Overview

Meta_Kim is not another AI coding tool. It is the governance layer for AI coding work.

The hard part of AI coding is no longer getting a model to change files. The hard part is deciding what should happen first, which capability should own it, what evidence proves it worked, and how the lesson survives the next run.

Claude Code, Codex, OpenClaw, and Cursor are all hands: they can write code and change files. But who decides which file to change first? Who reviews the result? Who fixes the problems that show up? And how do we make sure the same mistake does not repeat next time?

Meta_Kim is built for that. It is AI above AI: a unified governance layer that keeps complex work from turning into a mess.

One-line summary

First clarify what needs to happen -> then decide who should do it -> review after execution -> preserve what was learned -> feed that back into the next run.

This is not a new concept. Mature engineering teams already do this. Meta_Kim turns it into a runnable system instead of relying on human discipline alone.

Before / After

Without Meta_Kim	With Meta_Kim
One giant chat response tries to do everything	Work is routed through intent, capability, owner, review, verification, and writeback
A tool is chosen because it is available	A capability is selected because it fits the task, runtime, OS, dependency, and risk
Passing commands get mistaken for success	Evidence is checked against the user's real goal
Good fixes disappear into chat history	Reusable lessons become governed skills, agents, scripts, contracts, or run-scoped tasks

3-minute proof

Meta_Kim is easiest to understand by watching one governed run, not by reading every rule.

npm run meta:theory:run
npm run meta:theory:report -- --run-id latest
npm run meta:delivery:bundle

The proof path shows five things:

• a fuzzy request is turned into an explicit intent and success standard
• capability search happens before execution ownership is chosen
• work is split into bounded worker tasks instead of one giant chat response
• review and verification produce artifacts, not just reassuring prose
• blocked runtime evidence stays blocked, for example Cursor native live is not promoted from smoke evidence

For a guided walk-through, start with examples/first-run/README.md.

Quick Start

If you just want to try it quickly, run:

npx --yes github:KimYx0207/Meta_Kim meta-kim

Or install it the traditional way:

git clone https://github.com/KimYx0207/Meta_Kim.git
cd Meta_Kim
npm install
node setup.mjs

💡 After install: setup.mjs prints where every artifact lives. To revisit that summary anytime (or diff vs. the previous install), run npm run meta:status in the directory where you installed.

If you plan to maintain the repository, edit the canonical sources first: canonical/agents/, canonical/skills/meta-theory/, config/contracts/, and config/capability-index/. Then run (requires Node.js >= 22.13.0):

npm run meta:sync
npm run meta:validate

Recommended reading order:

1. This file, README.md
2. AGENTS.md
3. CLAUDE.md when working on Claude Code behavior
4. canonical/runtime-assets/cursor/rules/meta-enforcement.mdc when working on Cursor rules

Usage Paths

After global install (node setup.mjs or npx), what works where:

Where you are	What works automatically	What needs explicit trigger
Meta_Kim repo with Claude Code	Full governance via CLAUDE.md (8-stage spine, gates, dispatch rules)	—
Any other project with Claude Code	Hooks (safety, format, memory save) + /meta-theory skill	Say "run meta theory" or type /meta-theory
Codex	AGENTS.md rules + 9 custom agents + /meta-theory command	Type "run meta theory" or /meta-theory
OpenClaw	Workspace agents + internal lifecycle hooks; tool-blocking policy needs a typed plugin adapter	Requires OpenClaw config/auth
Cursor	Official subagents, .cursor/rules, hooks, and MCP mirrors	Lightweight; mainly read + review

Platform Support Tiers

Meta_Kim now tracks platform support in tiers instead of treating every compatible surface as a full runtime projection.

Tier	Products	What it means
Formal runtime projections	Claude Code, Codex, OpenClaw, Cursor	Canonical governance is projected into runtime-specific files and checked by npm run meta:sync / npm run meta:check.
Native dependency install targets	opencode, Qwen, Zed, Gemini, CodeBuddy, Antigravity, JoyCode	ECC supports these through its upstream installer, but Meta_Kim does not claim a full runtime projection until profile, layout, sync, and tests exist.
Candidate probes	Qoder CLI	Official Qoder docs expose skills, subagents, hooks, and MCP surfaces. Meta_Kim records it as a candidate probe, not a formal supported runtime yet.

Source of truth: config/runtime-compatibility-catalog.json.

---

Contact

GitHub KimYx0207 |
X @KimYx0207 |
Website aiking.dev |
WeChat Official Account: 老金带你玩AI

Feishu knowledge base:
long-term updates

Buy me a coffee

If Meta_Kim has been useful, support the project with a coffee.

WeChat Pay	Alipay

Method basis

Meta_Kim’s methodological foundation comes from research on meta-based intent amplification, authored by this project’s maintainer (KimYx0207):

• Paper: https://zenodo.org/records/18957649
• DOI: 10.5281/zenodo.18957649

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Architecture: Hidden Skeleton + Dynamic Dealing

This is the core design idea of Meta_Kim. If you only read one section, read this one.

First, split the core terms so they do not get mixed up later

Concept	What it is	What it is not
Hidden skeleton	The backend framework that always exists under the visible workflow	A fixed list of responsibilities written in advance
8-stage workflow	The human-readable execution spine exposed by the hidden skeleton	The whole governance logic
11-phase business workflow	A run-packaging progression layered on top of the 8 stages after classification	A replacement for the 8 stages
Dealing	Dynamic control built around the 8-stage workflow and agent units	Simple task assignment
Gate	A pass/fail condition	The stage itself
Contract	The structured output a node must produce	Slogans or abstract values
Agent-unit governance	A practical way to manage boundaries, capabilities, upgrades, and rollback	A role menu
Three-layer memory	Long-term memory split across memory / graphify / SQL	One mixed notebook

If you only want one sentence to remember:

The 8-stage workflow moves execution forward, gates decide whether a stage can pass, contracts define the required outputs, and dealing adds dynamic intervention.

8 stages = the hidden skeleton

Meta_Kim has 8 fixed execution stages. This is the hidden skeleton:

flowchart LR
    C[Critical<br/>Clarify the request] --> F[Fetch<br/>Search capabilities]
    F --> T[Thinking<br/>Plan the approach]
    T --> E[Execution<br/>Dispatch the work]
    E --> R[Review<br/>Inspect the result]
    R --> MR[Meta-Review<br/>Review the review]
    MR --> V[Verification<br/>Verify reality]
    V --> EV[Evolution<br/>Write back lessons]

    style C fill:#fbbf24,color:#000
    style F fill:#34d399,color:#000
    style T fill:#60a5fa,color:#000
    style E fill:#f87171,color:#fff
    style R fill:#a78bfa,color:#fff
    style MR fill:#a78bfa,color:#fff
    style V fill:#34d399,color:#000
    style EV fill:#fbbf24,color:#000

Critical - pin down the real problem first

When the request is vague, ask clarifying questions instead of guessing. This stage produces intentPacket, which locks down the real user intent, success criteria, and exclusions. If the request is already clear, the system records an explicit skip reason instead of quietly skipping.

Fetch - search existing capabilities before inventing new ones

Search whether existing agents, skills, tools, or MCP integrations already cover the need. The core idea here is capability-first: define the capability first, then search for the owner that declares it, then dispatch to the best match. Capability-index lookup goes config/capability-index/ -> runtime mirror -> local inventory -> fallback. Do not start by hardcoding a specific agent name.

Governance Decision Engine

Meta_Kim is not only the 8-stage spine. It first identifies the governance trigger, checks runtime and OS capability, checks dependency capability, separates owner from weapon, filters by Win/Mac/runtime support, asks the user only for branch-changing choices, executes deterministic parts, verifies whether the user goal actually landed, and writes reusable learning back. Reference-only projects are absorbed into Meta_Kim data, not silently promoted into dependencies; see config/governance/decision-pattern-catalog.json.

Thinking - define boundaries, owners, sequence, deliverables, risks, and stop conditions

Break the task into subtasks, assign owners, and make dependencies and parallel groups explicit. This stage produces a dispatchBoard: who does what, what can run in parallel, and who is responsible for merging the result. At least two solution paths should be explored; do not lock into a single route too early.

Execution - produce the actual work while still under governance

Dispatch the subtasks to specialist agents. Each subtask is wrapped in a workerTaskPacket, including file context, constraints, review owner, and verification owner. Independent subtasks should run in parallel when possible. Execution is not completion - the output still has to pass review and verification.

Review - check quality and boundary compliance

Inspect code quality, security, architecture compliance, and boundary violations. Produce a structured reviewPacket with findings. Each finding has a severity from CRITICAL to LOW. This is not a formality - unresolved findings cannot move forward.

Meta-Review - inspect whether the review standard itself is biased or too loose

Review the review. If the review standard is too weak, the system is not really reviewing. If it is biased, it is reviewing the wrong thing. This stage protects the quality of the review system itself.

Verification - confirm that reality matches the claim

Verify whether the fixes really closed the review findings. This stage produces verificationResult and closeFindings. If the fix did not actually close the finding, go back and repair it before verifying again. This is the most honest gate in the system.

Evolution - write capability gaps and reusable patterns back into the system

Convert experience into structural upgrades: reusable patterns go into memory, failures become learning artifacts, capability gaps are handed to Scout, and agent boundaries are written back into canonical sources. Every run must end with a writebackDecision: either write back something concrete or explicitly explain why there is nothing to persist. A run that does not preserve learning is wasted work.

---

The 8 stages together form the execution spine.

Why are they only "relatively" fixed? Because some stages can be skipped in simple cases - but the system must explicitly record why they were skipped. Nothing is skipped silently.

11 phases = a business workflow built on the skeleton

If the 8-stage workflow is the skeleton, then the 11-phase business workflow is the run-packaging progression that grows on top of it:

direction -> planning -> execution -> review -> meta_review -> revision -> verify -> summary -> feedback -> evolve -> mirror

It is not a second system. It is derived from the 8-stage skeleton. The difference is:

• The 8 stages focus on execution logic - "what order should work happen in"
• The 11 phases focus on business governance - "what each phase must deliver, how completion is defined, and when mirrors must be refreshed"

flowchart TB
    subgraph spine["8-stage workflow (hidden skeleton)"]
        direction LR
        C1[Critical] --> F1[Fetch] --> T1[Thinking] --> E1[Execution] --> R1[Review] --> MR1[Meta-Review] --> V1[Verification] --> EV1[Evolution]
    end

    subgraph workflow["11-phase business workflow"]
        direction LR
        D2[direction] --> P2[planning] --> EX2[execution] --> RE2[review] --> MET2[meta_review] --> REV2[revision] --> VER2[verify] --> SUM2[summary] --> FB2[feedback] --> EVO2[evolve] --> MIR2[mirror]
    end

    C1 -.-> D2
    F1 -.-> P2
    T1 -.-> P2
    E1 -.-> EX2
    R1 -.-> RE2
    MR1 -.-> MET2
    V1 -.-> VER2
    EV1 -.-> EVO2

    style spine fill:#1e1b4b,stroke:#7c3aed,color:#e0e7ff
    style workflow fill:#14532d,stroke:#22c55e,color:#dcfce7

The 11-phase business workflow adds revision, summary, feedback, and mirror, so the process is not only about "getting it done" but also about getting it done well, closing the loop correctly, and keeping runtime projections aligned.

Contracts = what each node must deliver

Workflow alone is not enough. Each stage also needs to define what it must output. That is what the contracts do.

Meta_Kim contracts are not verbal agreements. They are structured packets:

Contract artifact	Stage	Purpose
intentPacket	Critical	Lock the real intent and prevent drift
dispatchBoard	Thinking	Define owners, dependencies, and parallel groups
workerTaskPacket	Execution	Carry the full context for each subtask
reviewPacket	Review	Record structured findings
revisionResponse	Revision	Respond to each review finding
verificationResult	Verification	Confirm whether the issue was actually closed
summaryPacket	Summary	Final summary before public release
evolutionWriteback	Evolution	Define what should be written back

flowchart LR
    subgraph packets["Contract artifact flow"]
        direction LR
        IP[intentPacket<br/>Intent lock] --> DP[dispatchBoard<br/>Dispatch board]
        DP --> WTP[workerTaskPacket<br/>Task packet]
        WTP --> RP[reviewPacket<br/>Review findings]
        RP --> RR[revisionResponse<br/>Revision response]
        RR --> VR[verificationResult<br/>Verification result]
        VR --> SP[summaryPacket<br/>Final summary]
        SP --> EW[evolutionWriteback<br/>Learning writeback]
    end

    IP ~~~ C2["Critical"]
    DP ~~~ T2["Thinking"]
    WTP ~~~ E2["Execution"]
    RP ~~~ R2["Review"]
    RR ~~~ REV2["Revision"]
    VR ~~~ V2["Verification"]
    SP ~~~ S2["Summary"]
    EW ~~~ EV2["Evolution"]

    style packets fill:#1a1a2e,stroke:#e94560,color:#fff

These artifacts are not optional documents. They are the system’s source of truth. Without contracts, the next node is not "handing off" - it is guessing what the previous node meant. That is why so much AI collaboration falls apart on complex work.

The current implementation carries these artifacts explicitly: taskClassification before execution, cardPlanPacket before dealing, dispatchEnvelopePacket before dispatch, reviewPacket.findings after review, revisionResponses + verificationResults + closeFindings between revision and verification, summaryPacket before external publication, and writebackDecision before evolution.

npm run meta:validate:run checks whether these artifact chains close completely.

Gates = stage reached does not mean stage passed

Contracts define what each node must deliver. Gates define whether that delivery is good enough to move forward.

In one sentence:

A stage tells you where you are; a gate tells you whether you are allowed to move on.

flowchart LR
    A["Reach a stage"] --> B{"Gate decision"}
    B -->|Pass| C["Release: move forward"]
    B -->|Fail| D["Revision: add evidence / fix output"]
    B -->|Hold| E["Pause: wait for conditions to mature"]
    B -->|Escalate| F["Higher-level intervention"]

    style A fill:#dbeafe,stroke:#2563eb,color:#000
    style B fill:#7c3aed,stroke:#4c1d95,color:#fff
    style C fill:#dcfce7,stroke:#16a34a,color:#000
    style D fill:#fee2e2,stroke:#dc2626,color:#000
    style E fill:#e0f2fe,stroke:#0284c7,color:#000
    style F fill:#fef3c7,stroke:#f59e0b,color:#000

Key gates in the system:

Gate	What it blocks	Pass condition
planning gate	Moving from planning into execution	Boundaries, owners, deliverables, and risks are defined
metaReview gate	Whether meta-review is strong enough	The review standard itself is not biased, missing, or too loose
verify gate	Whether the fix really closed the issue	finding -> revision -> verification closes cleanly
summary gate	Whether the result can be published	Verification passed + summary completed
publicDisplay gate	Whether the system can claim "done"	verifyPassed + summaryClosed + singleDeliverableMaintained + deliverableChainClosed

The most important one is the publicDisplay gate. If verification has not passed, the summary is not closed, or the deliverable chain is broken, the system cannot pretend that the work is finished.

The relationship between gates and contracts:

• Contracts answer "what must this node deliver" - they are about delivery obligations
• Gates answer "is this good enough to move forward" - they are about release decisions
• Without contracts, gates have nothing to judge
• Without gates, contracts are just a ceremony

Dynamic dealing = flexibility layered on top of the skeleton

The 8-stage skeleton is relatively fixed, but real tasks vary too much to be handled by one rigid path. That is why Meta_Kim introduces dynamic dealing.

Dealing corresponds to the 8 stages, but not as a simple 1:1 map. The 10 cards are:

Card	Trigger condition	Attention cost
Clarify	The request is vague	Low
Shrink scope	The repository is too large or has too many files	Low
Options	The request is clear but there are many possible paths	Medium
Execute	The plan is decided	High
Verify	Execution is complete	Medium
Fix	Verification failed	Medium
Rollback	Risk is spreading	High
Risk	Security, global, or multi-party impact is involved	High
Nudge	The user is stuck and needs a light push	Low
Pause	Three high-cost cards have been used in a row	Zero

The important part is that some cards are dynamic:

• When three high-attention cards are dealt consecutively, the system forcibly inserts Pause - it does not wait for the user to notice
• When security risk appears, Risk preempts the current flow
• When the user already knows something, the corresponding card is skipped
• When task iteration exceeds the upper bound, the system escalates to Warden adjudication

Dynamic dealing gives the fixed skeleton some breathing room: strict where it must be strict, flexible where flexibility helps.

flowchart TD
    START[Current card completed] --> SKIP{Check next card<br/>skip_condition}
    SKIP -->|Satisfied, skip| NEXT[Continue to the next card]
    SKIP -->|Not satisfied| INTR{Check interrupt queue}
    INTR -->|Security risk preempts| RISK[Risk card<br/>highest priority]
    INTR -->|No preemption| PAUSE{Three or more<br/>high-cost cards?}
    PAUSE -->|Yes, force a break| P[Pause card<br/>zero attention]
    PAUSE -->|No| DEAL[Deal by priority]
    RISK --> DEAL
    P --> DEAL
    DEAL --> COUNT{Iterations over<br/>max_iterations?}
    COUNT -->|Yes| WARDEN[Escalate to Warden adjudication]
    COUNT -->|No| START

    style RISK fill:#dc2626,color:#fff
    style P fill:#1e3a5f,color:#93c5fd
    style WARDEN fill:#7c3aed,color:#fff
    style DEAL fill:#16a34a,color:#fff

Closed loop = iterate, generate, improve

Once the skeleton, progression workflow, contracts, and dynamic dealing are in place, the system forms a closed loop:

Request arrives -> skeleton starts -> dealing decision -> dispatch execution -> review and verify -> preserve lessons -> upgrade agents -> next run starts stronger

The loop is not one-and-done. Each round can:

1. Generate the missing agent - if a capability gap appears, the system can create a new agent through the Type B pipeline
2. Improve agent capability - Evolution writes back changes to SOUL.md, skill loadouts, and toolchains
3. Clarify every agent’s boundary - each agent owns one class of work; boundary violations are intercepted by Sentinel

flowchart TD
    INPUT[Request arrives] --> SPINE[Hidden skeleton starts]
    SPINE --> CARD[Dynamic dealing decision]
    CARD --> DISPATCH[Dispatch to specialist agent]
    DISPATCH --> REVIEW[Review + verification]
    REVIEW --> |Pass| EVOLVE[Preserve lessons]
    REVIEW --> |Fail| FIX[Fix + review again]
    FIX --> REVIEW
    EVOLVE --> UPGRADE[Upgrade agent capability]
    UPGRADE --> |Capability gap found| CREATE[Type B pipeline<br/>auto-create new agent]
    UPGRADE --> |Boundary needs adjustment| BOUNDARY[Adjust agent boundary]
    CREATE --> INPUT2[Next run starts stronger]
    BOUNDARY --> INPUT2

    style INPUT fill:#fbbf24,color:#000
    style EVOLVE fill:#34d399,color:#000
    style CREATE fill:#f87171,color:#fff
    style INPUT2 fill:#fbbf24,color:#000

Agent boundaries + skill integration

The 9 meta roles each own a different domain:

Role	Responsibility	What it does not own
meta-warden	Coordination, arbitration, final synthesis	Does not directly write code
meta-conductor	Workflow and rhythm control	Does not do security review
meta-genesis	Agent design and SOUL.md	Does not choose tools
meta-artisan	Skill, MCP, and tool matching	Does not define persona
meta-sentinel	Security, permissions, rollback	Does not choreograph rhythm
meta-librarian	Memory and continuity	Does not execute code
meta-prism	Quality review and anti-slop	Does not search for capabilities
meta-scout	External capability discovery	Does not coordinate internally
meta-chrysalis	Evolution writeback, scar capture, recursive-safety gatekeeping	Does not evolve itself or bypass Warden gates

Each agent can load powerful skills and commands as needed. Meta_Kim ships with 9 community skills and supports custom extension.

flowchart TD
    WARDEN[meta-warden<br/>Coordination / arbitration / synthesis] --> CONDUCTOR[meta-conductor<br/>Workflow / rhythm]
    WARDEN --> GENESIS[meta-genesis<br/>Agent design]
    WARDEN --> ARTISAN[meta-artisan<br/>Skill / tool matching]
    WARDEN --> SENTINEL[meta-sentinel<br/>Security / permissions / rollback]
    WARDEN --> LIBRARIAN[meta-librarian<br/>Memory / continuity]
    WARDEN --> PRISM[meta-prism<br/>Quality review]
    WARDEN --> SCOUT[meta-scout<br/>External capability discovery]
    WARDEN --> CHRYSALIS[meta-chrysalis<br/>Evolution writeback]

    GENESIS -.-> |SOUL.md| ARTISAN
    ARTISAN -.-> |Skill loadout| GENESIS
    CONDUCTOR -.-> |Task board| WARDEN
    SENTINEL -.-> |Security interception| WARDEN
    PRISM -.-> |Review report| WARDEN
    SCOUT -.-> |Capability candidates| ARTISAN
    LIBRARIAN -.-> |Context memory| WARDEN
    CHRYSALIS -.-> |Scar / writeback proposal| WARDEN

    SKILLS[9 community skills<br/>+ custom extensions] --> ARTISAN
    HOOKS[Hook automation<br/>intercept / format / check] --> SENTINEL

    style WARDEN fill:#7c3aed,color:#fff
    style CONDUCTOR fill:#60a5fa,color:#000
    style GENESIS fill:#fbbf24,color:#000
    style ARTISAN fill:#34d399,color:#000
    style SENTINEL fill:#f87171,color:#fff
    style LIBRARIAN fill:#a78bfa,color:#fff
    style PRISM fill:#fb923c,color:#000
    style SCOUT fill:#2dd4bf,color:#000
    style CHRYSALIS fill:#84cc16,color:#000

Hook automation

In Claude Code, Meta_Kim uses hooks for automation:

• Dangerous command blocking: operations like rm -rf and DROP TABLE are blocked automatically
• Git push reminder: remind you to check before pushing
• Formatting: automatically format JS/TS files after edits
• Type checking: run TypeScript checks after edits
• console.log warning: remind you to remove console.log
• Session-end audit: check for leftover issues before the session ends
• Session-end memory save: write session summaries to MCP Memory Service on session end
• Subagent context injection: automatically inject project context into subagents

These hooks are not optional polish. They are the execution-layer guardrails of the governance system.

Cross-platform mapping

The whole architecture can be mapped onto any project that supports agents and agent-to-agent communication.

Meta_Kim currently maps to four platforms:

Platform	Status	Mapping style
Claude Code	Fully supported	.claude/agents/*.md + SKILL.md + hooks + MCP
Codex	Fully supported	.codex/agents/*.toml + .agents/skills/ + commands + hooks
OpenClaw	Formal projection; enforcement partial	openclaw/ workspaces + skills + internal hooks; typed plugin needed for blocking policy
Cursor	Formal projection; lightweight runtime	.cursor/agents/*.md + .cursor/rules/*.mdc + skills + hooks + MCP

The canonical source layer is canonical/agents/, canonical/skills/meta-theory/, config/contracts/, and config/capability-index/. The repository mirrors that layer into platform-specific projections through npm run meta:sync.

flowchart TB
    CANONICAL["canonical/ + config/<br/>(single source layer)"]

    CANONICAL --> |npm run meta:sync| CLAUDE[".claude/<br/>Claude Code<br/>agents + skills + hooks"]
    CANONICAL --> |npm run meta:sync| CODEX[".codex/ + .agents/<br/>Codex<br/>agents.toml + skills + hooks"]
    CANONICAL --> |npm run meta:sync| OPENCLAW["openclaw/<br/>OpenClaw<br/>workspaces + skills + internal hooks"]
    CANONICAL --> |npm run meta:sync| CURSOR[".cursor/<br/>Cursor<br/>agents + rules + skills + hooks + MCP"]

    NEW[New platform...] -.-> |config mapping| CANONICAL

    style CANONICAL fill:#7c3aed,color:#fff
    style CLAUDE fill:#fbbf24,color:#000
    style CODEX fill:#34d399,color:#000
    style OPENCLAW fill:#60a5fa,color:#000
    style CURSOR fill:#f87171,color:#fff
    style NEW fill:#555,color:#aaa

You can keep adding platform mappings over time as long as the platform supports agents and agent communication.

But there is an important caveat: the four runtimes are not equal. Claude Code currently has the most complete execution surface and is the primary editing runtime.

Capability surface	Claude Code	Codex	OpenClaw	Cursor
Agents	Native agents/subagents, mature at both project and user scope	Strong custom agents/subagents	Workspace-style agents, supports agent-to-agent	Official subagents under .cursor/agents, still lighter as a governance host
Skills / references	Native skills, references, and a mature global ecosystem	.agents/skills/ is the project skill root	Workspace skills and installable skills	Lighter skill/reference support
Hooks / automation	Project hooks + settings.json + plugin ecosystem	Trusted .codex/hooks.json project/user hooks	Internal lifecycle hooks; typed plugin hooks needed for blocking/canceling policy	.cursor/hooks.json lowerCamel lifecycle hooks with preToolUse / failClosed
MCP / configuration	Full native MCP and config surface	Can connect via runtime adapters and MCP	Clear workspace config	Can use MCP, but the surface is lighter
Governance loop capacity	Highest	High, but below Claude Code	High, but different in form	Lightest

The reason is not sentiment. Claude Code natively supports agents, skills, references, hooks, settings, MCP, plugins, and global capability discovery, which makes the whole loop - dealing -> contracts -> gates -> automation guardrails -> writeback - easier to carry end to end.

Choice surfaces are runtime-specific. Claude Code should use AskUserQuestion; Codex should use request_user_input when ~/.codex/config.toml has [features].default_mode_request_user_input = true; Cursor uses an alwaysApply project rule to trigger a chat decision card plus official preToolUse / failClosed hooks for tool gating; OpenClaw uses workspace/chat cards unless a typed plugin approval hook is explicitly installed.

Four-layer repository structure

Layer	Location	Purpose
Canonical source	canonical/agents/, canonical/skills/meta-theory/, config/contracts/, config/capability-index/	Preferred place for long-term edits
Runtime projections	.claude/, .codex/, openclaw/, .cursor/	Mirrors of the same capabilities for different runtimes
Local state	.meta-kim/state/{profile}/, .meta-kim/local.overrides.json	Profile-level state, run index, continuity
Scripts and checks	scripts/, npm run *	Sync, validate, discover, and accept

Three state layers (project / global / local)

These three layers are easy to mix up, so they must stay separate:

Layer	Storage location	What it decides
Project-level	Current repository canonical/, config/contracts/, config/capability-index/, runtime projections, docs, scripts	What this project itself defines
Global-level	~/.claude/, ~/.codex/, ~/.openclaw/, ~/.cursor/, ~/.meta-kim/global/	What can still be discovered on this machine
Local-level	.meta-kim/state/{profile}/run-index.sqlite, compaction/, profile.json	What a run left behind for this profile

What lives inside .meta-kim/?

.meta-kim/ is Meta_Kim's local save file. It does three things:

1. Remembers your choices — local.overrides.json

When you run node setup.mjs for the first time and pick "I want Claude Code and Codex", that choice is saved here. Next time you run setup, you don't have to choose again.

Example: You have Claude Code, Codex, and OpenClaw installed, but only want the first two. This file stores that preference — all scripts read it to know which runtimes to install skills for.

2. Records work history — state/{profile}/run-index.sqlite

When you run a governed workflow (e.g. "use the 8-stage spine to review some code"), the result can be indexed into a SQLite database. Later you can query "what did I review last time, what was found, what's still unresolved?"

Example: Last week you asked meta-prism to review the auth module. This week you changed the auth module again. The system checks .meta-kim/state/ and finds "last review found 3 issues, 2 were fixed, 1 is still open" — you don't have to repeat yourself.

3. Cross-session recovery — state/{profile}/compaction/

When you're halfway through a conversation and your token budget runs out, the compaction packet saves your current progress (which step you're on, what's still pending) so you can pick up where you left off in a new session.

Example: You ask Meta_Kim to do a complex multi-file refactor. You get through step 6 before the session ends. Next session, the system reads the compaction packet: "at step 6, step 7 hasn't started" — picks up from step 7, no need to start over.

Other files: doctor-cache/ stores npm run meta:doctor:governance results (written after each run), migrations/ tracks schema upgrades between Meta_Kim versions, profile.json stores profile metadata. All managed by scripts — you never edit them by hand.

Quick reference:

Path	What it does	When written
local.overrides.json	Remembers your runtime selection from setup.mjs	Auto — first setup.mjs run
state/{profile}/profile.json	Profile metadata (creation time, name)	Auto — setup.mjs creates the default profile
state/{profile}/run-index.sqlite	Indexed governed run records — who ran what, what was found, what's still open	On demand — npm run meta:index:runs -- <artifact>
state/{profile}/compaction/	Cross-session handoff packets: unfinished steps, pending findings, open verification gates	On demand — governed run that needs to survive a session break
state/{profile}/doctor-cache/	Cached results from npm run meta:doctor:governance	On demand — doctor:governance writes here
state/{profile}/migrations/	State migration tracking (schema upgrades between versions)	Auto — when state schema changes between versions

What works globally vs. in-repo only

Meta_Kim's gates and protocols work on three enforcement layers. After global installation (node setup.mjs), here is what works in any project versus what requires the Meta_Kim repo:

Enforcement layer	Global install	Needs Meta_Kim repo
Prompt layer (agents + skills enforce gates/protocols)	Works — installed to ~/.claude/skills/ and ~/.claude/agents/	—
Hook layer (session-end gate checks, memory save to MCP Memory Service, dangerous command blocking)	Works — configured in .claude/settings.json	—
Config layer (contract definitions are referenced in skill prompts)	Works — AI reads the rules from the installed skill	—
Code validation (npm run meta:validate:run hard-checks packet chains)	—	Required — script lives in scripts/validate-run-artifact.mjs

The first three layers are the primary defense and work everywhere. Code validation is a final safety net that requires running from the Meta_Kim repo (or pointing to its scripts).

---

Three-Layer Memory

Meta_Kim does not use a single memory layer. It uses three, each with a different job, so agents can keep improving while becoming more familiar with the project.

Each layer has different activation requirements:

• Layer 1 is built into Claude Code — requires Claude Code runtime (auto-memory at ~/.claude/projects/*/memory/)
• Layer 2 is installed automatically by node setup.mjs
• Layer 3 is installed by node setup.mjs but requires manual server startup (see Layer 3 activation below)

Layer 1: Memory (agent upgrade memory)

• Responsibility: agent upgrades and continuous learning
• Storage: .claude/projects/*/memory/
• Mechanism: before each run ends, the system reads memory and uses it to decide whether the agent should be upgraded or its boundary should change
• Core value: agents get smarter over time instead of restarting from zero each time
• Activation: automatic — AI reads and writes memory during each session
• Query: ask AI directly — "what did we learn from previous sessions about this project?"

Layer 2: Graphify (project-level LLM wiki)

• Responsibility: project-level code knowledge graph
• Storage: graphify-out/graph.json (NetworkX node-link format); for humans and agents, prefer graphify-out/GRAPH_REPORT.md when present
• Mechanism (data): node setup.mjs (optional Python step) installs graphify and idempotently runs python -m graphify claude install and python -m graphify hook install even if graphify was already installed via pip; git hooks rebuild the graph on commit/checkout in the current repo. npm run meta:graphify:install does the same (including hooks).
• Mechanism (usage): synced meta-theory dev-governance.md Fetch Step 0.5 defines how the model should detect and use the graph — not a background service. Claude Code subagents get a short hint via subagent-context.mjs, not automatic embedding of graph.json. Codex / OpenClaw / Cursor share the same reference after meta:sync but have no SubagentStart hook; optional python -m graphify codex install or python -m graphify claw install in a target repo patches that repo’s docs per graphify CLI (python -m graphify --help).
• Core value:
  • Make memory increasingly familiar with the project - not by remembering raw code, but by understanding structure and relationships
  • Reduce hallucinations - agents answer from graph facts instead of guessing
  • Cut token usage - subgraph extraction replaces raw file reads, with up to 71x compression
• Quality threshold:
  • Fuzzy nodes > 30% -> mark the graph as low quality and fall back to direct file reads
  • Total nodes < 10 -> the graph is too sparse and should fall back to Glob/Grep
  • A "god node" with too many incoming edges -> mark as a serial bottleneck
• Activation: node setup.mjs optional Python step or npm run meta:graphify:install — install/check, networkx, Claude-side registration, this repo’s git hooks; first graph build still depends on a hook run or a manual build command
• Query: python -m graphify query "your question" — natural language query against the code graph

Platform Automation Comparison

Capability	Claude Code	Codex	OpenClaw	Cursor
PreToolUse hook (auto-prompt before Glob/Grep)	✅ settings.json	✅ trusted .codex/hooks.json	❌	✅ .cursor/hooks.json preToolUse
Slash command /graphify	✅	✅	✅	✅
git hook auto-rebuild (post-commit/checkout)	✅	✅	✅	✅
AGENTS.md resident rules	N/A	✅	✅	✅
Multi-platform install via setup.mjs	✅ claude	✅ codex	✅ claw	✅ cursor

Key insight: Claude Code, Codex, and Cursor all have native hook configuration, but their schemas differ. OpenClaw uses its own internal/plugin hook model.

For multi-platform setups, run node setup.mjs — it loops through all selected platforms and runs graphify <platform> install for each one idempotently.

Layer 3: SQL (vector-level session retrieval)

• Responsibility: vector storage and retrieval for project sessions
• Storage: SQLite + vector extension (sqlite-vec)
• Mechanism: store key session information as vectors, then retrieve later by semantic similarity
• Core value:
  • Cross-session continuity - pick up where the last conversation left off
  • Vector-level retrieval - semantic understanding instead of keyword matching
  • Precise recall - find the most relevant context from historical sessions
• Activation: node setup.mjs installs and configures the MCP Memory Service (Layer 3), installs runtime memory hooks, then attempts to start the HTTP service in the background.
  • For Claude Code: SessionStart and Stop memory-save hooks are auto-registered during node setup.mjs; session-start writes project state via mcp_memory_global.py --mode session.
  • For Codex: ~/.codex/hooks.json receives SessionStart, UserPromptSubmit, and Stop bridges to meta-kim-memory-save.mjs, so start/prompt/end checkpoints are automatic.
  • For OpenClaw: ~/.openclaw/hooks/mcp-memory-service receives a managed hook for command:new, command:reset, session:compact:after, and command:stop.
  • For Cursor: ~/.cursor/hooks.json receives beforeSubmitPrompt and stop bridges to the shared memory hook.
• Start server: memory server --http (with MCP_ALLOW_ANONYMOUS_ACCESS=true on macOS/Linux, or $env:MCP_ALLOW_ANONYMOUS_ACCESS="true" in Windows PowerShell), then access at http://localhost:8000.
• Port: the server and Meta_Kim hooks use http://localhost:8000.
• Hooks: auto-registered for Claude Code, Codex, Cursor, and OpenClaw; each runtime uses its native hook format while sharing the same MCP Memory HTTP endpoint.
• MCP registration vs writes: .mcp.json registers the MCP Memory server (memory server) for client access. Automatic session writes are separate lifecycle hooks: Claude Code uses stop-memory-save.mjs, Codex/Cursor use meta-kim-memory-save.mjs, and OpenClaw uses its managed mcp-memory-service hook.
• Query: npm run meta:query:runs -- --owner <agent> — find past runs by agent, or npm run meta:index:runs -- <artifact> for manual indexing of validated run artifacts
• Troubleshooting:
  • Python hook fails on Windows: If the SessionStart hook fails with exit code 49 or shows no output, the Python command may point to the Windows Store shim. Run node scripts/install-mcp-memory-hooks.mjs to auto-detect and fix. The installer now skips WindowsApps shims and prefers explicit Python executables from LOCALAPPDATA\Programs\Python*. Use --force flag to re-register even if current path appears valid.
  • Check installation: Run node scripts/install-mcp-memory-hooks.mjs --check to verify hook status and Python path validity.
  • Manual verification: Test your Python command with python --version or the detected path with "C:/Users/YOUR_USER/AppData/Local/Programs/Python/Python311/python.exe" --version.

How the three layers work together

flowchart TB
    subgraph memory["Layer 1: Memory"]
        M_IN[Before the run ends<br/>read memory] --> M_JUDGE[Decide whether the agent<br/>needs an upgrade]
        M_JUDGE --> M_OUT[Update boundary<br/>adjust capability]
    end

    subgraph graphify["Layer 2: Graphify"]
        G_IN[When source files > 20<br/>auto-generate graph] --> G_COMPRESS[Subgraph extraction<br/>up to 71x compression]
        G_COMPRESS --> G_QUERY[Agent answers from graph<br/>facts]
    end

    subgraph sql["Layer 3: SQL"]
        S_IN[Session key information<br/>stored as vectors] --> S_INDEX[SQLite + sqlite-vec<br/>vector index]
        S_INDEX --> S_RECALL[Semantic similarity<br/>precise recall]
    end

    memory <--> graphify
    graphify <--> sql
    sql <--> memory

    GOAL1[Reduce hallucinations<br/>answer from facts, not invention]
    GOAL2[Reduce token usage<br/>compression instead of full reads]

    memory --> GOAL1
    graphify --> GOAL1
    graphify --> GOAL2
    sql --> GOAL2

    style memory fill:#fbbf24,color:#000
    style graphify fill:#34d399,color:#000
    style sql fill:#60a5fa,color:#000
    style GOAL1 fill:#dc2626,color:#fff
    style GOAL2 fill:#dc2626,color:#fff

The three memory layers work together toward two core goals:

1. Greatly reduce hallucinations - agents answer from facts and context instead of inventing details
2. Greatly reduce token usage - use graph compression instead of full-file reads and vector retrieval instead of brute-force search

---

Ops Command Quick Reference

Daily use

Command	Purpose
node setup.mjs	Interactive install / update / check wizard
git pull --ff-only	For clone installs, pull the latest Meta_Kim source from GitHub
node setup.mjs --update	Refresh the current installation projections, skills, and dependencies; it does not pull Meta_Kim source code
node setup.mjs --check	Environment check without writing
node setup.mjs --lang zh-CN	Force the Chinese UI

Sync and validation

Command	Purpose
npm run meta:sync	Sync from canonical sources to all four runtimes
npm run meta:check:runtimes	Check whether the four runtimes are in sync
npm run meta:validate	Validate repository integrity
npm run meta:verify:all	Full validation, including runtime smoke checks
npm run meta:doctor:governance	Governance health check

Where the active rules live

Surface	Current source
Repository / Codex rules	AGENTS.md
Claude Code rules	CLAUDE.md
Meta-theory skill	canonical/skills/meta-theory/SKILL.md
Meta-theory details	canonical/skills/meta-theory/references/
Cursor declarative backup rule	canonical/runtime-assets/cursor/rules/meta-enforcement.mdc
Cursor choice-surface fallback rule	canonical/runtime-assets/cursor/rules/meta-choice-surface.mdc
Runtime mirrors	.claude/, .codex/, .cursor/, openclaw/ after npm run meta:sync

When in doubt, edit canonical sources first, then run npm run meta:sync and npm run meta:check.

How to tell which scripts are useful

Start with package.json scripts. The supported maintenance paths are the meta:* commands documented above; most helper files under scripts/ exist because those commands call them. Use npm run meta:status, npm run meta:check, and npm run meta:verify:all for normal operation. Inspect an individual helper only when a package.json script or test points to it.

Skills and dependencies

Command	Purpose
npm run meta:deps:install	Install the 9 community skills globally
npm run meta:deps:install:all-runtimes	Install them into all runtimes
npm run meta:deps:install:claude-plugins	Install Claude Code marketplace plugins only
npm run discover:global	Scan global capabilities
npm run meta:sync:global	Sync meta-theory to the user-level runtime

planning-with-files is a core external dependency, not a project-local .agents/skills/ mirror. After dependency install, check runtime home directories such as ~/.codex/skills/planning-with-files/, ~/.claude/skills/planning-with-files/, ~/.cursor/skills/planning-with-files/, or ~/.openclaw/skills/planning-with-files/. Do not conclude it is missing from the absence of .agents/skills/planning-with-files/ alone.

Native dependency installs (Superpowers, ECC, cli-anything)

Superpowers has native plugin entry points in Claude Code, Codex, and Cursor. Meta_Kim no longer treats the old Codex / Cursor skills/superpowers fallback as a correct plugin install; update runs remove the legacy fallback written by older Meta_Kim versions and tell users to use the host-native plugin entry point.

ECC uses the upstream affaan-m/ECC package and plugin identity. Claude Code installs through ecc@ecc; home-based targets such as Codex, opencode, and Qwen use ECC's own CLI installer with the core profile. Project-local ECC targets such as Cursor, Zed, Gemini, CodeBuddy, Antigravity, and JoyCode must be installed from each project root with the upstream ECC command, so Meta_Kim cleans old fallback directories and prints the exact command instead of installing into the wrong global or npm-cache directory.

For plugin bundles without a native host plugin entry point, the installer still falls back to a sparse-checkout of the upstream bundle's runtime-specific subtree:

Runtime	Preferred subdir chain
Claude Code	native claude plugin install <spec>@<marketplace> (skills without claudePlugin fall back to skills/)
Codex	Superpowers uses the Codex Plugins pane or /plugins; ECC uses npx --yes --package [email protected] ecc install --profile core --target codex and currently installs the refactor-cleaner agent, not the /refactor-clean slash command because upstream ECC does not expose commands-core for Codex; other bundles fall back through .codex/ → .codex-plugin/ → skills/
Cursor	Superpowers uses /add-plugin superpowers or Cursor's plugin marketplace; ECC is project-local: run npx --yes --package [email protected] ecc install --profile core --target cursor from the project root; other bundles fall back through .cursor/ → .cursor-plugin/ → skills/
OpenClaw	skills/
opencode	ECC uses npx --yes --package [email protected] ecc install --profile core --target opencode; other bundles fall back through .opencode/ -> skills/
Qwen	ECC uses npx --yes --package [email protected] ecc install --profile core --target qwen
Zed, Gemini, CodeBuddy, Antigravity, JoyCode	ECC is project-local: run npx --yes --package [email protected] ecc install --profile core --target <target> from each project root
Qoder CLI	Candidate probe only: generic bundle probing can look for .qoder/ -> skills/, but ECC is not run for Qoder because upstream ecc install --help does not list qoder

Sparse-checkout fallback trees land in ~/.<runtime>/skills/<id>/; native ECC installs do not. Run npm run meta:deps:install:claude-plugins for the Claude marketplace path only, or npm run meta:deps:install:all-runtimes to cover every supported home runtime at once. Upgrading from an older install? Legacy full-repo clones are auto-detected by the .claude-plugin/ marker at the target root and re-extracted on the next run; old Codex/Cursor skills/superpowers, skills/ecc, and skills/everything-claude-code fallbacks are removed or replaced with native-install instructions.

Advanced ops

Command	Purpose
npm run meta:validate:run -- <file.json>	Validate governed run artifacts
npm run meta:eval:agents	Lightweight runtime smoke test
npm run meta:eval:agents:live	Live prompt-backed acceptance
npm run meta:probe:clis	Probe local CLI tools
npm run meta:test:mcp	MCP self-test
npm run meta:index:runs -- <dir>	Index validated run artifacts
npm run meta:query:runs -- --owner <agent>	Query the run index
npm run migrate:meta-kim -- <dir> --apply	Import an older prompt pack

---

FAQ

Q: I installed via npx, where are my files?

Meta_Kim writes to 3 places:

1. Current directory — .claude/, .codex/, .cursor/, openclaw/ runtime projections for THIS project
2. Your home — ~/.claude/skills/meta-theory/ (plus .codex / .cursor / .openclaw) for global skills shared across all projects
3. Manifest — ~/.meta-kim/install-manifest.json tracks everything for safe rollback

Run npm run meta:status (or node setup.mjs --check) in the directory where you ran npx to see the full footprint. Use npm run meta:uninstall for a safe rollback.

Q: What is different about Meta_Kim compared with a normal AI coding assistant?

A normal AI coding assistant does what you ask, with no governance layer in between. Meta_Kim inserts several layers between "ask" and "do": first it confirms what you actually want, then it plans who should do it, then it reviews the result, then it verifies the fix, and finally it preserves the lesson. It is not another AI; it is engineering discipline for AI.

Q: I only need to change one file. Do I need Meta_Kim?

No. Meta_Kim is for cross-file, cross-module, and multi-capability tasks. If you are only changing one function inside one file, plain Claude Code is enough. Do not use a cannon to hit a mosquito.

Q: What is the relationship between the 8-stage workflow and the 11-phase business workflow?

The 8-stage workflow is the execution skeleton (Critical -> Fetch -> Thinking -> Execution -> Review -> Meta-Review -> Verification -> Evolution) and stays relatively fixed. The 11-phase business workflow is a run-packaging workflow defined in config/contracts/workflow-contract.json (direction -> planning -> execution -> review -> meta_review -> revision -> verify -> summary -> feedback -> evolve -> mirror) and focuses on deliverable flow, closure, and runtime mirroring. It does not replace the 8 stages; it adds governance discipline on top.

Q: What does dynamic dealing mean?

The 8-stage workflow is fixed, but real tasks vary too much. Dealing gives the system flexibility inside that fixed path - for example, after three high-intensity actions in a row, the system can auto-pause with Pause; when a security issue appears, Risk can preempt the current flow. The fixed skeleton protects the baseline, and dynamic dealing provides adaptability.

Q: Will the three-layer memory be too heavy?

No. The three layers have separate jobs:

• Memory is very light - just a few markdown files
• Graphify only activates when source files exceed 20, and the graph can be reused after generation
• SQL uses local SQLite and does not require an extra database service

Together, they cost far less than asking AI to reread the entire project from scratch every time.

Q: Which platforms are supported?

Claude Code, Codex, OpenClaw, and Cursor are formal runtime projections. ECC additionally supports native install targets for opencode, Qwen, Zed, Gemini, CodeBuddy, Antigravity, and JoyCode. Qoder CLI is tracked as a candidate probe because its official docs expose compatible skills, subagents, hooks, and MCP surfaces, but it is not yet a formal Meta_Kim runtime projection. The exact support boundary lives in config/runtime-compatibility-catalog.json.

Q: Is the installation complicated?

One command is enough:

npx --yes github:KimYx0207/Meta_Kim meta-kim

Or clone the repository and run:

git clone https://github.com/KimYx0207/Meta_Kim.git
cd Meta_Kim
npm install
node setup.mjs

The wizard will guide you through language, platform, and installation scope.

Q: Why is it called "Meta"?

In Meta_Kim, meta means the smallest governable unit. A valid meta unit must:

• Own one clear class of responsibility
• Define what it refuses
• Be reviewable on its own
• Be replaceable
• Be safe to roll back

Not everything deserves to be called meta. Only what meets that bar counts.

Q: How does this project relate to MCP?

Meta_Kim uses MCP (Model Context Protocol) to expand the capability boundary of agents. Through the .mcp.json configuration, agents can call external tools and services. But Meta_Kim itself is not an MCP server - it is a governance framework, and MCP is only one of its integrated tools.

Further Reading

• README.zh-CN.md
• AGENTS.md
• CLAUDE.md
• config/contracts/workflow-contract.json
• canonical/runtime-assets/cursor/rules/meta-enforcement.mdc

---

Third-party Dependencies

Meta_Kim is MIT licensed. The following optional skill repositories are installed separately via node setup.mjs — each repo's license applies independently.

npm Dependencies

Package	License
@inquirer/prompts	MIT
@modelcontextprotocol/sdk	MIT
zod	MIT

Optional Skill Repositories

Repository	License
KimYx0207/agent-teams-playbook	MIT
KimYx0207/findskill	MIT
KimYx0207/HookPrompt	MIT
obra/superpowers	MIT
affaan-m/ECC	MIT
OthmanAdi/planning-with-files	MIT
HKUDS/CLI-Anything	Apache 2.0
garrytan/gstack	MIT
anthropics/skills	No license declared (© Anthropic, PBC)

Optional pip Packages

Package	License
graphifyy	MIT
mcp-memory-service	Apache 2.0

---

License

This project is licensed under the MIT License.