clauderizer 0.2.1

Drop-in, MCP-native working memory for AI agents: gameplans, phases, a dependency graph, and post-hoc cascade — over plain markdown.

Clauderizer

Durable, structured memory for AI agents — drop it into any repo in one command.

uvx clauderize init

Clauderizer gives a stateless coding agent something it normally lacks: a memory that survives the end of the session and the end of the context window. Plans, decisions, invariants, a dependency graph, and findings live as plain, git-diffable markdown — and an MCP server turns reading and updating that memory into real tool calls the agent can just use.

It's the active, MCP-native successor to a battle-tested markdown "gameplan" convention — and it was built by dogfooding itself: Clauderizer planned and tracked its own construction.

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The problem it kills

Long-running AI work hits two walls:

1. A fresh session starts blank. Every "where were we?" is re-derived from scratch — or re-explained by you for the tenth time.
2. A long session fills its context window mid-task and forgets its own earlier decisions.

The usual answer is conventions: "keep a decisions log," "write a handoff," "run cascade after edits." But conventions rely on the agent remembering to follow them, so they rot — the cascade script never gets built, lessons stop propagating, the plan quietly drifts from reality. (If you've watched an agent confidently contradict a decision it made an hour ago, you know the failure mode.)

Clauderizer keeps the proven model of that convention and makes it a system instead of a hope:

Goal	How
🔎 Discoverable	Self-describing MCP tools + a SessionStart hook that injects current status into context. No "read these 7 files in this order" ritual.
🎛️ Configurable	A real pet / standard / saas size dial and host-language profiles — data, not prose advice.
🤖 Agentic	Cascade, pre-flight, and handoff assembly are real tool calls — not instructions the agent has to remember to run.
📦 Drop-in	One command clauderizes any repo, in any language.

Markdown is the source of truth. The graph index is a disposable cache rebuilt from the markdown on demand — if they ever disagree, markdown wins. No database, no lock-in: your memory is just readable files in docs/.

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What a session actually feels like

You open a fresh agent session in a clauderized repo. Before you type anything, the SessionStart hook has already injected:

[Clauderizer] Gameplan 2026-06-05-launch: Phase 3/8 IN PROGRESS — "Wallet review".
Baseline: 142 tests passing. Pending cascades: 0.
Next: cz_next_phase_context, then cz_preflight.
Tools: cz_status, cz_next_phase_context, cz_cascade, cz_preflight, cz_write_handoff, …

The agent now knows where things stand and what to do next — no reading order, no re-briefing. A typical loop:

cz_next_phase_context   → the full bundle for this phase (read-only; assembles, doesn't write)
cz_preflight            → runs the host project's real tests/build/lint, for real, not "claimed"
… do the work …
cz_add_decision / cz_add_invariant / cz_add_finding / cz_upsert_entity   → structured records
cz_cascade              → walk the graph, flag dependents that may need updating
cz_write_handoff        → assemble the next cumulative handoff (all still-relevant lessons)

Every write goes through one idempotent path, so IDs auto-number and frontmatter stays valid — the agent never hand-mangles your docs.

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Quickstart: from an empty folder

Brand-new project with nothing in it — not even Clauderizer installed? Three steps.

1. Make the repo and clauderize it. uvx runs Clauderizer without installing anything:

mkdir my-project && cd my-project
git init                                # recommended — pre-flight uses git
uvx clauderize init --size standard     # scaffolds docs/, CLAUDE.md, skills, MCP server, hook

No language to detect yet, so the host profile starts as generic. Once your first phase adds a package.json / pyproject.toml / go.mod / Gemfile, just re-run clauderize init (it's idempotent) and the profile switches to the real one, enabling pre-flight tests.

2. Open a Claude Code session in the folder. The SessionStart hook greets the agent with the (currently empty) status automatically — no setup prompt needed.

3. Write the very first prompt. Point the agent at the goal and let it build the plan:

Plan a gameplan to <one sentence: what you're building>. It's a brand-new <stack, e.g. "TypeScript + Vite"> project with nothing in it yet. Ask me whatever you need to pin down scope, then create the gameplan: capture the key decisions and break it into session-sized phases — start with a phase that scaffolds the project. Show me the plan before we execute.

That fires the new-gameplan skill: the agent clarifies scope with you, records the decisions, lays out the phases, and writes the Phase 0 handoff. From there every session is just:

Do the next phase.

Phase 0 typically scaffolds the project itself, so by the time it's done you have a real, building codebase tracked by a real plan — and the memory to resume it cleanly in any future session.

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Quickstart: into an existing project

Already have a codebase? Clauderizer drops in without disturbing it — init is idempotent and never clobbers your files: it adds a marker stanza to an existing CLAUDE.md, key-merges .mcp.json, and skips any doc that already exists.

1. Clauderize the repo. Run it on a clean working tree (or a fresh branch) so the change is easy to review:

cd my-existing-repo
uvx clauderize init --size standard     # auto-detects node / python / go / ruby
clauderize doctor                       # confirm it's wired AND runnable

Because your project already has a language, pre-flight runs your real test/build/lint commands from the start (tweak them in .clauderizer/profile.lock.toml if your repo is non-standard). Commit this diff on its own branch/commit so adopting Clauderizer is a clean, reviewable change in your history.

2. Open a Claude Code session. The hook injects status (no gameplan yet).

3. (Recommended) Seed memory from what already exists — so the graph reflects reality before you build on it:

Before we plan anything, skim this codebase and seed Clauderizer's memory: capture the major subsystems as entities, the load-bearing decisions and invariants you can infer, and the real baseline (current test count, language/framework versions) as source-of-truth. Don't change any code — just record.

4. Plan your first initiative — the first prompt points at a feature or change on top of the existing code:

Plan a gameplan to <the initiative, e.g. "add OAuth login">. This is an existing <stack> codebase — read the relevant code first, capture source-of-truth values, record the key decisions, and break it into session-sized phases with verifiable exit criteria. Show me the plan before we execute.

Then, as always:

Do the next phase.

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Working with gameplans (how you actually drive it)

You talk to the agent in plain English; Claude does the tool calls. You almost never invoke a cz_* tool yourself — the six skills init installs auto-trigger on what you say (and also work as slash commands, e.g. /clauderizer-do-phase). The only thing that's fully automatic is the SessionStart hook: every session opens with the current status already in context, so the agent knows where things stand before you type a word.

The whole lifecycle is just natural language:

You say…	Skill	What the agent does
"Plan a gameplan to ship the billing system"	new-gameplan	clarifies the goal, captures real source-of-truth values, then cz_create_gameplan → cz_add_decision → cz_add_phase ×N → writes the Phase 0 handoff
"Do the next phase" · "continue" · "work on phase 3"	do-phase	cz_next_phase_context → cz_preflight (runs your real tests/build) → does the work → records outcomes → cz_cascade → cz_write_handoff
"Remember we decided X" · "note that…" · "that was a mistake, the fix is…"	record	routes to the right cz_add_decision / cz_add_invariant / cz_add_lesson / cz_add_correction / cz_add_finding
"Scope changed — add a task for Y"	amend	cz_add_amendment, cascading to affected phases
"Close out the gameplan"	close-gameplan	full cascade, updates project docs, writes a POST-MORTEM.md
"Where are we?"	—	cz_status (or just read what the hook already injected)

So the day-to-day rhythm is:

clauderize init                         # once, per repo
"Plan a gameplan to <your goal>"        # the agent breaks it into phases
"Do the next phase"                     # …repeat each session until done
"Remember that <decision/lesson>"       # capture as you go; it propagates forward
"Close out the gameplan"                # when every phase is done

You steer; Claude keeps the memory, the graph, and the rituals honest between sessions.

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Install

pipx install "clauderizer[mcp]"   # regular use (engine + MCP server)
# or zero-install:
uvx clauderize init

The core engine has no runtime dependencies; the mcp extra is only needed to run the MCP server. init prefers the installed clauderizer-mcp/clauderizer-hook scripts (venv / pipx) and falls back to uvx only when nothing's on your PATH — so it wires up correctly on native Linux, macOS, and Windows→WSL alike.

What init drops in

your-repo/
├── CLAUDE.md                    # a Clauderizer stanza (between markers — your text is preserved)
├── .mcp.json                    # registers the clauderizer MCP server
├── .claude/
│   ├── settings.json            # SessionStart hook
│   └── skills/clauderizer-*/    # six workflow skills (/do-phase, /cascade, /record, …)
├── .clauderizer/
│   ├── config.toml              # size dial + host profile + active gameplan
│   ├── profile.lock.toml        # per-project test/build/lint commands (editable override)
│   └── index.json               # disposable graph cache (gitignored)
└── docs/                        # the canonical markdown memory
    ├── VISION.md, ARCHITECTURE.md, DECISIONS.md, INVARIANTS.md, HARDENING.md, …
    └── gameplans/
        ├── GAMEPLAN-PROCEDURE.md
        └── <date>-<name>/       # GAMEPLAN, handoffs, cascade reports, status

init is idempotent: re-running fills gaps and refreshes engine-owned files but never clobbers your content (marker blocks, key-scoped JSON merges, exists-checks). Run clauderize doctor any time to verify the install is not just present but actually runnable (it probes that the MCP/hook commands can launch — a green check on a broken setup is worse than no check).

The model

• Gameplan → Phase → Task — a coherent initiative, broken into session-sized phases, broken into tasks.
• Project DAG — long-lived entities (subsys.*, feat.*, ext.*, D-NNN, INVARIANT-NN) declared via frontmatter; edges are depends_on (with semver pins). Query it with cz_graph_query.
• Cascade — after a tracked change, walk the DAG forward and reconcile dependents. It's post-hoc and judgment-based: the tool finds what might be affected and marks it "needs review"; the agent decides. (No more "I'll build the cascade script later" — it's built in.)
• Cumulative handoffs — every handoff carries all still-relevant lessons forward, assembled from one canonical list, so phase N+3 never repeats a mistake phase N already solved.
• Append-only memory — decisions, invariants, hardening findings, incidents, corrections, and lessons are never deleted, only superseded. A permanent audit trail.

CLI

clauderize init [--size pet|standard|saas] [--profile auto|node|python|go|ruby]
                [--gameplan "Name"] [--run-cmd "uvx --from clauderizer"]
clauderize status [--json]   # the current digest
clauderize doctor            # verify the install is present AND runnable; report drift
clauderize reindex           # rebuild the graph cache from markdown
clauderize mcp               # launch the MCP server (stdio)

MCP surface

Read · cz_status · cz_next_phase_context · cz_graph_query Rituals · cz_preflight · cz_cascade · cz_write_handoff Mutations · cz_create_gameplan · cz_add_phase · cz_add_amendment · cz_add_decision · cz_add_invariant · cz_add_finding · cz_add_lesson · cz_add_correction · cz_upsert_entity · cz_transition_status Resources · clauderizer://status · clauderizer://procedure · clauderizer://entity/{id}

The tools are deliberately separate and self-describing rather than one generic mutate — that's the whole point of going MCP-native: an agent dropped into the repo discovers the workflow from the tool schemas, no documentation pass required.

Configurable two ways

Size dial — pet (just a gameplan + handoffs), standard (named docs + cascade + full 7-check preflight), saas (the full doc set + incidents + amendments). It governs which doc modules and rituals are active. It's a data manifest, not a fork.

Host-language profiles — the engine reads/writes markdown and is agnostic to your project's language. Support for a language is a profile: a data file describing its test/build/lint/ typecheck commands. Ships with Node, Python, Go, Ruby, and a generic fallback; need per-project commands? Edit .clauderizer/profile.lock.toml and they take effect. Adding a language is a new <lang>.toml, not code.

Development

uv venv && . .venv/bin/activate
uv pip install -e ".[mcp,dev]"
pytest

Releasing (maintainers)

Publishing is automated and tokenless via PyPI Trusted Publishing (OIDC) — no secrets stored.

One-time setup on PyPI → the clauderizer project → Publishing → add a trusted publisher: owner collincusce, repo Clauderizer, workflow publish.yml, environment pypi.

Then for each release: bump version in pyproject.toml + __version__, update the CHANGELOG, and cut a GitHub Release. The publish.yml workflow builds, runs twine check, and uploads to PyPI automatically. (uv build locally to sanity-check first.)

License

MIT.