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Gives AI agents the project context that currently only exists inside a senior engineer's head.","maintainers":[{"name":"10kdevs","email":"ygbhupesh003@gmail.com"}],"readme":"# MATHA\n\n**The persistent cognitive layer for AI-assisted development.**\n\n---\n\nYou gave the AI your entire codebase. You explained the business logic for two hours. You did a Q&A, walked through every edge case, corrected every misunderstanding.\n\nThen you closed the session.\n\nThe next day you opened a new one. And it remembered nothing.\n\nNot the HWM calculation that took three sessions to get right. Not the rule about deposit events not triggering profit cycles. Not the assumption that broke everything in V1 and cost you twelve days to fix. Gone. Cold start. Again.\n\nThis is not an AI intelligence problem. Every tool — Cursor, Copilot, Claude Code, Windsurf — is stateless by design. They are powerful and amnesiac in equal measure. The larger your project, the more painful that amnesia becomes.\n\nMATHA fixes this.\n\n---\n\n## What MATHA Does\n\nMATHA is a local MCP server that runs alongside your project and gives any AI agent the context that currently only exists inside a senior engineer's head.\n\nIt captures three things that no markdown file can hold:\n\n**Intent** — why the project exists, what the non-negotiable rules are, what it explicitly does not do. Not features. The reasoning behind them.\n\n**Decisions** — every assumption that broke, every correction that was made, every \"no that's wrong\" moment that cost you days. Recorded by the agent itself over MCP as a by-product of the work (with a CLI fallback), not by humans writing docs after the fact.\n\n**Behaviour contracts** — assertions that must remain true for a component. Stored with the component, surfaced whenever an agent is about to touch it, with violation history escalating the warning.\n\nEvery session writes back what it learned. Every session starts warmer than the last. The brain stays on.\n\n---\n\n## How It Works\n\nMCP is the primary surface: the agent calls `matha_brief` before touching code and `matha_record` when it learns something. The CLI is for setup, diagnostics, and agents without MCP support:\n\n```bash\n# Once per project — seeds the brain from your project's intent and git history\nmatha init\n\n# Print the project brief for a scope (copy-paste fallback for non-MCP agents)\nmatha before --scope src/payments/ --intent \"change retry logic\"\n\n# Record what a session learned (fallback for non-MCP agents)\nmatha after\n\n# Diagnose the setup: which brain, schema status, record counts, staleness\nmatha doctor\n\n# Upgrade .matha/ after a matha version bump\nmatha migrate\n```\n\nThe write-back means a correction never has to be made twice.\n\n### Keeping the brain trustworthy\n\nA memory that silently rots is worse than no memory. matha treats staleness and drift as first-class:\n\n```bash\n# Triage queue: confirm, retire, or skip unconfirmed and possibly-stale records\nmatha review\n\n# Commits since the last recorded knowledge that no record covers (backfill list)\nmatha catchup\n\n# Match a git diff against the brain — advisory in CI, --strict to fail on criticals\nmatha check --diff origin/main\n\n# Declare an admin boundary: pinned, confirmed, always CRITICAL on a path match\nmatha boundary add --paths db/schema/ --rule \"Schema changes require DBA sign-off\"\n\n# Human-readable, PR-diffable brain summary / self-contained HTML report\nmatha export --md --out BRAIN.md\nmatha ui\n\n# Seed an existing codebase: agent proposes records from your docs, you confirm\nmatha onboard\n\n# Wire the Claude Code SessionStart hook that injects the brief automatically\nmatha hooks --install\n```\n\nRecords whose code changed after they were written are flagged `possiblyStale` and rank-penalized — surfaced, never silently wrong. Agent writes land as `probable`; only a human (`matha after`, `matha review`) can mark knowledge `confirmed`. The codemap refreshes itself incrementally on read (a persisted commit cursor means even a huge history is analysed once, then only new commits).\n\n---\n\n## Installation\n\n```bash\nnpm install -g @10kdevs/matha\n```\n\nZero-install first run:\n```bash\nnpx @10kdevs/matha init\n```\n\nRequires Node.js 20+. No API key. No cloud dependency. All data stays in your repository.\n\n---\n\n## Connecting To Your IDE\n\nAfter `matha init`, connect MATHA to your IDE via MCP.\n\nThe init command writes `.matha/mcp-config.json` with the exact config for your machine. Add it to your IDE's MCP configuration.\n\n**Claude Code:**\n```bash\n# .matha/mcp-config.json contains the correct config\n# Add to your Claude Code MCP settings\n```\n\n**Cursor:**\n```json\n{\n  \"mcpServers\": {\n    \"matha\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@10kdevs/matha\", \"serve\", \"--project\", \"/path/to/your/project\"]\n    }\n  }\n}\n```\n\nOnce connected, your AI agent can call `matha_brief()` as its first action in any session — receiving the full project context before writing a line. The server resolves the right `.matha/` from `--project`, the IDE's workspace roots, or the file paths in each tool call — and if none resolves, it says so explicitly rather than serving an empty brain. Run `matha doctor` any time to see exactly which brain is being served.\n\n---\n\n## The Brain\n\nMATHA's knowledge lives in `.matha/` in your repository. Committed to version control. Owned by your team. Never sent anywhere.\n\n```\n.matha/\n├── hippocampus/        intent, rules, decisions, danger zones\n├── cerebellum/         behaviour contracts, violation log\n└── cortex/             stability map, co-change graph, boundaries\n```\n\nThe codemap (cortex) builds itself from git history. Files that change together are linked. Files with low churn and high connectivity are classified frozen — AI agents are warned before touching them.\n\nRetrieval is hierarchical: a danger zone recorded against `src/payments/` fires for `src/payments/retry.ts`. Records written by an agent carry `probable` confidence until a human confirms them; garbage inputs (empty components, one-letter \"corrections\") are rejected at write time.\n\n---\n\n## MCP Tools\n\nAI agents connected via MCP have access to a deliberately small surface:\n\n```\nmatha_brief(scope?, intent?, filepaths?)   Token-budgeted project context + scored matches\nmatha_match(scope?, intent, filepaths?)    What does the brain know about this change?\n                                             (scope omitted → keyword search over intent alone)\nmatha_record(type, ...)                    The one write tool:\n                                             decision | danger | contract   (new knowledge)\n                                             violation | supersede | retire (lifecycle)\nmatha_refresh()                            Rebuild the codemap from git history\n```\n\nAgents can create knowledge and maintain it — log a contract violation, supersede a decision that proved wrong, retire one that no longer applies. What they cannot do is self-promote: everything an agent writes is at most `probable`, and declared boundaries are CLI-only.\n\nThere is also an MCP **prompt**, `matha_context`, which injects the brief plus the standing \"record what you learn\" instruction — IDEs that support MCP prompts can pull it at session start with zero configuration.\n\nRetrieval is scored (structural path match × lexical relevance × confidence × recency) and the brief stays under a ~1,500-token budget — criticals are ranked first, so they are never the part that gets truncated. Every response includes `diagnostics.brainDir` — you always know which brain answered.\n\nTwo things worth knowing about `brief.truncated` and `recentDecisions`: `truncated` means *something* didn't fit the budget (an oversized record, or the section budget itself), not that the whole response was cut short — check `matchResults.length` and `tokenEstimate` for the real picture. `recentDecisions` is intentionally global (most-recent-first across the whole brain), not scope-filtered — it's the \"what's been learned lately\" section; scope-relevant knowledge surfaces separately in `matchResults`.\n\nThe sub-10ms warm p95 figure (from the eval harness) describes in-process retrieval inside a long-lived MCP server session with a warm cache — a `matha before` CLI invocation pays Node's process-startup cost on top of that (typically 150–250ms) and isn't the number that claim describes.\n\n### Tuning for large, high-file-count repos\n\nOn a repo with a large fraction of files classified `frozen`, `matha check --strict` can flag an unusually large share of an ordinary diff as CRITICAL. Set `frozenFileSeverity: \"warning\"` in `.matha/config.json` to keep the signal without the block:\n\n```json\n{ \"schema_version\": \"1.1.0\", \"frozenFileSeverity\": \"warning\" }\n```\n\nThe classifier itself also guards against the most common cause: a file touched within the last 60 days never classifies `frozen`, regardless of its long-run churn average — a bounded/incremental analysis window can otherwise make a historically active, business-critical file look \"settled\" just because few of its changes fall inside the analysed window.\n\n---\n\n## Benchmarks\n\n```bash\nnpm run eval:report\n```\n\nRuns matha's own golden-set retrieval eval (the CI gate in `tests/eval/golden.test.ts`) standalone, against a naive substring-match baseline (matha's own pre-scoring behaviour), and writes [`BENCHMARKS.md`](BENCHMARKS.md). Deterministic — the fixture clock is fixed, so recall/precision are exactly reproducible on any machine.\n\nThis is an in-house fixture, not a public academic benchmark — there is no published dataset for \"retrieve the right decision for a given file + intent,\" which is a narrower, more structural task than conversational long-memory QA. The numbers are honest about that scope, not a leaderboard claim.\n\n---\n\n## Initialising From An Existing Document\n\nIf your project already has a BRD, spec, or requirements document:\n## Setting Up Your Project Brain\n\nBefore running `matha init`, generate a `requirements.md` file that \ncaptures your project's intent, rules, and boundaries in a format \nMATHA understands deeply.\n\n**Paste this prompt into any AI assistant:**\n```\nI am setting up MATHA — a persistent cognitive layer for AI-assisted \ndevelopment. I need you to generate a requirements.md file for my \nproject that MATHA will parse during initialisation.\n\nMy project: [describe your project in 2-3 sentences]\n\nGenerate a requirements.md with exactly these sections:\n\n## Overview\nA concise paragraph explaining what problem this project solves \nand why it exists. Focus on the WHY, not the features.\n\n## Business Rules\nA bullet list of non-negotiable rules that must always be true.\nThese are constraints the codebase must never violate.\nExamples: calculation logic, data integrity rules, \nfinancial constraints, domain-specific invariants.\n\n## Out of Scope\nA bullet list of things this project explicitly does NOT do.\nThese are boundaries that protect the system from scope creep.\n\n## Owner\nThe name or team responsible for this project.\n\nBe specific and precise. Vague rules are useless. \nEach rule should be concrete enough that a developer \nwho has never seen the codebase understands exactly \nwhat it means.\n```\n\nThen run:\n\n```bash\nmatha init --from requirements.md\n```\n\nMATHA will parse the document, show you what it found, \nand let you confirm or override before writing anything.\n\n\nMATHA parses business rules, boundaries, and intent from the document and pre-fills the init prompts. You review and confirm. Nothing is written without your sign-off.\n\n---\n\n\n## What MATHA Is Not\n\nMATHA does not generate code.\n\nMATHA does not replace your IDE, your AI model, or your version control.\n\nMATHA does not require a specific model, a specific IDE, or a specific language. If your tool supports MCP, it connects to MATHA on day one.\n\nMATHA does not send your data anywhere. The brain lives in your repository.\n\n---\n\n## The Real Problem It Solves\n\nThere is a moment every developer who uses AI tools eventually hits.\n\nThe project is large enough that you cannot hold all of it in your head. The AI cannot hold all of it in its context window. You find yourself explaining the same decision three times across three sessions. You find a bug and you are not confident which part of the codebase is responsible. You realise you have forgotten the core logic you wrote four weeks ago.\n\nAt that moment, the AI is not the problem. The absence of accumulated understanding is the problem.\n\nMATHA is the accumulated understanding.\n\nIt is what the senior engineer carries in their head after three years on a project — the why behind every decision, the scars from every production incident, the rules that cannot be broken and the code that cannot be touched without knowing why it works the way it does.\n\nThat knowledge survives sessions. It survives team changes. It survives the moment you come back to a project six months after you last touched it.\n\nThe code gets better. The context never resets. The brain stays on.\n\n---\n## Auto-Wiring MATHA (Set Once, Works Forever)\n\nAfter connecting MATHA as an MCP server, add this rule to your IDE's\nAI settings. It eliminates the manual before/after step entirely.\n\n**Cursor** → Settings → Rules for AI  \n**Windsurf** → Settings → AI Rules  \n**Claude Code** → CLAUDE.md in project root  \n\nPaste this rule:\n\n> At the start of every conversation, call matha_brief() before\n> writing any code. Review all rules, danger zones, and prior\n> decisions. Flag any hasCritical:true results before proceeding.\n> After completing work, call matha_record() for any\n> assumption that changed during the session.\n\nOne rule. Every session starts warm.\n\n---\n\n## Case Study\n\n[How MATHA would have saved 12 days on a real PAMM trading platform →](docs/pamm-case-study.md)\n\n---\n\n## Contributing\n\n[CONTRIBUTING.md](CONTRIBUTING.md)\n\n---\n\n## License\n\nMIT — owned by nobody, usable by everyone.\n\n---\n\n*Built because the problem was real.*","readmeFilename":"README.md"}