Show HN: AI-factory – a spec-driven pipeline that stops AI agents rotting code Highflame released ai-factory, an open-source spec-driven development pipeline for AI coding agents that enforces deterministic gates and multi-agent review to prevent codebase degradation. The pipeline, which the company says delivers 3-5x faster feature delivery in internal use, installs as slash commands for Claude Code or as a declarative pack for Highflame's codeoid runtime. 0→1 with an AI coding agent is easy. 1→100 is where it gets hard — the code that survives review, scales past the demo, and doesn't quietly rot the codebase over months. That's where ai-factory comes in. A complete spec-driven development practice for AI coding agents — skills, agents, deterministic hooks, and a role-based capability model — installed once and parameterized to your org through config, not forks. Why ai-factory Faster — across our own development at Highflame, the spec-driven pipeline plus parallel sprint orchestration routinely delivers 3–5x : features/PRs shipped, bugs resolved, and time-to-merge, vs. ad-hoc AI-assisted coding. our internal experience — your mileage will vary Less slop — deterministic gates, an adversarial multi-agent review bench, and a verification-is-non-negotiable ethos: unreviewed AI output can't reach main . Leaner context — heavy research and review run in subagents with their own context windows; the main session stays focused. Two ways to run it: Standalone in — 41 skills as slash commands Claude Code https://claude.com/claude-code /spec → /architect → /proceed → /ship , symlinked live into every session. Clone, ./install.sh , go. Under — the same methodology as a declarative pack on codeoid's multi-session runtime, with per-agent identity and cross-session memory: codeoid https://github.com/highflame-ai/codeoid codeoid run --pack aif-sdlc . Three layers: The AIF pipeline — a spec-driven development lifecycle spec → architect → validate → implement → reflect → review → ship with parallel sprint orchestration, a multi-agent review bench, and the aif health CLI. The org-workflow layer — day-to-day engineering skills issue-to-PR orchestration, debugging, deprecation, git conventions, session handoffs, environment triage , review agents, and deterministic hooks. Everything org-specific resolves through .aif/config.yml — nothing is hardcoded, and every skill states what it does when a config key is absent. The pack registry — the methodology as declarative packs/ codeoid https://github.com/highflame-ai/codeoid packs schema: codeoid/pack@v1 . A pack is data-only a pack.yaml + capability roles + constitution ; codeoid runs its governed phase pipeline. Contribute a pack once, and any team can select it. See— first pack: packs/README.md . aif-sdlc git clone https://github.com/highflame-ai/ai-factory.git cd ai-factory && ./install.sh install.sh is idempotent and repair-capable. It symlinks: | Link | Target | Consumed by | |---|---|---| ~/.claude/skills | skills/ | every skill /spec , /ship , … | ~/.claude/agents | agents/ | every subagent @security-reviewer , … | ~/.claude/aif-hooks | hooks/ | the hooks block in each repo's .claude/settings.json | ~/.claude/aif-bin | bin/ | sidecar helpers | ~/.claude/aif-references | references/ | on-demand checklists |