pilotspace-add 2.0.0

ADD (AI-Driven Development) 2.0. The agent is the hands; ADD is the memory, judgment, and conscience — one freeze per feature, trust from passing tests, state on disk so context rot never survives a new session. Installs the ADD skill and tooling into any project.

ADD — AI-Driven Development

Your AI's first milestone is always great. ADD is for every milestone after that.

A minimal, state-tracked skill for building software when the AI writes the code and you own the two things it cannot do alone: decide what to build, and verify it is correct. Native on Claude Code; every other CLI coding agent follows the same loop through the phase guides.

The agent is the hands. ADD is the memory, judgment, and conscience — the part of the team that survives when the context window doesn't. Memory: the board, frozen contracts, and living specs on disk (add.py status resumes any session losslessly). Judgment: personas propose each task's lane and the loop learns from the traced outcomes (add.py deltas). Conscience: evidence-scored gates, the tamper tripwire, the security hard-stop. It sits on top of a context foundation (DDD → SDD → UDD) and runs as a red/green TDD ↔ AI-build loop. The full reasoning — why every rule exists — is the AIDD book, published online. Read it once; keep it open beside you.

  Foundation (context):  DDD  ·  SDD  ·  UDD
  Engine (this skill):   TDD  ⇄  ADD
  Flow per task:  Direction (spec · scenarios · contract · red tests → ONE freeze)
                  → Build (red → green)  → Verify (evidence-scored gate)  ↻

Quick Start

# Node / npm
npx @pilotspace/add init

# Python / pip
pip install pilotspace-add && pilotspace-add init

Then, in your coding agent, say what you want to build:

/add — "Let users log in with email + password / SSO, and keep them signed in for 30 days unless they explicitly log out."

The agent sizes it into a milestone (you confirm the shape), drafts the specification bundle — spec → scenarios → contract → red tests as one Direction pass (you approve once, at the frozen contract), then builds and verifies to green. Full detail below — Install, Use it, and the 10-minute Quickstart.

Highlights

• 📉 Anti-context-rot by design — every decision lives on disk (PLAN.md, frozen contracts, red suites, .add/state.json), so a fresh session resumes losslessly; measured flat 1.0 quality across evolving milestones while conversation-carried flows decayed.
• ✅ Approve once, then let it run — one human sign-off at the frozen contract; the agent drives Direction → Build → Verify.
• 🔬 Proof, not promises — verified against observed behavior and pre-declared expectations, never just a plausible-looking diff; weakening a test to reach green is treated as tampering.
• 💸 Lean by measurement — a thin 31-verb state kernel, a 3-call task walk, one file per feature; $2.20 per trusted milestone vs spec-kit's $3.90 in the latest head-to-head.
• 🔒 Security never gets waved through — any security finding is a hard stop, human in the loop.
• 🧠 Personas route the work — a project-owned persona proposes each task's lane, the freeze ratifies it, outcomes are traced, and the loop reflects on the record (GEPA) so the method adapts to your codebase.
• 🎨 See it before you build it — a wireframe and a zero-dependency HTML mock, approved before any code.
• 👥 Built for teams — git-native multi-user, N parallel milestones, DAG-scheduled waves.
• 🧩 One slice, many components — monorepo or multi-repo, in one team.
• 🤝 Works with your AI — Claude, Copilot, Cursor, Codex, Gemini; install via npm, pip, or the Claude Code plugin.

Direction before speed. Trust comes from passing tests — not from reading code and finding it plausible.

Why ADD — the pain point is context rot, and we measured it

Every AI coding tool can write code fast now, and every flow we benchmark aces a greenfield first milestone. The unsolved part is trust across change: when the spec evolves in milestone 2 and breaks compatibility in milestone 3, does the work you already trusted stay trusted?

Our benchmark runs the same six-milestone evolving project through each flow under a pinned model with deterministic probe scoring (report, revised edition). The causal finding: when ONE continued conversation carried the milestones, every flow decayed the same way (coverage .92 → .75, with an early list-shape spec violation carried through five further milestones, never re-examined). When every milestone instead started a fresh session resuming from disk, ADD held every floor at 1.0 across all six milestones — through a breaking shape change and a cross-cutting refactor — with zero regressions, at ~$2.90 per milestone (a 3–5× cut vs ADD 1.x's $4.65–13.94 per trusted feature). Honesty note, from the same report: on this friendly workload a strong model under spec-kit also passed the restart-mode floors (and ran cheaper) — we published the retraction of our own earlier collapse claim when we found the meter defect behind it. What ADD uniquely adds is the guarantees: contracts that can't be silently edited, tests that can't be quietly weakened, security findings that can't scroll past.

That's the design, in three moves:

• One file per feature. Spec, scenarios, contract, test-plan, and gate record all live inline in a single PLAN.md. No sprawling doc tree.
• State on disk, not in chat. A stdlib-Python kernel tracks where you are in .add/state.json, so a fresh session resumes with one command instead of re-reading the repo — and instead of trusting a long conversation's memory.
• Progressive disclosure. The skill narrates the whole loop itself and loads a deeper phase reference only when the beat needs it — the context window stays lean.

Where ADD fits vs. skill libraries (e.g. agency-agents)

ADD is the trust layer — the gated loop (Direction → Build → Verify) that decides when work is trusted, and the on-disk memory it runs on. It is not a catalog of ready-made expert personas. Skill libraries like agency-agents, or role-specific subagents (a backend expert, a security reviewer, a senior Java engineer), sit at a different layer: they answer who does the work — a domain stance, vocabulary, and craft rules for one kind of task. ADD answers how you trust what gets built, no matter who or what wrote it.

The two layers compose; they don't compete. ADD's persona loop distills a lean, project-fit persona from a teacher corpus like agency-agents — vendored at personas-teacher/, read off-build by the AI while drafting a persona, never a runtime dependency — down to the three parts a project actually needs: Identity (the stance), Critical Rules (the non-negotiables), and Success Metrics (the done-bar). The project then owns that persona outright.

A distilled persona is applied as an advisory overlay during direction, build, or verify — it shapes how a step gets done, never whether it happens: it can't skip a gate, edit a frozen contract, or wave through a security finding. In 2.0 personas also carry task-kinds and propose each task's route (full walk · fast lane · inline); the freeze ratifies the route, the gate traces the outcome, and add.py deltas rolls the traces into a per-lane scoreboard the loop reflects on (GEPA). That gated loop is what ADD contributes underneath any persona, distilled or not.

Best setup: ADD alongside other agent libraries

1. Install ADD (below) — it drives the loop: which phase you're in, what needs your approval, whether something is proven.
2. Keep whatever subagent libraries you already use. ADD ships ONE add agent in the same .claude/agents/ mechanism as any other Claude Code subagent — the spawn names the mode (direction · build · verify · advise · persona) and the agent loads that beat's guide plus the best-fit persona. It coexists with a distilled persona, an agency-agents-derived specialist, or a built-in expert with zero conflict; nothing is replaced.
3. Prefer the add agent for anything phase-shaped — spawn it in verify mode for the independent adversarial refute-read, in build mode for a red→green batch — before an ad-hoc spawn. Reach for another specialist when a piece needs deep domain expertise the phase guide doesn't carry (a Java-specific review, a payments-domain lens).
4. The gates hold no matter who did the work. A delegated subagent proposes; the orchestrating agent records. A security finding is always a HARD-STOP, and a low self-reported confidence means refine or re-spawn — never a pass — whichever subagent produced it.

Install

Pick your ecosystem — all three install the same skill and tooling:

# Node / npm
npx @pilotspace/add init

# Python / pip
pip install pilotspace-add
pilotspace-add init

# Claude Code plugin — no npm or pip needed
/plugin marketplace add pilotspace/ADD
/plugin install add@add-method

The plugin carries the engine. On first /add, the skill materializes it into the project (node "${CLAUDE_PLUGIN_ROOT}/bin/cli.js" init --no-skill) and scaffolds .add/ — a self-contained, portable result identical to the npm/pip flow. The skill stays in the plugin, so nothing is duplicated.

No flags needed — the project name is inferred from your folder and the stage defaults to prototype (pass --name "My App" --stage mvp to choose up front).

Already installed? Refresh to the latest without a re-install — npx @pilotspace/add@latest update (or pipx run pilotspace-add update) re-materializes the skill and tooling while leaving your project work (.add/state.json, PROJECT.md, milestones, tasks) untouched; add --check to see whether a project is behind the installed package. Coming from 1.x? One command — python3 .add/tooling/add.py migrate — converts the whole board (task docs to PLAN.md, living 5-DD specs seeded); it's idempotent and refuses to guess on conflicts.

New here? Follow the 10-minute Quickstart — it walks your first feature end to end.

This installs:

Path	What
.claude/skills/add/	the add skill Claude loads — the loop itself, plus three on-demand phase references
.add/tooling/add.py	the state kernel — scaffolder + tracker, 31 verbs (Python, stdlib only)
.add/personas-teacher/	the vendored teacher corpus personas are distilled from (off-build reading, never runtime)
.add/DESIGN.md	(UI projects) the prose front-door to the render-ready UDD foundation — delete it if your project has no UI

The AIDD book itself no longer installs into projects — it lives online at pilotspace.github.io/ADD, and the engine deep-links the exact chapter whenever a gate or guide cites its rationale.

On a UI project, UDD gives the AI a frozen design ground to draft from: DESIGN.md plus a lintable JSON foundation under .add/design/ (design tokens · component catalog · prototype trees). add.py check lints that foundation, going red with a named code on any layer, catalog, tree, or cross-file violation — and staying silent when a project has no design set.

Project state (.add/state.json) and the living-documentation files (CONVENTIONS.md, GLOSSARY.md, MODEL_REGISTRY.md, dependencies.allowlist, SOUL.md — the AI's human-owned voice) are not created here — the installer drops files only; initialisation is the agent's first move when you run /add.

What this plugin does, writes, and runs (boundaries)

ADD is a development methodology, so by design it works inside your project — here is exactly what that means, so there are no surprises:

• Runs only when you ask. Nothing executes on install. The skill acts when you run /add (or another agent follows the guideline block). It is user-initiated, every time.
• What it runs: the bundled engine and bootstrapper only — node bin/cli.js and python3 .add/tooling/add.py. No downloaded or remote code is executed; everything it runs ships in the package.
• What it writes: files under your project's .add/ (state, milestones, tasks) and the managed guideline block in CLAUDE.md / AGENTS.md. On a plugin install it also materializes the engine into .add/ on first run. It writes nowhere outside the project working directory; it never touches files above the project root.
• Network: one optional, advisory update check. On status / guide the engine may make a single HTTPS GET to https://registry.npmjs.org/@pilotspace/add/latest to see if a newer version exists — at most once per 24h (cached in .update-cache.json), 1.5s timeout, fail-open (offline ⇒ silent no-op). It only writes a one-line note to stderr and never changes a command's output or exit code. Disable it entirely with ADD_NO_UPDATE_CHECK=1. No other network access, no telemetry, no analytics.
• No secrets, no credentials, no privileged access. Pure local file orchestration.

Use it

ADD is AI-first: you talk to the agent; it drives the method. In Claude Code, run /add and say what you want to build:

/add — "Let users log in with email + password / SSO, and keep them signed in for 30 days unless they explicitly log out."

Works with your agent. The installer detects which coding agent you're in and drops the context file it reads — so ADD drives through the CLI under Claude Code, Codex, OpenCode, Cursor, Windsurf, Trae, Gemini CLI, GitHub Copilot, Cline, and Aider (anything else falls back to a generic AGENTS.md). Only Claude Code runs the /add skill natively; every other agent follows the same loop through the phase guides via add.py status / guide.

The agent orients from state.json, sizes your request into a milestone (you confirm the shape), then drafts each task's specification bundle — Spec + Scenarios + Contract + Tests as one Direction pass — and you give one approval at the frozen contract. A self-driving build→verify run takes it to green; security findings always stop back to you.

Under the hood the agent runs the CLI as its hands — and you can hand-drive it too:

python3 .add/tooling/add.py status      # where am I? (resume point)

The non-negotiables

1. Direction before speed — no Build until spec, scenarios, contract, and red tests exist.
2. Trust evidence, not inspection — a feature is trusted because its tests pass and the non-functional risks (concurrency, security, architecture) were checked.
3. Never weaken a test or edit a frozen contract to make the build pass.
4. No silent skips — every Verify records PASS, RISK-ACCEPTED, or HARD-STOP. Security findings are always HARD-STOP.
5. Ask, don't guess.

The artifacts survive; the code is disposable

The durable asset is the decisions — spec, scenarios, contract, tests. The code is one implementation that satisfies them and can be regenerated. If the thing you'd be upset to lose is "the code," you're still working the old way. This is also the anti-context-rot stance in one sentence: the artifacts on disk, not the conversation, are the project's memory.

Read the method

Start at the book — Foundations → the loop → operating it across a team → templates, prompts, and a full worked example.

More entry points:

• 📖 Read the book online — the full AIDD method, chapter by chapter
• 🔍 Full hands-on walkthrough — one real feature, end to end
• 📊 Benchmark results — every trust and cost claim, measured
• ⚖️ ADD vs spec-kit — the honest comparison — where we tie, where they win, what only ADD guarantees
• 🗞️ ADD Across the Org: AI-Driven Development Beyond Code

What's new in 2.0

• Skill-led loop on a thin kernel — the skill narrates Direction → Build → Verify; add.py shrinks to a 31-verb state kernel (54 → 31).
• PLAN.md + one-shot migrate — the per-task file is the plan; a whole 1.x board converts with one idempotent command.
• Five living 5-DD specs with delta-append — the project's evolving truth, compacted forward instead of rewritten.
• Persona routes + the GEPA scoreboard — personas propose lanes, gates trace outcomes, deltas shows the per-lane record, and the loop reflects on it.
• The book stops shipping — published online only, deep-linked from the engine.
• Measured: every trust floor 1.0 across the 3-milestone benchmark at $2.20 per trusted milestone; plus a new --session-mode continue harness that measures context rot itself.

Develop

npm test     # runs the Python tests for the tooling (red/green)

License: MIT.