io.github.cdeust/automatised-pipeline

automatised-pipeline — codebase intelligence as an MCP server

What An Agent Can Ask · Getting Started · Pipeline · Tools · Architecture · Zetetic Standard

Companion projects:
Cortex — persistent memory that consolidates and reconsolidates across sessions
zetetic-team-subagents — 97 genius reasoning agents + 18 team specialists
prd-spec-generator — TypeScript PRD generator that consumes our graph intelligence

Every AI coding assistant hits the same wall: you ask it to change handle_tool_call, and it either hallucinates a function that was renamed last week, edits something in the wrong community of the codebase, or silently breaks a call chain three modules away. Agents operate on strings; codebases have structure. The gap is where bugs live.

automatised-pipeline is a Rust MCP server that indexes any Rust, Python, TypeScript, Java, Kotlin, Swift, Objective-C, C, C++, or Go codebase into a LadybugDB property graph, resolves imports and call chains across files, detects functional communities via Leiden-class community detection, traces execution flows from entry points, builds a hybrid BM25 + sparse TF-IDF + RRF search index, and exposes all of it to AI agents through 24 MCP tools.

It is the codebase intelligence layer that sits between a finding ("this bug exists") and a PRD ("here is the fix, here is what it affects, here is what it must never break"). It is read-only intelligence — it never writes code, opens PRs, or runs CI. It tells the system what is true about the code so the next stage can reason without guessing.

One pipeline stage = one MCP tool. 10 stages. 24 tools. 12,000+ lines of Rust. 434 tests. Zero warnings. Every constant sourced.

What an agent can ask it

analyze_codebase(path: "/path/to/project", output_dir: "/tmp/run")
  → index + resolve + cluster + build search index in one call
  → 430 nodes, 400 edges, 216 communities, 35 processes on our own codebase

search_codebase(graph_path, query: "process incoming tool requests")
  → hybrid ranked results: BM25 lexical + sparse TF-IDF semantic + RRF fusion
  → returns: handle_tool_call (score 0.021), dispatch_request (0.020), ...

get_context(graph_path, qualified_name: "src/main.rs::handle_tool_call")
  → 360° view: community membership, process participation,
    incoming calls, outgoing calls, types used, types that use it
  → did-you-mean suggestions when the symbol isn't found exactly

get_impact(graph_path, qualified_name)
  → blast radius: every process that transits this symbol, every community it touches
  → the answer to "what breaks if I change this?"

detect_changes(graph_path, diff_text OR base_ref+head_ref)
  → git diff → affected symbols → impacted communities → touched processes
  → risk score for the change

validate_prd_against_graph(prd_path, graph_path)
  → does the PRD reference real symbols? (symbol hallucination check)
  → does "scoped to X" match the actual community count?
  → does "doesn't affect main" hold against the call graph?

check_security_gates(graph_path, changed_symbols)
  → auth-critical community touch · unsafe symbol · public API change ·
    unresolved imports · test coverage gap

verify_semantic_diff(before_graph_path, after_graph_path)
  → what nodes/edges appeared, what disappeared, what dangles,
    new cycles via Tarjan SCC, regression score with verdict

Getting started

Prerequisites

• Rust 1.94+ (rustup install stable)
• CMake (LadybugDB builds its C++ core from source — ~5 minutes first build, cached after)

Clone + build

git clone https://github.com/cdeust/automatised-pipeline.git
cd automatised-pipeline
cargo build --release
# First build: ~5 minutes (compiles LadybugDB C++ core)
# Subsequent builds: <1 second incremental

Register the MCP server

The repo ships a .mcp.json that Claude Code picks up automatically when you open the directory:

{
  "mcpServers": {
    "ai-architect": {
      "command": "cargo",
      "args": ["run", "--quiet", "--release", "--manifest-path", "Cargo.toml", "--", "--profile", "core"]
    }
  }
}

Or register globally (recommended agent setup — the core profile):

claude mcp add ai-architect -- /absolute/path/to/target/release/automatised-pipeline --profile core

Tool profiles

The server registers one of two tool sets, chosen once at startup:

Select with the --profile flag or the AP_PROFILE environment variable (the flag wins):

automatised-pipeline --profile core   # agent-facing 8
AP_PROFILE=core automatised-pipeline  # same, via env
automatised-pipeline                  # default: full (all 24)

The default stays full until the next major version — shrinking the default tool surface is a breaking change. New agent installations should opt into core: analyze_codebase already runs index + resolve + cluster in one call, so the 16 hidden tools are pipeline plumbing an agent never needs, and hiding them keeps the tool prompt small.

First run

# Run the binary directly to verify the handshake
./target/release/automatised-pipeline

# Or exercise it via stdio JSON-RPC:
printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
  '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"health_check","arguments":{}}}' \
  | ./target/release/automatised-pipeline

Use with other MCP hosts

The server is a self-contained stdio binary — any MCP host can launch it. Install once:

cargo install ai-architect-mcp   # installs the `automatised-pipeline` binary into ~/.cargo/bin

Install into your agent host (auto-config)

One command detects your installed hosts and writes the right MCP config for each — never clobbering the rest of the file:

automatised-pipeline install

It configures the top six hosts it detects: Claude Code (~/.claude.json), Codex CLI (~/.codex/config.toml), Gemini CLI (~/.gemini/settings.json), Cursor (~/.cursor/mcp.json), VS Code (Code/User/mcp.json), and Zed (~/.config/zed/settings.json).

• Never clobbers. The existing config is parsed; only our ai-architect entry is added or updated; every other server survives. A file it cannot safely parse is never overwritten — it prints the exact entry to paste by hand.
• Zed JSONC. Zed's settings.json allows comments, which strict JSON editing would destroy, so install refuses to edit it in place and prints the snippet + instructions instead (your comments stay byte-for-byte).
• Codex TOML is edited comment- and format-preserving (via toml_edit).
• Flags: --dry-run (print planned changes, write nothing), --only <host> / --skip <host> (filter; --only forces a host even if undetected), --with-hooks (also register the Grep/Glob PreToolUse hook, see below). Re-running is idempotent (a second run reports "no change").
• Uninstall: automatised-pipeline uninstall removes exactly our entries (and the hook), leaving everything else intact.

automatised-pipeline install --dry-run                 # preview
automatised-pipeline install --only cursor --only zed  # just these
automatised-pipeline install --with-hooks              # + the grep→graph hook
automatised-pipeline uninstall                         # remove our entries

Binary → first query. Measured on this machine (2026-07): install completes in ~1.3 s (dominated by process/DB startup; the config write itself is sub-second); analyze_codebase on this repo's own src/ (114 files → 16.5k nodes, 16.3k edges — index + resolve + cluster) takes ~12 s wall; the first search_codebase returns instantly. So once the binary exists, install → analyze → first graph query is ~15 s — well under the 2-minute target. The one-time cargo build --release (~5 min, compiling the LadybugDB C++ core) is a separate, before-the-clock step.

Fail-open grep→graph hook

automatised-pipeline install --with-hooks registers a Claude Code PreToolUse hook (matcher Grep|Glob) that runs automatised-pipeline hook-augment. Before a Grep/Glob in a project that has an ai-architect graph, it injects a one-line suggestion to consider search_codebase/query_graph first. Cardinal rule: it never blocks the tool call — no graph, an unparseable payload, or any error → it prints nothing and exits 0. Hook registration is opt-in (the --with-hooks flag), never default.

Or configure a host by hand

The CLI commands below assume ~/.cargo/bin is on your PATH. GUI hosts (Cursor, Windsurf, VS Code) may not inherit your shell PATH — in the JSON configs, replace automatised-pipeline with the output of which automatised-pipeline. Use the core profile (8 read-only tools) for agent hosts.

Gemini CLI

gemini mcp add -e AP_PROFILE=core ai-architect automatised-pipeline

Or install as an extension (this repo ships a gemini-extension.json):

gemini extensions install https://github.com/cdeust/automatised-pipeline

OpenAI Codex CLI (also picked up by the ChatGPT desktop app and Codex IDE extension — they share ~/.codex/config.toml)

codex mcp add ai-architect -- automatised-pipeline --profile core

Or in ~/.codex/config.toml:

[mcp_servers.ai-architect]
command = "automatised-pipeline"
args = ["--profile", "core"]

Cursor — .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):

{
  "mcpServers": {
    "ai-architect": {
      "command": "automatised-pipeline",
      "args": ["--profile", "core"]
    }
  }
}

Windsurf — ~/.codeium/windsurf/mcp_config.json: same mcpServers block as Cursor.

VS Code — .vscode/mcp.json:

{
  "servers": {
    "ai-architect": {
      "type": "stdio",
      "command": "automatised-pipeline",
      "args": ["--profile", "core"]
    }
  }
}

OpenAI Agents SDK (Python)

from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    name="ai-architect",
    params={"command": "automatised-pipeline", "args": ["--profile", "core"]},
) as server:
    agent = Agent(name="Assistant", mcp_servers=[server])

The pipeline

Every stage is a tool. Stages build on each other but are independently callable. The pipeline is serial in logical order but MCP calls are stateless — you can re-run stages 3a-3d on a fresh codebase without re-running stages 1-2.

Stages 5 (PRD generation), 7 (implementation), 10 (benchmark), 11 (deployment), 12 (PR) belong to other systems in the pipeline: prd-spec-generator, the coding agent, CI, and gh. This project is the read-only intelligence half.

24 MCP Tools

Every tool takes structured JSON arguments via the MCP protocol and returns a structured JSON response. No LLM is called from inside any tool — intelligence is the agent's job; the tool's job is safe, fast data movement with invariants.

Stage 0:  health_check
Stage 1:  extract_finding · refine_finding
Stage 2:  start_verification · append_clarification · finalize_verification · abort_verification
Stage 3a: index_codebase · query_graph · get_symbol
Stage 3b: resolve_graph · lsp_resolve
Stage 3c: cluster_graph · get_processes · get_impact
Stage 3d: search_codebase · get_context · analyze_codebase · detect_changes
Stage 3e: index_history
Stage 4:  prepare_prd_input
Stage 6:  validate_prd_against_graph
Stage 8:  check_security_gates
Stage 9:  verify_semantic_diff

Each tool has a JSON Schema enforced at the wire, reason codes on error (no cryptic protocol errors), and a receipt-style response with timing and counts.

Agent installs rarely need all 24 — the core profile (see Tool profiles) registers just the 8 code-intelligence tools.

Team-shared graph artifact (optional)

index_codebase can commit a compressed snapshot of the graph so teammates who clone the repo never have to cold-index it.

• index_codebase with "export_artifact": true writes, after a successful index, a tar → zstd snapshot to <path>/.automatised-pipeline/graph.zst plus a graph.meta.json sidecar (schema version, git sha, tool version, node/edge counts). It also appends a .gitattributes entry (.automatised-pipeline/graph.zst binary merge=ours) so the committed binary never produces merge conflicts across branches. Commit both files.

• index_codebase with "bootstrap": true — when there is no local graph at <output_dir>/graph but a committed artifact is present — decompresses the snapshot instead of cold-indexing. Staleness is checked first by comparing the artifact's git sha with the repo's current HEAD:

  • shas equal → import;
  • shas differ → by default the import is refused and a full index runs; a stderr line reports how many commits behind the artifact is, and the tool response carries a bootstrap_skipped object;
  • "accept_stale": true → import the stale snapshot anyway, and the response carries a stale_artifact {artifact_sha, head_sha, commits_behind} report so a stale graph is never mistaken for a fresh one.

  An import failure also falls back to a full index explicitly (logged to stderr), never a silent partial graph.

All three flags default to false, so existing behavior and the core/core8 profiles are unchanged. The artifact is entirely optional: without it, index_codebase cold-indexes exactly as before.

Post-import incremental fill (re-index only the artifact_commit..HEAD diff instead of a full re-index) is tracked in #62 — it needs a changed-files-only indexer, which AP does not yet have.

Architecture

Rust MCP server, hand-rolled stdio JSON-RPC 2.0 (no SDK — we own the wire). Clean Architecture with module boundaries.

transport (stdio, JSON-RPC framing)
      ↓
server/main.rs  (request dispatch, tool registry)
      ↓
handlers (do_* functions, one per tool)
      ↓
core modules:
    graph_store        — LadybugDB port (Cypher + UNWIND + prepared statements)
    parser/{rust,python,typescript,mod}  — tree-sitter AST extractors
    indexer            — walk + parse + persist pipeline
    resolver           — cross-file import/call/impl resolution
    lsp_{client,resolver}  — optional LSP deep resolution
    clustering         — inline Louvain + C2 repair + process tracing
    search/{bm25,vector,rrf,mod}  — hybrid search (Tantivy + sparse TF-IDF + RRF)
    prd_input          — stage 4: bundle for prd-spec-generator
    prd_validator      — stage 6: validate PRD claims against graph
    security_gates     — stage 8: auth/unsafe/API/imports/coverage checks
    semantic_diff      — stage 9: before/after graph regression scoring
    git_diff           — diff parser + symbol mapping

Dependencies

Sixteen crates. Nothing speculative; everything justified.

Deliberately not included: async runtime (we're stdio-blocking), HTTP client, LLM SDK, embedding model runtime (sparse TF-IDF replaces it at zero dep cost).

Storage

Graphs are per-finding by design (Lamport's isolation invariant): each finding gets its own LadybugDB instance at <output_dir>/runs/<run_id>/findings/<finding_id>/graph/. Zero-coordination concurrency, trivial cleanup, no cross-finding state leakage. Redundant indexing for shared codebases is acknowledged and mitigated in a later optional cache layer — not shoehorned into the core.

Configuration — max_db_size

Every LadybugDB Database this crate opens reserves virtual address space up front, sized by max_db_size. lbug's own default (SystemConfig::default()) is 1 << 43 = 8 TiB per instance; with graph_cache's MAX_CACHED_GRAPHS = 8 entries live in the read-path cache at once, that is a 64 TiB worst case (issue #25). src/graph_store.rs::system_config() is the single choke point every GraphStore::open_or_create call resolves through, in this precedence order:

1. AP_LBUG_TEST_MAX_DB_SIZE — test-only, set for every cargo test process via .cargo/config.toml's [env] table (512 MiB / 2^29, issue #21). Always wins when present, so cargo test behavior is independent of the production knob below.
2. AP_LBUG_MAX_DB_SIZE — production override, unset by default. Bytes, must be a power of two and at least 8 MiB (lbug's own BufferManager::verifySizeParams floor). An invalid value is rejected with an actionable error at GraphStore::open_or_create time — never a silent fallback.
3. Default: 8 GiB (1 << 33 bytes) when neither var is set. Derivation: measured every lbug graph-DB file reachable on the machine that produced this fix (75 distinct graphs — see the table below); the largest was 495,849,472 bytes (~473 MiB, a cortex-viz index run including node_modules). Sizing rule: next power of two ≥ (largest measured × 16), floor 8 GiB. 473 MiB × 16 ≈ 7.39 GiB is below the floor, so the floor (already a power of two) applies.

Re-measure and raise AP_LBUG_MAX_DB_SIZE (or the compiled-in default) if a materially larger workload is observed in production — e.g. indexing a monorepo with node_modules included.

Measured graph sizes (2026-07-15, du -k on every graph file found under ~/.cache/cortex/code-graphs/*/graph, ~/.cortex/ap_graph/graph, and **/.prd-gen/graphs/*/graph), top 10 of 75:

Total across all 75 measured graphs: ~4.87 GiB. Every graph other than the top 3 (which include node_modules) is under 150 MiB — the node_modules-inclusive runs are the actual worst case driving the sizing rule above.

The zetetic standard

Inherited from zetetic-team-subagents. Not a prompt suggestion — an enforcement rule that holds in code.

In this codebase it concretely means:

1. Every algorithm traces to a source. Louvain → Blondel et al. 2008. Leiden C2 repair → Traag et al. 2019. RRF → Cormack, Clarke, Büttcher 2009. SCC → Tarjan 1972. BM25 via Tantivy → Robertson et al. 1994.
2. Every named constant has a // source: comment. RRF_K = 60 cites Cormack 2009. BULK_BATCH_SIZE = 500 cites Kùzu/LadybugDB tuning. PARSE_TIMEOUT_MICROS = 5_000_000 is justified in the block above it.
3. No invented numbers. Where a value was chosen by judgment, the comment says so ("heuristic, not paper-backed") and cites its operational justification.
4. Tool responses cite the spec that governs each error reason. unsafe finding_id (spec §5.1.4, §9.3 Q4): must match [A-Za-z0-9._-]+ — callers see which rule they violated.
5. When a capability can't be proved at spec time, the tool degrades gracefully and says so in plain language. Example: lsp_resolve on a stub binary returns lsp_probe_failed: found on PATH but didn't respond as an LSP server (stdout closed immediately; likely a stub, proxy, or non-LSP binary) — not a cryptic protocol error.

Security

Four CRITICAL, four HIGH, three MEDIUM findings were surfaced by a security-auditor agent pass and fixed in commit 512d683:

• Cypher injection via insert_edge → centralized cypher_str() escaping (\ first, then ')
• Git argument injection → validate_git_ref rejects --, newlines, NUL; -- separator before refs
• Arbitrary binary execution via lsp_command → strict allowlist (rust-analyzer, pyright, pyright-langserver, typescript-language-server)
• Symlink traversal → fs::symlink_metadata + MAX_DEPTH
• Resource exhaustion → MAX_FILES=100_000, MAX_FILE_BYTES=10 MB, MAX_TOTAL_BYTES=2 GB, MAX_DEPTH=64
• Tree-sitter pathological input → set_timeout_micros(5_000_000) + MAX_PARSE_BYTES=1 MB
• query_graph read-only → forbidden-keyword whole-word filter (CREATE/DELETE/MERGE/SET/REMOVE/DROP/ALTER/CALL/LOAD)
• graph_path filesystem safety → validate_graph_path_safe() before any remove_dir_all
• LSP rootUri → RFC 3986 percent-encoding
• Diff line overflow → DIFF_LINE_MAX = u64::MAX / 2 guard

Each fix has a test that asserts the exploit is now rejected. Run cargo test to see 434 tests pass including the exploit-regression suite.

Scale

Verified by the dba agent through compile-and-run probes against lbug 0.15.3:

The bulk-insert path uses UNWIND with a typed struct schema (the engineer who wrote the first version used LogicalType::Any which fails the binder — the typed struct form works). Prepared statements are cached in a RefCell<HashMap<query, PreparedStatement>> on the GraphStore. Sparse TF-IDF replaces the dense N × V × 4B matrix — 30.5× smaller on our own codebase (108 KB vs 3.2 MB) and scales linearly with non-zero terms rather than vocab size. Clustering eliminated probe_node_label_for_process (per-node Cypher round-trip) in favor of a single in-memory HashMap<id, label> population pass.

500-file synthetic Rust fixture indexes in ~38 seconds end-to-end (parse + resolve + cluster + search index), down from the pre-audit implied "5 min – 1 hour" bracket.

Falsifiable evidence — graph tools vs a Grep/Glob/Read baseline

The core proposition — a graph query beats file-by-file exploration — is measured, not asserted. benchmarks/eval_headtohead/ is a pre-registered (PRE_REGISTRATION.md, committed before execution), two-condition, head-to-head evaluation over a committed 4-language corpus (Python, TypeScript, Go, Rust), 20 questions across 5 capability dimensions. Every number below is a field in benchmarks/eval_headtohead/results.json, regenerable by benchmarks/eval_headtohead/reproduce.sh (no network, no API key). Provenance and the honest negative are in that folder's MANIFEST.md.

Pre-registered hypotheses H1 (tokens), H2 (tool calls), H3 (precision on impact queries) are SUPPORTED; H4 (recall no-regression) is FALSIFIED and we say so: the graph's recall is 0.83 vs the substring baseline's 1.00, because AP misses a Go program entry (get_processes classification), some cross-language type-usage edges, and a Rust higher-order call. Those four lost questions are in raw_results.json — a sweep that reports only wins is not evidence. The blinded LLM-as-a-Judge answer-quality leg is config-gated (AP_EVAL_JUDGE_CMD) and was budget-gated off for the published run; the deterministic precision/recall/token/tool-call numbers above stand on their own.

Integration with the rest of the stack

                 ┌─────────────────────────────────────────┐
                 │           Claude Code agent             │
                 └────────────┬────────────────────────────┘
                              │ MCP (stdio JSON-RPC)
                              ↓
      ┌──────────────────────────────────────────────────┐
      │             automatised-pipeline                 │  ← this repo
      │  stage 0 · 1 · 2 · 3a-e · 4 · 6 · 8 · 9          │
      │  Rust · LadybugDB · tree-sitter · Tantivy        │
      └──────┬──────────────────┬────────────────────────┘
             │                  │
             │                  └────→  stage 5 (PRD gen)
             │                          [prd-spec-generator]
             ↓                          TypeScript / Node
     ┌─────────────────┐                    │
     │     Cortex      │                    │
     │  memory engine  │ ←──────────────────┘
     │  PostgreSQL +   │
     │    pgvector     │
     └─────────────────┘
             ↑
             │  cross-session memory for findings,
             │  decisions, lessons learned
             │
     ┌─────────────────────────────┐
     │  zetetic-team-subagents     │
     │  97 genius + 18 specialists │
     │  problem-shape routing      │
     └─────────────────────────────┘

• Cortex — every architectural decision made during a pipeline run gets remembered. When the next finding touches a similar area, Cortex surfaces the prior reasoning before you re-derive it.
• zetetic-team-subagents — the genius agents (Shannon, Lamport, Simon, Popper, Feynman, Fermi, dba, architect, security-auditor, engineer) designed this project stage by stage. Every major decision in stages/*.md traces to an agent dispatch.
• prd-spec-generator — consumes our stage-4.prd_input.json artifact via disk or MCP-to-MCP query of search_codebase / get_context / get_impact. Each in its ideal language: our performance-critical graph work in Rust, their document generation in TypeScript.

Testing

cargo test                                          # 434 tests, full suite
cargo test --release --test scalability_bench       # 500-file synthetic fixture
cargo test --release --test lbug_bulk_investigation # dba's 9 UNWIND probes
cargo test --release --test stage3a_integration     # end-to-end per sub-stage
cargo test --release --test stage9_integration      # before/after diff
cargo check                                         # zero warnings required
cargo build --release                               # release binary

Every stage has an integration test with fixture data. The lbug_bulk_investigation test is intentionally preserved — it's the compile-and-run proof that dba's UNWIND pattern works, kept for regression protection and documentation.

Repository layout

automatised-pipeline/
├── src/
│   ├── main.rs                    ← MCP server, 24 tool handlers
│   ├── tool_schemas.rs            ← JSON Schemas for every tool
│   ├── lib.rs                     ← re-exports for integration tests
│   ├── graph_store.rs             ← LadybugDB port (UNWIND + prepared + cached)
│   ├── parser/
│   │   ├── mod.rs                 ← language dispatch
│   │   ├── rust.rs · python.rs · typescript.rs
│   ├── indexer.rs                 ← walk + parse + persist
│   ├── resolver.rs                ← cross-file resolution
│   ├── lsp_client.rs              ← minimal LSP probe + client
│   ├── lsp_resolver.rs            ← LSP-backed deep resolution
│   ├── clustering.rs              ← Louvain + C2 repair + BFS process tracing
│   ├── search/
│   │   ├── mod.rs                 ← orchestration, get_context, 3-layer qn lookup
│   │   ├── bm25.rs · vector.rs · rrf.rs
│   ├── prd_input.rs               ← stage 4
│   ├── prd_validator.rs           ← stage 6
│   ├── security_gates.rs          ← stage 8
│   ├── semantic_diff.rs           ← stage 9
│   └── git_diff.rs                ← diff parsing + symbol mapping
├── stages/                        ← locked spec per stage (Shannon, then engineer implements)
│   ├── stage-1.md · stage-2.md · stage-3.md · stage-3b.md · stage-3c.md
│   ├── stage-6.md · stage-8.md
│   ├── stage-1.review.md · stage-3-db-evaluation.md · stage-3-research.md
│   └── decisions/                 ← Popper / Lamport / Simon verdicts per decision
├── tests/
│   ├── stage{3a,3b,3c,3d,4,6,8,9}_integration.rs
│   ├── multilang_integration.rs
│   ├── stage3d_hybrid_search.rs
│   ├── scalability_bench.rs
│   ├── lbug_bulk_investigation.rs
│   ├── tfidf_size_report.rs
│   └── fixtures/multilang/        ← sample.rs · sample.py · sample.ts
├── .claude/
│   ├── agents/                    ← 18 specialists + 97 genius agents
│   ├── skills/ · commands/ · tools/ · hooks/
│   └── scripts/
├── .mcp.json
├── NOTES.md                       ← stages table + growth rule
├── Cargo.toml
└── README.md

The zetetic decisions behind the build

Every major architectural decision was made by a genius agent with a specific problem shape. Stored in stages/decisions/*.md and in Cortex.

Agents are spawned via zetetic-team-subagents; each genius is a reasoning pattern (not a persona) with canonical moves and primary-source citations.

Status

Public repo, MIT licensed. Security audit fixes are in, correctness fixes are in, scale fixes are in, stages 4/6/8/9 are live, but every capability marked "live" above has been verified end-to-end on this machine, not yet in a production context.

What works today: indexing Rust, Python, TypeScript, Java, Kotlin, Swift, Objective-C, C, C++, and Go codebases end-to-end, resolving cross-file relationships, clustering into communities, tracing processes from entry points, hybrid search, PRD input preparation, PRD claim validation, security gate checking, before/after regression detection.

What's deferred:

• Cross-file indexer batching to unlock the full 38× UNWIND win (currently 1.17× aggregate; per-edge rate is already 0.143 ms)
• is_unsafe extraction in the Rust parser (stage 8 S2 runs in info-skip mode pending this)
• LSP-based deep method resolution on inferred types
• Multi-repo / workgroup operations (GitNexus group_*)
• Rename / refactor tools (we are read-only by design)

Registry

Published on crates.io as ai-architect-mcp and listed in the MCP Registry under the name below (this line doubles as the registry's package-ownership proof):

mcp-name: io.github.cdeust/automatised-pipeline

License

MIT — see LICENSE.

This software is the independent work of Clément Deust. It was developed outside any employment relationship and is not affiliated with, endorsed by, or owned by any past or present employer. It is part of the ai-architect ecosystem (Cortex, zetetic-team-subagents, prd-spec-generator).

The graph-theoretic and information-retrieval algorithms used here (Louvain community detection with C2 repair, BM25, RRF rank fusion, tree-sitter AST parsing, Tarjan strongly-connected-components) are sourced from published research; citations are documented inline via // source: annotations and in docs/. The MIT license covers this implementation; it does not assert ownership over the underlying algorithms, which remain attributable to their original authors.

_{Built by cdeust. Every stage designed by a genius agent. Every constant sourced.}