ADLC Team Skills — Agentic SDLC for Engineering Teams Stop Vibe Coding in Silos. Build a Shared Cognitive Layer for Your Engineering Team. Individual prompt hacks create quick wins for solo developers, but when scaled across a team, "vibe coding" leads to chaotic technical debt, context rot, unreviewable PRs, and lost code ownership. Speed is solved; Trust and Verification is the new bottleneck in AI engineering. ADLC Team Skills ( tikalk/adlc-team-skills ) is the open-source Team Layer of the Twelve-Factor Agentic SDLC . It turns AI agents from isolated guessers into compliant, accountable team members that share your team's constitution, product strategy, architectural standards, and evaluation benchmarks. Team AI Directives ( tikalk/agentic-sdlc-team-ai-directives ) is the companion repository that holds your team's version-controlled context modules (constitution, rules, personas, examples), CDR index, and skills manifest. Quickstart # Install skills + generate slash commands + wire session_start events npx adlc-skills-cli add tikalk/adlc-team-skills -a opencode # Or with npx skills only (skills without commands/events) npx skills add tikalk/adlc-team-skills -a claude -g Works out of the box with any agent supporting the Agent Skills standard — Claude Code, Codex, OpenCode, Cursor, GitHub Copilot, and others. Slash commands + events: adlc-skills-cli wraps npx skills add and additionally generates /name slash commands and wires session_start event hooks (via .events.json ) for 9 coding agents. Skills repos without .events.json get commands only. Universal orchestration: mission-brief auto-discovers skills from any source (mattpocock/skills, addy osmani/agent-skills, superpowers, spec-kit, or your own) and dynamically wires them into the mission pipeline. No vendor lock-in. Golden path — bootstrap a new team-ai-directives team-boot auto-runs at session start via the event hook. On an unconfigured project it outputs a warning telling the user to run /team-setup . team-setup is also available on demand: npx adlc-skills-cli add tikalk/adlc-team-skills -a opencode # install skills + commands + events Then choose Mode 3 — Scaffold new empty team-ai-directives , or Mode 1 — Clone from GitHub to fork tikalk/agentic-sdlc-team-ai-directives : team-setup → pick destination (default ./team-ai-directives) + team name → scaffolds README / AGENTS.md / CDR.md / .skills.json / constitution placeholder / OKF index files + git init team-constitution → interactively replace the placeholder with your real principles team-boot (auto) → assembles constitution + CDR index + PDR/ADR indexes + skills registry into the system prompt at session start Already have a directives repo? team-setup offers three other modes: Mode 1 — Clone from GitHub (e.g. fork tikalk/agentic-sdlc-team-ai-directives ) Mode 2 — Point to existing local path (wire a repo you already have) Mode 4 — Already configured (verify an existing setup) How It Works: Before vs. After ADLC Without ADLC (Vibe Coding) With ADLC Team Skills Session starts from zero : Agent knows nothing about your architecture, team rules, or deprecated patterns. Auto-bootstrapped context : team-boot auto-loads your Team Constitution & active decisions on session start. Prompt wall bloat : Dumping a 10,000-token prompt wall wastes tokens and causes model instruction drift. Progressive disclosure : team-boot injects a ~100-token index; team-discover fetches only the 1–2 rules relevant to the task. Ambiguity leads to guessing : Agent invents functions or database schema instead of asking questions. Contract-first specs : mission-brief defines Goal, Constraints, Non-Goals, and Success Criteria before writing code. Learnings evaporate : Debugging fixes and newly discovered edge cases disappear when the chat ends. Closed feedback loop : levelup-specify extracts session execution traces and commits new rules directly to Git. Silent regressions : Prompt edits or base model updates silently degrade agent output. Verification-first evals : Automated LLM judges and binary graders test code against business risks before human review. Directives as Code: The Four Team Pillars [ THE GREAT FILTER ] (Human Team Lead Macro-Review) ▲ │ ┌──────────────────┴──────────────────┐ │ Pillar 4: Governance & Evals │ │ (Tier 1 Fast Checks + LLM Judges) │ └──────────────────▲──────────────────┘ │ ┌──────────────────┴──────────────────┐ │ Pillar 3: Spec-Driven Workflow │ │ (Contract-First Mission Pipeline) │ └──────────────────▲──────────────────┘ │ ┌──────────────────┴──────────────────┐ │ Pillar 2: Product & Architecture │ │ (Product PDRs + Architecture ADRs)│ └──────────────────▲──────────────────┘ │ ┌───────────────────┴───────────────────┐ │ Pillar 1: Strategy & Team Directives│ │ (team-boot / levelup / CDR repository) │ └───────────────────────────────────────┘ 🏛️ Pillar 1: Strategy & Team Directives (Team-First Alignment) Put the team at the center of your AI strategy. Instead of individual developers hoarding prompt shortcuts on local machines, team standards live in a version-controlled Git repository ( team-ai-directives ). team-boot : Auto-runs at session start via the event hook, assembling the team constitution, CDR index, PDR/ADR indexes, and skill registry into the system prompt. Progressive Disclosure (No Token Waste) : Instead of dumping massive prompt walls into every session, team-boot injects a compact ~100-token index. team-discover loads full rule bodies only when relevant to the active task. team-constitution : Interactively define, review, or amend your engineering team's core principles. team-repair : Re-index CDR.md, scan for rule conflicts, and verify directive freshness. team-boot → assembles constitution + CDR index + PDR/ADR + skills into system prompt team-discover → manual re-scan for structured discovery tables (/team-discover) team-constitution → create or amend the team constitution interactively team-repair → re-index CDR.md, scan for conflicts, verify freshness 🎯 Pillar 2: Product Strategy & Architectural Governance (PDRs & ADRs) Without documented decisions, every implementation session re-derives (or misinterprets) product intent and architectural rules. Product Decision Records ( product-* ) : Capture product decisions as individual PDR files, resolve ambiguities through an interactive clarification workflow, and compile them into a self-contained PRD.md . Architectural Decision Records ( architect-* ) : Reverse-engineer or define architectural decisions using Rozanski & Woods viewpoints (Functional, Security, Deployment, Performance) and compose them into a unified AD.md . product-roadmap : Track milestone progress across four layers of truth — decisions (PDRs), execution (live issues via MCP), code evidence, and milestone gates. Product: product-init → product-clarify → product-implement → product-analyze Architecture: architect-init → architect-clarify → architect-implement → architect-analyze Roadmap: product-roadmap (tracks PDRs + issues + code + gates) 📐 Pillar 3: Spec-Driven Workflow ("Debug the Spec, Not the Code") AI is an obsessive guesser — when faced with ambiguity, it invents solutions instead of asking questions. Move from a Conversational model to a Contract model. mission-brief : The team's autonomous pipeline runner. Takes a feature prompt, derives a formal contract (Goal, Constraints, Non-Goals, Success Criteria), generates an ordered step list, and walks a specify → plan → tasks → implement ↺ converge loop to completion. Mantra: "Debug the Spec, Not the Code" : When an agent makes a mistake, don't just patch the code — add the missing constraint to the specification so the mistake is never repeated. mission-brief "add user profile API with JWT" ├── Phase 2: Brief (Goal, Constraints, Non-Goals, Success Criteria) ├── Phase 3: Route Classification (spec | change | quick) ├── Phase 4: Discovery (auto-wires local installed skills & SDD frameworks) └── Phase 5: Execute (specify → plan → tasks → implemen