notebooklm (different mode), engineering/autoresearch-agent (different use case)

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Version: 1.0.0 Source: Path-B direct conversion of megaprompts/13-research-megaprompt.md