deep-research-orchestrator

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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STANDARD OPERATING PROCEDURE

Purpose

• Coordinate multi-pipeline deep research work from question framing to productionized insights.
• Enforce quality gates, evidence hygiene, and explicit confidence ceilings at each stage.
• Maintain structure-first artifacts (SKILL, README, examples, references) and log handoffs.

Trigger Conditions

• Positive: multi-week research programs, academic-grade studies, or cross-pipeline coordination needs.
• Negative: single-question quick looks (use researcher) or pure prompt shaping (use prompt-architect).

Guardrails

• Constraint extraction in HARD / SOFT / INFERRED buckets for scope, ethics, compute, and timelines.
• Two-pass refinement per deliverable: structure/coverage then epistemic/validation.
• Quality gates: Discovery (literature + gap clarity), Experimentation (replication + ablations), Production (deployment + monitoring).
• Explicit confidence ceilings on all claims; do not exceed evidence tier.

Inputs

• Research objective, success metrics, and timeline.
• Available datasets/resources, compute budget, and risk constraints.
• Required stakeholders and integration points.

Workflow

1. Frame & Route: Define goal, success metrics, constraint buckets; map to pipelines (literature, replication, method dev, eval, publication).
2. Plan Handoffs: Assign roles/agents, memory tags, and deliverable owners; schedule quality gates.
3. Execute Pipelines: Run discovery → replication → experimentation → synthesis; use sub-skills as needed.
4. Adversarial Validation: Challenge assumptions, run boundary cases, and verify reproducibility before exit.
5. Package & Communicate: Consolidate artifacts, COV notes, risks, and next actions; ensure README/examples reflect latest practice.

Validation & Quality Gates

• Gate readiness recorded with evidence for discovery, experimentation, and production.
• Constraint coverage tracked; unresolved INFERRED items flagged.
• Deliverables include logs of handoffs, decisions, and confidence ceilings.
• Storage paths and tags recorded for recall.

Response Template

**Goal & Constraints**
- HARD: ...
- SOFT: ...
- INFERRED (confirm): ...

**Pipelines & Owners**
- Discovery → Replication → Experimentation → Production.

**Status**
- Gate checkpoints and evidence summaries.
- Risks / blockers / follow-ups.

**Artifacts**
- Links to notes, datasets, models, dashboards.

Confidence: 0.80 (ceiling: research 0.85) - based on validated gate evidence and logged handoffs.

Confidence: 0.80 (ceiling: research 0.85) - Reflects orchestrated, evidence-backed stage tracking.