القائمة
Multi-expert intelligence pipeline review — discovery quality, ingestion resilience, scoring validity, platform compliance, taxonomy coherence, cost efficiency
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Multi-expert AI/ML specification review with scoring gate — model architecture, evaluation rigor, safety/alignment, and production readiness for AI systems and LLM apps
Multi-expert architecture review — boundaries, integration patterns, failure modes, evolvability
Multi-expert business analysis with advisory recommendations (no scoring gate)
Multi-expert mobile/native app specification review with scoring gate — Android, iOS, Swift, SwiftUI
Multi-expert personal-development review with scoring gate — learnability, adoption, human-centeredness, and capability impact for talent/learning/AI-augmentation designs
Multi-expert research and discovery panel with source evaluation, API feasibility, collection strategy, and intelligence gap analysis. Use when evaluating data sources, APIs, scraping strategies, OSINT collection plans, or any research/discovery effort that needs expert validation before implementation.
| name | sh-consigliere-panel |
| description | Multi-expert intelligence pipeline review — discovery quality, ingestion resilience, scoring validity, platform compliance, taxonomy coherence, cost efficiency |
/sh:consigliere-panel [specification_content|@file] [--mode discussion|critique|socratic] [--focus discovery-quality|ingestion-resilience|scoring-validity|platform-compliance|taxonomy-coherence|cost-efficiency] [--experts "name1,name2"] [--iterations N] [--verbose]
--verbose): Full expert deliberations, cross-expert dialogue, reasoning traces, and detailed per-expert analysis before scores and findings.Silent mode still performs full internal analysis — quality is preserved, only the output is compressed.
/Users/jcords-macmini/projects/20_agentflow/experts/PANEL_PROTOCOL.md and apply its Grounding (non-code branch — every finding quotes the source section it rests on) and Refute Stage sections. Findings that misquote or cannot cite the source MUST NOT be reported.experts/panels/consigliere-panel.yaml for panel definition, focus areas, auto-select rules, and scoring configexperts/individuals/ for each selected expert — these files contain the expert's domain, methodology, and critique focusauto-select keywords — add matching experts up to max-experts: 7 cap--focus area or use default-experts from panel YAML. --experts override replaces defaults entirelyExperts are defined as individual markdown files in experts/individuals/. Each file contains structured frontmatter with:
The panel YAML (experts/panels/consigliere-panel.yaml) defines:
| Category | Expert | Domain | Failure Mode Coverage |
|---|---|---|---|
| Discovery Quality | Hilary Mason | Data signals, authority metrics, ranking algorithms | "It ranks wrong" |
| Discovery Quality | DJ Patil | Cross-platform identity, entity resolution | "It matches wrong" |
| Platform Resilience | Michael Nygard | Reliability patterns, failure modes, circuit breakers | "It breaks" |
| Platform Resilience | Filippo Valsorda | API security, platform compliance, rate limits | "It gets banned" |
| Ingestion Pipeline | Martin Kleppmann | Data pipelines, consistency, deduplication | "It loses data" |
| Ingestion Pipeline | Jay Kreps | Event streaming, delivery guarantees, throughput | "It drops messages" |
| Taxonomy Coherence | Deborah McGuinness | Ontologies, taxonomies, classification systems | "It classifies wrong" |
| Cost Efficiency | Chip Huyen | MLOps, LLM cost optimization, model routing | "It costs too much" |
--mode discussion)Collaborative improvement through expert dialogue. Experts build upon each other's insights sequentially. Cross-expert validation and consensus building around critical improvements.
--mode critique)Systematic review with severity-classified issues (CRITICAL / MAJOR / MINOR). Each finding includes: expert attribution, specific recommendation, priority ranking, and quality impact estimate.
--mode socratic)Learning-focused questioning to deepen understanding. Experts pose questions about scoring validity, delivery guarantees, platform constraints, taxonomy principles, and cost projections. No direct answers — forces the author to think critically.
5 dimensions, each scored 0-10:
| Dimension | Description |
|---|---|
| Discovery Validity | Scoring formulas produce meaningful rankings; normalization is bounded; false positives are controlled |
| Platform Resilience | External system failures degrade gracefully; auth lifecycle is managed; rate limits are respected |
| Pipeline Integrity | Quarantine-first honored; dedup correct; plugin contracts satisfied; async handoffs don't lose data |
| Taxonomy Coherence | Classification is consistent; feed-forward growth doesn't pollute; cross-walks are bidirectional |
| Cost Efficiency | API calls within budget; LLM routing minimizes cloud spend; storage growth is bounded |
Pass threshold: overall score >= 7.0
Output includes per-dimension scores, overall score, critical issues, expert consensus points, and an improvement roadmap (immediate / short-term / long-term).
Intelligence pipeline review document containing:
AUTO-FIX, NOT SYNTHESIS-ONLY — this panel produces the analysis AND then applies fixes for every finding (high, medium, and low) automatically, per 00_Governance/CLAUDE.md §8 Panel Auto-Fix Policy. It never asks which findings to apply and never presents a menu: it fixes everything, then reports what changed. A below-gate score means fix the findings and re-run, not stop and ask.
Fix ALL findings automatically — high, medium, and low severity. Do not ask which findings to fix. Do not present a menu. Fix everything, then report what was changed.