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deep-research-orchestrator

@majiayu000/claude-skill-registry
5
0

End-to-end orchestration of the deep research lifecycle across discovery, experimentation, and production gates.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name deep-research-orchestrator
description End-to-end orchestration of the deep research lifecycle across discovery, experimentation, and production gates.
allowed-tools Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite
model sonnet
x-version 3.2.0
x-category research
x-vcl-compliance v3.1.1
x-cognitive-frames HON, MOR, COM, CLS, EVD, ASP, SPC

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.