knit

An intelligent command layer for Claude Code.
Project-scoped memory, on-demand workflow protocol, parallel team worktrees,
and honest token accounting — all in one MCP server.

What knit is

Knit makes Claude Code do the right thing automatically because it can't predict how a user will phrase a request. It does three jobs at once:

Memory — every project keeps a brain at ~/.knit/projects/<hash>/. Sessions compound: learnings, false positives, session summaries, and a static-analysis import graph are all queryable next session.
Tokens — CLAUDE.md is ~100 lines (project facts only). Workflow protocol is fetched on demand via knit_get_workflow(phase). Knit is net-negative on context cost.
Workflow — a 4-tier classification (Inquiry / Trivial / Standard / Complex) with phase-triggered plan mode, quality-gated LEARN, and team-scoped git worktrees so parallel agents don't step on each other.

It's a single product, not three. Every design choice has to win on memory + tokens + workflow together.

What's new in v0.7.0

Universal protocol injection. Knit sets the MCP server-level instructions field, so every MCP client (Claude Code, Cursor, Codex) sees Knit's flow at session start — before tool descriptions. Session 1 follows the protocol instead of stumbling onto it.
Tier-gated tool surface. 38 tools split into three tiers: Tier 1 (26 universal — memory, knowledge graph, workflow, classification, false-positive suppression, reflection, Protocol Guard config, diagnostics) is always exposed. Tier 2 (team worktrees, subagent installer) auto-activates when the project shape matches (≥3 detected domains, .claude/agents/ exists) or via explicit opt-in. Tier 3 (admin/setup) is opt-in only. Solo-domain projects no longer see 9 team-worktree tools cluttering their decision space.
knit_list_features is the discoverability escape hatch — always available, always tells you what's hidden and exactly how to enable it (knit_enable_feature({feature: "teams" | "subagents" | "admin"})). Persisted to ~/.knit/projects/<hash>/features.json so the choice survives sessions.
Inquiry tier in the classifier. Read-only "what / where / audit / explain" tasks now route to tier: "inquiry" with no plan mode and no phases — fixes a long-standing over-routing bug where audit-style questions hijacked Complex.
CLAUDE.md cut ~88% (16.7 KB → ~2 KB on typical projects). The per-turn context tax dropped sharply; all project-specific content (header, project map, domain architecture, build gates, false positives) stays intact.
Lazy / minimal response modes. knit_load_session returns the lean core by default; opt into more via include=patterns,teams,metrics,recent_sessions,full_learnings,full_knowledge,all. knit_classify_task returns the minimal shape by default; pass verbose=true for the diagnostic fields.
Legacy CLAUDE.md migration. Users upgrading from v0.5.x with / markers are auto-migrated — the legacy block is replaced cleanly with the new lean block instead of leaving an orphan.

Per-session token-budget table

Surface	v0.6.5	v0.7.0	Cut
CLAUDE.md per-turn	~16.7 KB	~2 KB	88%
Tool registry (typical project)	~6–8 KB	~3–4 KB	~50%
`knit_classify_task` response	~500 tok	~150 tok	70%
`knit_load_session` response	~3–5 KB	~1.5 KB	~60%

Upgrade note

After running npx knit-mcp@latest setup (or just updating the version pin), restart Claude Code. The MCP server's instructions field and tier-gated tools/list only flow into the system prompt at handshake — the cached process from before the upgrade keeps the v0.6.5 behavior until restart.

Setup (one time)

npx knit-mcp@latest setup

Adds the Knit MCP server to your Claude Code config (~/.claude.json). No per-project setup. Open Claude Code in any project and the first MCP tool call auto-initializes everything.

Supported shells: macOS, Linux, WSL, Git Bash, and Windows PowerShell. The generated hooks use POSIX-style single-quoted node -e '…' payloads. Windows cmd.exe does not treat single quotes as delimiters and is not supported as the hook-runner shell — on Windows, use PowerShell (default in modern Windows Terminal) or Git Bash. If you hit a hook error on Windows, file an issue with the shell you're using.

Quiet mode (no hook enforcement)

Knit ships Protocol Guard in warn mode by default — hooks print reminders, they never block. If you want it fully silent (no PreToolUse classification gate, no reminder messages), run this once inside Claude Code:

knit_set_protocol_strictness({ level: "off" })

The other hooks (LEARN compliance, KB metrics, final build verification) stay as observability nudges — they print, they don't gate. To remove them too, see Uninstall below.

Uninstall

rm -rf ~/.knit                                 # all per-project + global memory

Then:

Remove "knit-brain" from mcpServers in ~/.claude.json
Delete the  ...  block from each project's CLAUDE.md
Remove _knitOwned entries from each project's .claude/settings.local.json (or delete the file if Knit was the only thing in it)

Total time: ~30 seconds per project. Knit doesn't write anywhere else on your machine.

How data is stored

Knit data is centralized — not in every repo's working tree:

~/.knit/
└── projects/<hash>/                    ← one dir per project (sha256 of repo root)
    ├── knowledge.json                  ← import graph, exports, test mapping
    ├── knowledgebase.json              ← learnings + access metrics + false positives
    ├── sessions.jsonl                  ← session memory, append-only
    ├── teams.json                      ← custom teams (if defined)
    ├── worktrees.json                  ← active team worktree registry
    └── learnings/<project>.md          ← human-readable learnings

What stays in the project:

your-project/
├── CLAUDE.md                           ← ≤150-line thin shape, marker-wrapped
└── .claude/
    └── settings.local.json             ← per-machine hooks (knit-managed; gitignored by convention)

The project's own CLAUDE.md is wrapped in  ...  markers. Knit regenerates only the block between markers — never clobbers anything else you write. If your project already has a CLAUDE.md without markers, knit writes a sidecar at .claude/KNIT.md instead.

Override the data location with KNIT_HOME=/custom/path (useful for sandboxes and tests).

Workflow on demand

The protocol is in MCP, not preloaded in every session. CLAUDE.md tells the agent to call knit_get_workflow(phase) when it needs the actual procedure. Sections:

knit_get_workflow({phase: "research"})    // RESEARCH phase details
knit_get_workflow({phase: "plan"})        // PLAN + plan-mode rules
knit_get_workflow({phase: "execute"})     // EXECUTE + TDD
knit_get_workflow({phase: "optimize"})    // OPTIMIZE + role briefings
knit_get_workflow({phase: "review"})      // REVIEW gates
knit_get_workflow({phase: "learn"})       // LEARN quality gate
knit_get_workflow({phase: "handoff"})     // session handoff
knit_get_workflow({phase: "ship"})        // commit + ship + prod checklist
knit_get_workflow({phase: "tdd"})         // RED → GREEN → REFACTOR
knit_get_workflow({phase: "tools"})       // knit MCP tools reference

Plus overview, tier, phases. Call with no phase to list all sections.

Effect: v0.1's CLAUDE.md was ~700 lines / ~20 KB per session, every session. v0.2's is ~100 lines / ~2.7 KB. Protocol depth pulled only when needed.

35 MCP Tools

Query the brain (read-only, cached, ~5ms)

Tool	What it does
`knit_query_imports`	Reverse dependencies for a file. Use before edits.
`knit_query_dependents`	What a file imports.
`knit_query_exports`	What a file exposes.
`knit_query_tests`	Test coverage for a file, or list all untested.
`knit_find_fanout`	High-fanout files — the contracts.
`knit_search_learnings`	Past lessons by domain tag.
`knit_get_false_positives`	Confirmed non-issues to suppress in review.
`knit_brain_status`	Brain health + token accounting.
`knit_search_sessions`	Search past sessions by free text over summary+tags+branch.
`knit_load_session`	Call at session start — returns last sessions, handoff, learnings, false positives, teams, project knowledge in one round trip.

Update the brain (write — quality-gated)

Tool	What it does
`knit_classify_task`	First call on every task. Returns tier, phases, affected domains.
`knit_build_context`	Domain context for the current task.
`knit_record_learning`	Save a non-obvious insight. Quality check first.
`knit_record_false_positive`	Mark a finding as a confirmed non-issue.
`knit_save_session_summary`	Opt-in narrative summary of what this session did.
`knit_save_handoff`	Save state when context degrades.
`knit_setup_project`	Describe a non-code project (legal, marketing, research).
`knit_prune_sessions`	Prune sessions.jsonl by age — keep recent N or drop entries older than N days.
`knit_install_agent`	Install a single VoltAgent subagent (e.g. `typescript-pro`) into `.claude/agents/`.

Protocol Guard (v0.5.0+)

Runtime enforcement of the knit protocol via PreToolUse and SessionStart hooks. Default strictness: warn.

Tool	What it does
`knit_set_protocol_strictness`	Set strictness: `off` (no checks), `warn` (reminder), `block` (hard-fail Edit/Write without prior `knit_classify_task`).
`knit_get_protocol_strictness`	Read current strictness level for this project.

Workflow on demand

Tool	What it does
`knit_get_workflow`	Fetch protocol depth for one phase.

Parallel team worktrees

Tool	What it does
`knit_spawn_team_worktree`	Create a git worktree for a team.
`knit_list_team_worktrees`	List active team worktrees.
`knit_finalize_team_worktree`	Merge or discard a team's worktree.

Team review board

Tool	What it does
`knit_get_teams`	List auto-detected or custom teams.
`knit_define_team`	Create a custom team.
`knit_start_team_review`	Start a parallel review with shared findings.
`knit_get_team_prompt`	Per-team prompt including other teams' findings.
`knit_post_team_findings`	Post findings to the shared board.
`knit_get_board_summary`	Cross-team summary, severity-gated.

Cross-project learnings (Model C, opt-in)

Tool	What it does
`knit_record_global_learning`	Opt-in: save an insight to `~/.knit/global/learnings.jsonl` when it generalizes beyond this project.
`knit_search_global_learnings`	Free-text search across all of your projects' shared learnings.
`knit_reflect`	Detect patterns across recorded learnings. Useful with ≥3 entries (which Model C makes easy to reach).
`knit_get_suggestions`	Adaptive suggestions for the current task based on past patterns in given domains.

Per-project knit_record_learning stays primary. The global pool is for the lessons that travel between projects — "Stripe signature rules", "GitHub API pagination quirks", "Redis cluster failover behavior" — the kind of thing future-you will be glad you wrote down once, somewhere.

Subagents — VoltAgent + project personalization

v0.4 closes the gap where knit's team configs referenced agent names (typescript-pro, security-engineer, etc.) without actually installing them. A fresh user opening Claude Code had none of those agents on disk, so teams fell back to generic prompts.

Now: on first MCP call, knit installs personalized subagents into <project>/.claude/agents/knit-<name>.md. Each agent has:

The VoltAgent base — the curated system prompt from github.com/VoltAgent/awesome-claude-code-subagents (MIT-licensed, 131+ agents). Knit bundles the 6 most common (code-reviewer, security-engineer, qa-expert, typescript-pro, python-pro, golang-pro) so they install with zero network. Specialized agents fetch from VoltAgent at a pinned SHA the first time knit needs them.
An knit context block appended at the end with project name, stack, high-fanout files, recent relevant learnings, false positives to suppress, and the knit MCP tools the agent can call.

Each agent now has both VoltAgent's role expertise AND knit's project-specific context. When a team dispatches via Claude Code's Agent tool, the agent inherits both layers.

Never clobbers user-curated agents. If you have your own <project>/.claude/agents/typescript-pro.md, knit writes knit-typescript-pro.md alongside it. Different filename, no conflict.

knit install-agents              # install agents this project's teams need
knit install-agents --all        # install every known agent
knit install-agents --refresh    # re-fetch from network even if cached

KNIT_OFFLINE=1 disables network fetches (bundled-core still works). KNIT_AGENT_REGISTRY_REF=main overrides the pinned VoltAgent SHA. (Legacy ENGRAM_OFFLINE / ENGRAM_AGENT_REGISTRY_REF are still honored.)

Parallel team worktrees

A Complex task gets broken across multiple teams. Each team works in its own git worktree (sibling to the main repo, native git worktree convention). Multiple agents within one team share the team's worktree. The orchestrator collects each team's work, runs gates, and merges back.

/Users/p/my-repo                          <- main
/Users/p/my-repo-knit-ui-<ts>           <- UI team
/Users/p/my-repo-knit-api-security-<ts> <- API & Security team

// Orchestrator workflow
const ui = await knit_spawn_team_worktree({ team_name: "UI", task_description: "..." })
// Spawn agents with ui.path; they cd there and work
// ...
await knit_finalize_team_worktree({ team_name: "UI", action: "merge" })

Merge conflicts surface cleanly — knit_finalize_team_worktree with action: "merge" returns {status: "conflict", conflict_files: [...]} without destroying the worktree. Resolve manually, then call again.

Compatible with Claude Code's EnterWorktree({path}) — knit's worktrees register via native git worktree add, so any session can switch into one.

Token accounting

knit_brain_status answers the only question that matters: is knit saving more than it costs?

{
  "token_accounting": {
    "claude_md_kb": 2.7,
    "session_count": 12,
    "learnings_hit_rate_pct": 67,
    "note": "Healthy."
  }
}

Warnings surface when CLAUDE.md > 30 KB (knit is too heavy) or hit rate < 20 % on >10 learnings (most learnings unused — prune).

CLI

knit setup       # One time: add MCP to Claude settings
knit status      # Dashboard: sessions, learnings, hit rate, knowledge health
knit refresh     # Force rebuild knowledge brain

Example knit status output:

Knowledge Index
  Files:        47 indexed (12,340 lines)
  Imports:      23 edges mapped
  Untested:     8 files

Knowledge Base
  Learnings:      12 total
  Accessed:        8 (67% hit rate)
  False positives: 3

Token accounting
  CLAUDE.md:       2.7 KB
  Sessions logged: 14
  Hit rate:        67% → Healthy

How it's different

	gstack (skills)	ECC (agents)	Knit
Setup	Install skills per-project	Manual `.claude/` setup	One command. Done forever.
Memory	jsonl files in-tree	Memory directory	`~/.knit/projects/<hash>/` — centralized, project-keyed, searchable sessions
Token cost	Skills loaded into context	Rules loaded into context	Workflow fetched on-demand. CLAUDE.md is ~2.7 KB.
Parallel work	None	None	Team-scoped git worktrees
Self-measurement	None	None	`knit_brain_status.token_accounting`
Non-code projects	No	No	Description-driven domains via `knit_setup_project`

Migration from v0.1

If you have an existing project with knit v0.1 data at <project>/.claude/, knit v0.2 auto-migrates on the first MCP call:

Detects <project>/.claude/knowledge.json (or knowledgebase.json)
Copies all knit data forward to ~/.knit/projects/<hash>/
Writes <project>/.claude/MIGRATED.txt breadcrumb explaining where the data went
Leaves the old .claude/ directory intact (delete at your discretion)

No data loss, no dual-writes. Single migration per project.

Development

git clone https://github.com/PDgit12/knit.git
cd knit
npm install
npm run dev       # Run CLI locally
npm run test      # 295 tests
npm run typecheck # TypeScript strict mode
npm run build     # Compile CLI + MCP server

Architecture

knit (npm package)
├── dist/cli.js                 # CLI: setup, status, refresh
└── dist/mcp/server.js          # MCP server: 27 tools, auto-init

per-project, in ~/.knit/projects/<hash>/
├── knowledge.json              # import graph + exports + test map
├── knowledgebase.json          # learnings + access metrics
├── sessions.jsonl              # session memory, append-only
├── teams.json                  # custom teams
├── worktrees.json              # active team worktree registry
└── learnings/<project>.md      # human-readable learnings

per-project, in <project>/
├── CLAUDE.md                   # ≤150-line thin shape, marker-wrapped
└── .claude/settings.local.json # per-machine hooks, knit-managed (gitignored by convention)

Zero external dependencies for the knowledge brain. 295 tests. Strict-mode TypeScript.

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 100 Commits
.github/workflows		.github/workflows
scripts		scripts
src		src
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
THIRD-PARTY-NOTICES.md		THIRD-PARTY-NOTICES.md
V0.7-PLAN.md		V0.7-PLAN.md
eslint.config.js		eslint.config.js
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json
vitest.config.ts		vitest.config.ts

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knit

What knit is

What's new in v0.7.0

Per-session token-budget table

Upgrade note

Setup (one time)

Quiet mode (no hook enforcement)

Uninstall

How data is stored

Workflow on demand

35 MCP Tools

Query the brain (read-only, cached, ~5ms)

Update the brain (write — quality-gated)

Protocol Guard (v0.5.0+)

Workflow on demand

Parallel team worktrees

Team review board

Cross-project learnings (Model C, opt-in)

Subagents — VoltAgent + project personalization

Parallel team worktrees

Token accounting

CLI

How it's different

Migration from v0.1

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knit

What knit is

What's new in v0.7.0

Per-session token-budget table

Upgrade note

Setup (one time)

Quiet mode (no hook enforcement)

Uninstall

How data is stored

Workflow on demand

35 MCP Tools

Query the brain (read-only, cached, ~5ms)

Update the brain (write — quality-gated)

Protocol Guard (v0.5.0+)

Workflow on demand

Parallel team worktrees

Team review board

Cross-project learnings (Model C, opt-in)

Subagents — VoltAgent + project personalization

Parallel team worktrees

Token accounting

CLI

How it's different

Migration from v0.1

Development

Architecture

License

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