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AI-assisted engineering impact analysis — productivity metrics and code quality insights
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Onboard a new client/company onto this platform's real Agentic OS: create the company record, scan its websites/repos, auto-provision specialist agents, activate its 24x7 agency runtime, and know exactly which "OS" building blocks (memory, integrations, dashboard) already exist versus which are roadmap gaps. ADAPTED FROM: a third-party giveaway skill ("agentic-os-installer" by Gennaro Santoro / Operations Heroes) that described a generic vault + Google-suite + skill-pack installer. That skill's product (Obsidian vault, Gmail/Calendar/ Drive wiring, "skill packs") does not exist in this repo and its promotional content (Skool community link) does not belong here. This is a clean-room rewrite that keeps the useful idea — "stand up a working agency OS for a client from a short checklist" — and maps every step to the real module that already implements it in this codebase, per CLAUDE.md architecture rules.
Agile sprint planning, velocity tracking, and burndown metrics for agent-managed projects
Initiative-level portfolio management with dependency tracking and milestone coordination
Cross-harness agent patterns — standardize agent execution across different coding assistants
Temporal context graph for agent memory — track entity relationships and state changes over time
Andrej Karpathy's coding guidelines for AI agents — concise, correct, and well-tested code
| name | ai-engineering-insights |
| description | AI-assisted engineering impact analysis — productivity metrics and code quality insights |
Inspired by: DX Q1 AI-Assisted Engineering Impact Report
Purpose: Track engagement, performance, and tool quality for AI engineering tools — the metrics engineering leaders use to justify spend and pick winning vendors.
The DX report defines three metric pillars that local-llm-server now mirrors:
agents/ai_insights.pyfrom agents.ai_insights import (
EngagementMetrics, PerformanceAnalytics, AIToolMetrics,
UsageEvent, ToolKind, build_report,
)
eng = EngagementMetrics()
eng.record(UsageEvent("alice", "claude_code", ToolKind.AGENT, datetime.now(), accepted=True))
eng.weekly_active_users() # → 1
UsageEvent from agent loops, completion endpoints, chat handlers.build_report(...) output in admin GUI.AIToolMetrics.tool_ranking() to compare tools objectively.statistics.median — resistant to outliers (a single 100-hour PR doesn't skew cycle-time delta).