---

What's New in v3.2.0

• Unified preflight_check entry point — one tool that triages every prompt and chains the right checks automatically
• Smart triage classification — routes prompts through a decision tree (trivial → ambiguous → multi-step → cross-service)
• Correction pattern learning — remembers past mistakes and warns you before repeating them
• Cross-service contracts — extracts types, interfaces, routes, and schemas across related projects
• .preflight/ config directory — team-shareable YAML config for triage rules and thresholds
• Trend & comparative scorecards — weekly/monthly trend lines, cross-project comparisons, radar charts, PDF export
• Cost estimator — estimates token spend and waste from corrections

---

The Problem

We built this after analyzing 9 months of real Claude Code usage — 512 sessions, 32,000+ events, 3,200+ prompts, 1,642 commits, and 258 sub-agent spawns across a production Next.js/Prisma/Supabase app. The findings were brutal:

• 41% of prompts were under 50 characters — things like fix the tests, commit this, remove them
• Each vague prompt triggers a wrong→fix cycle costing 2-3x tokens
• ~33K characters/day duplicated from repeated context pastes
• 124 corrections logged — places where Claude went the wrong direction and had to be steered back
• 94 context compactions from unbounded session scope blowing past the context window
• Estimated 30-40% of tokens wasted on avoidable back-and-forth

The pattern is always the same: vague prompt → Claude guesses → wrong output → you correct → repeat. That's your money evaporating.

The Solution

24 tools in 5 categories that run as an MCP server inside Claude Code:

Category	Tools	What it does
✈️ Preflight Core	1	Unified entry point — triages every prompt, chains the right checks automatically
🎯 Prompt Discipline	12	Catches vague prompts, enforces structure, prevents waste
🔍 Timeline Intelligence	4	LanceDB vector search across months of session history
📊 Analysis & Reporting	4	Scorecards, cost estimation, session stats, pattern detection
✅ Verification & Hygiene	3	Type-check, test, audit, and contract search

Before / After

❌  "fix the auth bug"
     → Claude guesses which auth bug, edits wrong file
     → You correct it, 3 more rounds
     → 12,000 tokens burned

✅  preflight intercepts → clarify_intent fires
     → "Which auth bug? I see 3 open issues:
        1. JWT expiry not refreshing (src/auth/jwt.ts)
        2. OAuth callback 404 (src/auth/oauth.ts)
        3. Session cookie SameSite (src/middleware/session.ts)
        Pick one and I'll scope the fix."
     → 4,000 tokens, done right the first time

---

Quick Start

Option A: npx (fastest — no install)

claude mcp add preflight -- npx -y preflight-dev-serve

With environment variables:

claude mcp add preflight \
  -e CLAUDE_PROJECT_DIR=/path/to/your/project \
  -- npx -y preflight-dev-serve

Option B: Clone & configure manually

git clone https://github.com/TerminalGravity/preflight.git
cd preflight && npm install

Add to your project's .mcp.json:

{
  "mcpServers": {
    "preflight": {
      "command": "npx",
      "args": ["tsx", "/path/to/preflight/src/index.ts"],
      "env": {
        "CLAUDE_PROJECT_DIR": "/path/to/your/project"
      }
    }
  }
}

Restart Claude Code. The tools activate automatically.

Option C: npm (global)

npm install -g preflight-dev
claude mcp add preflight -- preflight-dev-serve

Note: preflight-dev runs the interactive setup wizard. preflight-dev-serve starts the MCP server — that's what you want in your Claude Code config.

---

How It Works

The Triage Decision Tree

Every prompt flows through a classification engine before any work begins. This is the actual decision tree from src/lib/triage.ts:

flowchart TD
    A[Prompt Arrives] --> B{Skip keyword?}
    B -->|Yes| T1[✅ TRIVIAL]
    B -->|No| C{Multi-step indicators?}
    C -->|Yes| MS[🔶 MULTI-STEP]
    C -->|No| D{Cross-service keywords?}
    D -->|Yes| CS[🔗 CROSS-SERVICE]
    D -->|No| E{always_check keyword?}
    E -->|Yes| AM1[⚠️ AMBIGUOUS]
    E -->|No| F{"< 20 chars + common cmd?"}
    F -->|Yes| T2[✅ TRIVIAL]
    F -->|No| G{"< 50 chars, no file refs?"}
    G -->|Yes| AM2[⚠️ AMBIGUOUS]
    G -->|No| H{Vague pronouns or verbs?}
    H -->|Yes| AM3[⚠️ AMBIGUOUS]
    H -->|No| CL[✅ CLEAR]

    style T1 fill:#2d6a4f,color:#fff
    style T2 fill:#2d6a4f,color:#fff
    style CL fill:#2d6a4f,color:#fff
    style AM1 fill:#e9c46a,color:#000
    style AM2 fill:#e9c46a,color:#000
    style AM3 fill:#e9c46a,color:#000
    style CS fill:#457b9d,color:#fff
    style MS fill:#e76f51,color:#fff

Each level triggers different tool chains:

Level	What fires	Example prompt
Trivial	Nothing — pass through	commit
Clear	File verification only	fix null check in src/auth/jwt.ts line 42
Ambiguous	Clarify intent + git state + workspace priorities	fix the auth bug
Cross-service	Clarify + search related projects + contracts	add tiered rewards
Multi-step	Clarify + scope + sequence + checkpoints	refactor auth to OAuth2 and update all consumers

Additionally, correction pattern matching can boost any triage level. If your prompt matches 2+ keywords from a previously logged correction, it's bumped to at least ambiguous — even if it would otherwise pass through.

Data Flow

flowchart LR
    A[User Prompt] --> B[Triage Engine]
    B --> C[Tool Chain]
    C --> D[Response]

    B -.-> E[".preflight/\nconfig.yml\ntriage.yml"]
    B -.-> F["Patterns\nLog"]
    C -.-> G["LanceDB\n(per-project)"]
    C -.-> H["Contracts\n(per-project)"]
    G -.-> I["~/.claude/projects/\n(session JSONL)"]

    style A fill:#264653,color:#fff
    style B fill:#2a9d8f,color:#fff
    style C fill:#e9c46a,color:#000
    style D fill:#e76f51,color:#fff

Session Data Structure

Claude Code stores session data as JSONL files:

~/.claude/projects/<encoded-path>/
├── <session-uuid>.jsonl              # Main session
├── <session-uuid>/
│   └── subagents/
│       └── <sub-uuid>.jsonl          # Sub-agent sessions

Each JSONL line is an event. The session parser extracts 8 event types:

Event Type	Source	What it captures
prompt	user messages	What the dev typed
assistant	assistant messages	Claude's text response
tool_call	assistant tool_use blocks	Tool invocations (Read, Write, Bash, etc.)
sub_agent_spawn	Task/dispatch_agent tool_use	When Claude delegates to a sub-agent
correction	user messages after assistant	Detected via negation patterns (no, wrong, actually, undo…)
compaction	system messages	Context was compressed (session hit token limit)
error	tool_result with is_error	Failed operations
commit	git log integration	Commits made during session

LanceDB Schema

Events are stored in per-project LanceDB databases with vector embeddings for semantic search:

~/.preflight/projects/<sha256-12-char>/
├── timeline.lance/       # LanceDB vector database
├── contracts.json        # Extracted API contracts
└── meta.json             # Project metadata

Table: events
├── id: string            (UUID)
├── content: string       (event text)
├── content_preview: string (first 200 chars)
├── vector: float32[384]  (Xenova/all-MiniLM-L6-v2) or float32[1536] (OpenAI)
├── type: string          (event type from above)
├── timestamp: string     (ISO 8601)
├── session_id: string    (session UUID)
├── project: string       (decoded project path)
├── project_name: string  (short name)
├── branch: string        (git branch at time of event)
├── source_file: string   (path to JSONL file)
├── source_line: number   (line number in JSONL)
└── metadata: string      (JSON — model, tool name, etc.)

The project registry at ~/.preflight/projects/index.json maps absolute paths to their SHA-256 hashes.

Contract Extraction

The contract extractor scans your project for API surfaces:

Pattern	What it finds
export interface/type/enum	TypeScript type definitions
export function GET/POST/…	Next.js API routes
router.get/post/…	Express route handlers
model Foo { … }	Prisma models and enums
OpenAPI/Swagger specs	Routes and schema components
.preflight/contracts/*.yml	Manual contract definitions

Contracts are stored per-project and searched across related projects during cross-service triage.

---

Onboarding a Project

Run onboard_project to index a project's history. Here's what happens:

1. Discovers sessions — finds JSONL files in ~/.claude/projects/<encoded-path>/, including subagent sessions
2. Parses events — extracts the 8 event types from each session file (streams files >10MB)
3. Extracts contracts — scans source for types, interfaces, enums, routes, Prisma models, OpenAPI schemas
4. Loads manual contracts — merges any .preflight/contracts/*.yml definitions (manual wins on name conflicts)
5. Generates embeddings — local Xenova/all-MiniLM-L6-v2 by default (~90MB model download on first run, ~50 events/sec) or OpenAI if OPENAI_API_KEY is set (~200 events/sec)
6. Stores in LanceDB — per-project database at ~/.preflight/projects/<sha256-12>/timeline.lance/
7. Updates registry — records the project in ~/.preflight/projects/index.json

No data leaves your machine unless you opt into OpenAI embeddings.

After onboarding, you get:

• 🔎 Semantic search — "How did I set up auth middleware last month?" actually works
• 📊 Timeline view — see what happened across sessions chronologically
• 🔄 Live scanning — index new sessions as they happen
• 🔗 Cross-service search — query across related projects

---

Tool Reference

✈️ Preflight Core

Tool	What it does
preflight_check	The main entry point. Triages your prompt (trivial → multi-step), chains the right checks automatically, matches against known correction patterns. Accepts force_level override: skip, light, full.

🎯 Prompt Discipline

Tool	What it does
scope_work	Creates structured execution plans before coding starts
clarify_intent	Gathers project context (git state, workspace docs, ambiguity signals) to disambiguate vague prompts
enrich_agent_task	Enriches sub-agent tasks with file paths, patterns, and cross-service context
sharpen_followup	Resolves "fix it" / "do the others" to actual file targets
token_audit	Detects waste patterns, grades your session A–F
sequence_tasks	Orders tasks by dependency, locality, and risk
checkpoint	Save game before compaction — commits + resumption notes
check_session_health	Monitors uncommitted files, time since commit, turn count
log_correction	Tracks corrections and identifies recurring error patterns
check_patterns	Checks prompts against learned correction patterns — warns about known pitfalls
session_handoff	Generates handoff briefs for new sessions
what_changed	Summarizes diffs since last checkpoint

🔍 Timeline Intelligence

Tool	What it does
onboard_project	Indexes a project's session history + contracts into per-project LanceDB
search_history	Semantic search with scope: current project, related, or all indexed projects
timeline	Chronological view of events across sessions
scan_sessions	Live scanning of active session data

📊 Analysis & Reporting

Tool	What it does
generate_scorecard	12-category report card — session, trend (week/month), or cross-project comparative. Radar chart SVG, PDF or markdown.
estimate_cost	Token usage, dollar cost, waste from corrections, preflight savings
session_stats	Lightweight session analysis — no embeddings needed
prompt_score	Gamified A–F grading on specificity, scope, actionability, done-condition

✅ Verification & Hygiene

Tool	What it does
verify_completion	Runs type check + tests + build before declaring done
audit_workspace	Finds stale/missing workspace docs vs git activity
search_contracts	Search API contracts, types, and schemas across current and related projects

---

Usage Examples

Real prompts you'd type in Claude Code once preflight is installed. The tools fire automatically via preflight_check, or you can call them directly.

Catch a vague prompt before it wastes tokens

> fix the auth bug

preflight_check intercepts → clarify_intent fires:
  "I found 3 auth-related recent changes:
   1. JWT refresh logic in src/auth/jwt.ts (modified 2h ago)
   2. OAuth callback route in src/routes/oauth.ts (3 open TODOs)
   3. Session middleware in src/middleware/auth.ts (failing test)
   Which one? Or describe the symptoms."

Scope a multi-step task before starting

> refactor the database layer to use Drizzle instead of Prisma

preflight_check triages as MULTI-STEP → scope_work + sequence_tasks fire:
  Phase 1: Install drizzle-orm, create schema (src/db/schema.ts)
  Phase 2: Migrate queries in src/services/ (12 files)
  Phase 3: Update tests (8 files reference prisma mocks)
  Phase 4: Remove prisma dependencies, delete prisma/ dir
  Estimated: 4 checkpoints, ~45 min

Search your session history

> use search_history to find how I set up the rate limiter

Searches LanceDB across your indexed sessions:
  Found 3 relevant events (similarity > 0.82):
  - Session abc123 (Jan 15): Added express-rate-limit to src/middleware/
  - Session def456 (Jan 16): Configured per-route limits in src/routes/api.ts
  - Session ghi789 (Feb 2): Fixed rate limit bypass via X-Forwarded-For

Grade your prompting habits

> use prompt_score on "update the thing"

  Grade: D
  - Specificity: 1/5 — no file paths, no identifiers
  - Scope: 1/5 — "the thing" is completely ambiguous
  - Actionability: 2/5 — "update" is vague (refactor? fix? add feature?)
  - Done-condition: 0/5 — no way to know when it's done
  Suggestion: "Update the price formatter in src/utils/currency.ts
  to handle JPY (zero-decimal currency)"

Check session health mid-work

> use check_session_health

  ⚠️ 47 uncommitted files (last commit: 38 min ago)
  ⚠️ 23 turns since last checkpoint
  💡 Consider running `checkpoint` before context compaction hits

Get a cost estimate

> use estimate_cost

  This session: ~$2.40 (148K tokens)
  Waste from corrections: ~$0.85 (3 wrong→fix cycles)
  Preflight savings: ~$1.20 (4 vague prompts caught early)
  Net: saving roughly 35% vs unchecked prompting

---

The 12-Category Scorecard

generate_scorecard evaluates your prompt discipline across 12 categories. Each one measures something specific about how you interact with Claude Code:

#	Category	What it measures	How it scores
1	Plans	Do you start sessions with a plan?	% of sessions where the first 3 prompts include file references and >100 chars
2	Clarification	Are your prompts specific?	% of user prompts containing file paths or specific identifiers
3	Delegation	Are sub-agent tasks well-defined?	% of sub-agent spawns with >200 char descriptions
4	Follow-up Specificity	Do follow-ups reference files?	% of follow-up prompts (after assistant response) with file refs or ≥50 chars
5	Token Efficiency	How many tool calls per file?	Ratio of tool_calls to unique files touched (ideal: 5-10 calls/file)
6	Sequencing	Do you stay focused or context-switch?	Topic switch rate — how often prompts jump between different directory areas
7	Compaction Management	Do you commit before compaction?	% of compaction events preceded by a commit within 10 messages
8	Session Lifecycle	Do you commit regularly?	% of sessions with healthy commit frequency (every 15-30 min)
9	Error Recovery	How quickly do you recover from mistakes?	Correction rate (% of messages) + recovery speed (within 2 messages)
10	Workspace Hygiene	Do you maintain workspace docs?	Baseline 75 + bonus for sessions referencing .claude/ or CLAUDE.md
11	Cross-Session Continuity	Do new sessions read context?	% of sessions where first 3 tool calls read project context docs
12	Verification	Do you test at the end?	% of sessions that ran tests/build/lint in the final 10% of events

Report Types

Type	What you get
Scorecard	Single-period snapshot with 12 categories, radar chart, best/worst highlights
Trend	Weekly or monthly trend line, per-category movement, top 3 improvement areas, best/worst prompts
Comparative	Side-by-side across multiple projects, cross-project pattern detection

All three support PDF export (requires Playwright) and historical baselines that track your rolling averages over time.

---

Cross-Service Awareness

Preflight understands that microservices share contracts. When your prompt mentions a keyword from a related project, triage escalates to cross-service and searches those projects for relevant context.

Setup

Option 1: .preflight/config.yml (recommended — committed to repo)

# my-api/.preflight/config.yml
related_projects:
  - path: /Users/jack/Developer/auth-service
    alias: auth-service
  - path: /Users/jack/Developer/notifications
    alias: notifications
  - path: /Users/jack/Developer/shared-types
    alias: shared-types

Option 2: Environment variable (per-user)

PREFLIGHT_RELATED=/path/to/auth-service,/path/to/notifications

How it works

1. You type: "add rewards tier validation"
2. Triage detects rewards matches always_check keyword → ambiguous
3. Cross-service keywords (auth, notification) don't match, but if you had typed "add rewards with auth check" → cross-service
4. Preflight searches related projects' LanceDB indexes and contract registries
5. Returns relevant types, interfaces, and routes from those projects
6. You get context like: "auth-service has interface AuthToken { userId, tier, permissions } and POST /api/validate-tier"

This prevents the common failure mode: changing a shared type in one service and forgetting the consumers.

---

Configuration Reference

.preflight/config.yml

Drop this in your project root. Every field is optional — defaults are sensible.

# Profile controls overall verbosity
# "minimal" — only flag ambiguous+, skip clarification detail
# "standard" — default behavior
# "full" — maximum detail on every non-trivial prompt
profile: standard                          # type: "minimal" | "standard" | "full"

# Related projects for cross-service awareness
related_projects:                          # type: RelatedProject[]
  - path: /absolute/path/to/service        #   path: string (absolute)
    alias: service-name                    #   alias: string (used in output + triage matching)

# Behavioral thresholds
thresholds:
  session_stale_minutes: 30                # type: number — warn if no activity for this long
  max_tool_calls_before_checkpoint: 100    # type: number — suggest checkpoint after N tool calls
  correction_pattern_threshold: 3          # type: number — min corrections to form a pattern

# Embedding configuration
embeddings:
  provider: local                          # type: "local" | "openai"
  openai_api_key: sk-...                   # type: string — only needed if provider is "openai"

.preflight/triage.yml

Controls the triage classification engine.

# Keywords that control routing
rules:
  # Prompts containing these → always at least AMBIGUOUS
  always_check:                            # type: string[] — default: [rewards, permissions, migration, schema]
    - rewards
    - permissions
    - migration
    - schema

  # Prompts containing these → TRIVIAL (pass through)
  skip:                                    # type: string[] — default: [commit, format, lint]
    - commit
    - format
    - lint

  # Prompts containing these → CROSS-SERVICE
  cross_service_keywords:                  # type: string[] — default: [auth, notification, event, webhook]
    - auth
    - notification
    - event
    - webhook

# How aggressively to classify
# "relaxed" — more prompts pass as clear
# "standard" — balanced
# "strict" — more prompts flagged as ambiguous
strictness: standard                       # type: "relaxed" | "standard" | "strict"

.preflight/contracts/*.yml

Manual contract definitions that supplement auto-extraction:

# .preflight/contracts/api.yml
- name: RewardsTier
  kind: interface
  description: Reward tier levels and their perks
  fields:
    - name: tier
      type: string
      required: true
    - name: multiplier
      type: number
      required: true
    - name: perks
      type: string[]

Environment Variables

Variable	Description	Default
CLAUDE_PROJECT_DIR	Project root to monitor	Required
OPENAI_API_KEY	OpenAI key for embeddings	Uses local Xenova
PREFLIGHT_RELATED	Comma-separated related project paths	None
EMBEDDING_PROVIDER	local or openai	local
PROMPT_DISCIPLINE_PROFILE	minimal, standard, or full	standard

Environment variables are fallbacks — .preflight/ config takes precedence when present.

💡 Ready-to-use examples: Copy examples/.preflight/ into your project root for a working starter config with detailed comments.

---

Embedding Providers

Provider	Setup	Speed	Dimensions	Quality	Privacy
Local (Xenova)	Zero config	~50 events/sec	384	Good	100% local
OpenAI	Set OPENAI_API_KEY	~200 events/sec	1536	Excellent	API call

First run with local embeddings downloads the Xenova/all-MiniLM-L6-v2 model (~90MB). After that, everything runs offline.

---

Architecture

flowchart TB
    CC[Claude Code] <-->|MCP Protocol| PS[preflight server]
    
    PS --> PC[✈️ Preflight Core]
    PS --> DT[🎯 Discipline Tools]
    PS --> TL[🔍 Timeline Tools]
    PS --> AN[📊 Analysis Tools]
    PS --> VR[✅ Verify Tools]

    PC --> TE[Triage Engine]
    DT --> PL[Pattern Learning]
    TL --> LDB[(LanceDB\nper-project)]
    AN --> SCE[Scorecard Engine]

    TE -.-> CFG[".preflight/\nconfig.yml\ntriage.yml\ncontracts/"]
    LDB --> SP[Session Parser]
    SP --> JSONL["~/.claude/projects/\n(session JSONL files)"]

    style CC fill:#264653,color:#fff
    style PS fill:#2a9d8f,color:#fff
    style PC fill:#e76f51,color:#fff
    style DT fill:#e9c46a,color:#000
    style TL fill:#457b9d,color:#fff
    style AN fill:#6a4c93,color:#fff
    style VR fill:#2d6a4f,color:#fff
    style LDB fill:#1d3557,color:#fff

Per-Project Data Layout

~/.preflight/
├── config.json                           # Global config (embedding provider, indexed projects)
└── projects/
    ├── index.json                        # Registry: path → { hash, onboarded_at }
    ├── a1b2c3d4e5f6/                     # SHA-256(project_path)[:12]
    │   ├── timeline.lance/               # LanceDB vector database
    │   ├── contracts.json                # Extracted API contracts
    │   ├── meta.json                     # Event count, onboard timestamp
    │   └── baseline.json                 # Historical scorecard averages
    └── f6e5d4c3b2a1/
        └── ...                           # Another project

---

Troubleshooting

"Cannot find module 'vectordb'" or LanceDB import errors

LanceDB uses native binaries. If you see module resolution errors:

# Clean install with native deps rebuilt
rm -rf node_modules package-lock.json
npm install

# If still failing, check your Node version (20+ required)
node --version

On Apple Silicon Macs, make sure you're running a native arm64 Node — not Rosetta. Check with node -e "console.log(process.arch)" (should print arm64).

First run is slow (~90MB model download)

The local embedding provider (Xenova/all-MiniLM-L6-v2) downloads a ~90MB model on first use. This is a one-time cost — subsequent runs use the cached model. If the download hangs behind a corporate proxy, switch to OpenAI embeddings:

export OPENAI_API_KEY=sk-...
export EMBEDDING_PROVIDER=openai

"OpenAI API key required for openai embedding provider"

You set EMBEDDING_PROVIDER=openai (or embeddings.provider: openai in .preflight/config.yml) but didn't provide a key. Either:

• Set OPENAI_API_KEY in your environment, or
• Switch back to local: export EMBEDDING_PROVIDER=local

Tools not showing up in Claude Code

1. Make sure the MCP server is registered. Run claude mcp list — you should see preflight.
2. If missing, re-add it:

   claude mcp add preflight -- npx tsx /path/to/preflight/src/index.ts
   

3. Restart Claude Code after adding.

CLAUDE_PROJECT_DIR not set

Some tools (onboarding, session search, contracts) need to know your project root. If they return empty results:

claude mcp add preflight \
  -e CLAUDE_PROJECT_DIR=/path/to/your/project \
  -- npx tsx /path/to/preflight/src/index.ts

Or set it globally: export CLAUDE_PROJECT_DIR=/path/to/your/project

.preflight/config.yml parse errors

If you see warning - failed to parse .preflight/config.yml, your YAML is malformed. Common issues:

• Tabs instead of spaces (YAML requires spaces)
• Missing quotes around values with special characters
• Incorrect indentation under related_projects

Validate with: npx yaml-lint .preflight/config.yml or paste into yamllint.com.

No session data found during onboarding

onboard_project looks for JSONL files in ~/.claude/projects/<encoded-path>/. If nothing is found:

• Make sure you've actually used Claude Code on the project (at least one session)
• Check that CLAUDE_PROJECT_DIR matches the exact path Claude Code was opened in
• The path encoding is URL-style — /Users/jack/my-app becomes %2FUsers%2Fjack%2Fmy-app

Ollama embeddings connection refused

If using Ollama as your embedding provider and getting connection errors:

# Make sure Ollama is running
ollama serve

# Pull the embedding model
ollama pull all-minilm

# Verify it works
curl http://localhost:11434/api/embed -d '{"model":"all-minilm","input":"test"}'

---

Contributing

This project is young and there's plenty to do. Check the issues — several are tagged good first issue.

PRs welcome. No CLA, no bureaucracy. If it makes the tool better, it gets merged.

Development

git clone https://github.com/TerminalGravity/preflight.git
cd preflight
npm install
npm run build
npm test

Project Structure

src/
├── index.ts                 # MCP server entry point
├── lib/
│   ├── triage.ts            # Prompt classification engine
│   ├── session-parser.ts    # JSONL session file parser
│   ├── timeline-db.ts       # LanceDB operations
│   ├── contracts.ts         # Contract extraction & search
│   ├── patterns.ts          # Correction pattern learning
│   ├── config.ts            # .preflight/ config loading
│   ├── embeddings.ts        # Embedding provider abstraction
│   ├── state.ts             # Persistent state (logs, patterns)
│   ├── git.ts               # Git operations
│   └── files.ts             # File discovery
└── tools/
    ├── preflight-check.ts   # Unified entry point
    ├── generate-scorecard.ts # 12-category scoring + trends
    └── ...                  # One file per tool

License

MIT — do whatever you want with it.