Meta Platform Agent Prompt Api Catalog V2

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Meta Platform Agent Prompt Api Catalog V2

Source: meta-platform-agent-prompt-api-catalog-v2.md (ingested 2026-03-28)

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Agent Prompt API System

Version: 2.0
Purpose: Systematic discovery and cataloging of all project resources via structured agent definitions
Total Agents: 120 specialized agents across 12 categories
Last Updated: 2025-01-27

C:\Users\mesha\Desktop\Desktop_Files\META-PLATFORM\Master Synthesis.md: 13 - governance: Tiered rules, compliance scoring, property-based targeting. 14 - gents: Function agents (code-gen, reviewer, tester, deployer, healer) + domain agents (Optimization/QAP, Physics, AI tools, Web, Data Science) coordinated via LangGraph. 15: - prompts: Centralized, provider-specific (claude, openai, shared), versioned with routing profiles. 16 - ools: Typed CLI (scaffold, sync, publish, manage) favoring progressive adoption and escape hatches. 17 - PROJECTS: Git submodules for local control and global governance. .. 20 - Tier 2 (~8%): Sonnet/4o for moderate complexity. 21 - Tier 3 (~2%): Opus/o1 for deep reasoning & critical changes. 22: - Fallback chain: Claude → GPT → Gemini → local; prompt adaptation per model and context-window awareness. 23 - Cost model: –,400/month vs ,000–,000 naive; batch APIs for non-urgent audits; regional routing only if residency mandates. 24 - Orchestration backbone: Dagster asset graph with dry-runs, staged rollouts, parallel DAG execution, retries/timeouts/fallbacks, state passing, conditional logic. .. 62 - Sync governance: meta-platform sync-governance --domain optimization --all-repos 63 - Compliance across submodules: meta-platform compliance-check --all-repos 64: - Prompt sync: meta-platform sync-prompts --all-repos 65
66 ## Quality, Redundancies, and Gaps .. 74 ## Cross-References 75 - Governance tiers ↔ Approval framework; dry-run diffs & staged rollouts feed decisions. 76: - Multi-LLM routing ↔ Agent metrics & cost dashboards; prompt versioning aligns to routing profiles. 77 - Template evolution ↔ Copier migration tests; Phase 2 v1→v2 migrations. 78 - Observability ↔ Grafana dashboards; circuit breakers use signals for self-regulation. .. 89 - Stand up minimal Dagster pipeline: governance scan → dry-run diff → approval decision → canary rollout → metrics emission. 90 - Define LangGraph skeleton for 2 function agents (code-gen, reviewer) and 2 domain agents (Python, Web); instrument metrics. 91: - Create prompts/claude/ routing profiles and caching policy; wire batch flows for audits. 92

API Overview

Master Synthesis Overview

  • Governance: Tiered rules, compliance scoring, property-based targeting
  • Agents: Function agents (code-gen, reviewer, tester, deployer, healer) + domain agents (Optimization/QAP, Physics, AI tools, Web, Data Science) coordinated via LangGraph
  • Prompts: Centralized, provider-specific (claude, openai, shared), versioned with routing profiles
  • Tools: Typed CLI (scaffold, sync, publish, manage) favoring progressive adoption and escape hatches
  • Projects: Git submodules for local control and global governance

C:\Users\mesha\Downloads\CONSOLIDATION\RESEARCH_CORE.md: 353 --- 354
355: # 🤖 SUPERPROMPTS 356
357: ## MEZAN Code Review Prompt 358
359: Use this prompt to review and improve MEZAN algorithm selection code: 360
361 ... 399 400
401: ## Turing Challenge Validation Prompt 402

Base Agent Interfacetypescript

Multi-LLM Strategy

  • Tier 1: Haiku/4o-mini for simple tasks
  • Tier 2: Sonnet/4o for moderate complexity
  • Tier 3: Opus/o1 for deep reasoning & critical changes
  • Fallback chain: Claude → GPT → Gemini → local; prompt adaptation per model and context-window awareness
  • Cost model: $1,400/month vs $5,000–$15,000 naive; batch APIs for non-urgent audits; regional routing only if residency mandates

Orchestration

  • Backbone: Dagster asset graph with dry-runs, staged rollouts, parallel DAG execution, retries/timeouts/fallbacks, state passing, conditional logic

Command Examples

  • Sync governance: meta-platform sync-governance --domain optimization --all-repos
  • Compliance across submodules: meta-platform compliance-check --all-repos
  • Prompt sync: meta-platform sync-prompts --all-repos

Cross-References

  • Governance tiers ↔ Approval framework; dry-run diffs & staged rollouts feed decisions
  • Multi-LLM routing ↔ Agent metrics & cost dashboards; prompt versioning aligns to routing profiles
  • Template evolution ↔ Copier migration tests; Phase 2 v1→v2 migrations
  • Observability ↔ Grafana dashboards; circuit breakers use signals for self-regulation

Implementation Steps

  • Stand up minimal Dagster pipeline: governance scan → dry-run diff → approval decision → canary rollout → metrics emission
  • Define LangGraph skeleton for 2 function agents (code-gen, reviewer) and 2 domain agents (Python, Web); instrument metrics
  • Create prompts/claude/ routing

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