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AI-assisted engineering impact analysis — productivity metrics and code quality insights
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
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).