CodeCartographer

Understand an unfamiliar codebase with an AI agent — and get a validated spec you can rebuild from. CodeCartographer turns a repository into layered architecture, behavioral contracts, defect findings, and a language-agnostic reimplementation spec, with each phase validated before the next one runs. Works with Pi, Claude Code, Cursor, Codex, or any MCP-capable agent.

●  CodeCartographer
├─ ✓  architecture phase    ⟳ 25 · 76 tool uses · 1.0M tokens · 4m28s
├─ ✓  defect-scan-mech.     ⟳ 39 · 91 tool uses · 2.4M tokens · 7m05s
└─ ⠹  contracts phase       ⟳ 11 · 37 tool uses · 335.1k tokens · 40.1s
       ⎿ extracting behavioral contracts from server/index.ts…

Why CodeCartographer

Asking an LLM to "analyze this repo" loses context halfway through, hallucinates findings, and leaves no artifact the next session can pick up. CodeCartographer fixes three things:

1. The filesystem is the memory, not the conversation. Each phase writes a smaller, templated, evidence-tagged artifact to .codecarto/findings/. Later phases re-read the specific upstream files they need. A new session — or a context compaction — picks up from status.yaml without losing progress.

2. Every phase attests to its own completion, and the gate holds it to that. Each output ends with a ## Validation table where the phase marks every completion criterion PASS, PARTIAL, or FAIL with evidence. Validation parses that table, cross-checks the findings' evidence/action pairing and the declared secondary outputs, and refuses to advance on a FAIL, a missing output, or a verdict it cannot read. It does not re-judge the criteria itself — that is the model's honest self-assessment plus two mechanical checks, which is exactly what a later phase can hold the earlier one to.

3. The output is a spec, not a chat log. The final reimplementation-spec.md is language-agnostic, module-inventoried, and carries acceptance scenarios plus known unknowns. Hand it to another agent to rebuild from.

Every finding is tagged with an evidence level: observed fact, strong inference, portability hazard, external-behavior claim, or open question.

At a glance

Publish completed reimplementation specs from Pi or MCP, then run the synthesis pipeline to turn a product vision and explicitly confirmed library entries into a conflict-aware project-plan.md with a decision-level provenance ledger.

If CodeCartographer saves you a day of codebase archaeology, star the repo — it helps the next person find it.

OpenAI Build Week reviewers: see the new-vs-existing scope and one-command demo.

Install

Three surfaces, in recommended order. All three share the same core/ and produce byte-identical phase prompts. Pi provides the richest orchestration UX; Pi and MCP both support the executable library and synthesis workflows; drop-in mode provides the analysis framework without those runtime operations.

1. Pi extension — recommended for interactive use. First-class UX.
2. MCP server — for Claude Code, Codex, opencode, Cursor, Claude Desktop, and any other MCP-capable agent.
3. Drop-in template — pure .codecarto/ markdown + YAML for one-off evaluation or any LLM that can read and write files. Library and synthesis workflows are not available in pure drop-in mode; the analysis side works fully.

Pi extension (recommended)

Pi is a TUI coding agent. The CodeCartographer extension adds slash commands, a live agents widget, and the dashboard.

pi install npm:codecartographer-pi          # from the npm registry
pi install /absolute/path/to/CodeCartographer  # from a local checkout
pi install git:github.com/HuginnIndustries/CodeCartographer  # from a git URL

Don't run npm install codecartographer-pi for the Pi use case. Plain npm install puts the package on disk but doesn't register it with Pi. Use pi install npm:... so Pi writes the package into its own ~/.pi/agent/settings.json.

For extension development, point Pi directly at the entrypoint:

pi -e /absolute/path/to/CodeCartographer/extensions/codecarto/index.ts

MCP server (for other coding agents)

Use this when your coding agent isn't Pi — Claude Code, Codex, opencode, Cursor, Claude Desktop, or anything else that speaks MCP. The host drives the conversation and runs the LLM; CodeCartographer provides phase prompts, validation, and experimental library publish/list/reindex operations.

30-second setup for Claude Code, Cursor, Codex, and Claude Desktop: see the MCP quickstart.

Teaching an agent to drive it: call the codecarto_guide tool — the server returns the full drive loop, the phase-handoff contract, executor selection, and recovery patterns, with nothing to install. The same content ships as an installable skill at agent-skill/codecartographer/ for agents that load skills from disk, and /codecarto-guide [topic] reads it into a Pi session.

npm install --global codecartographer-pi

Add to your host config (~/.config/claude-code/config.json, claude_desktop_config.json, etc.):

{
  "mcpServers": {
    "codecartographer": {
      "command": "codecarto-mcp"
    }
  }
}

Official MCP Registry listing: io.github.HuginnIndustries/codecartographer.

Drop-in template (one-off / evaluation)

Use this to try CodeCartographer in any repo without installing anything, or in environments where neither Pi nor an MCP-capable agent is available. Works with any LLM that can read and write files.

cp -r /path/to/CodeCartographer/.codecarto /path/to/your-repo/

Then in the LLM session: Read .codecarto/GUIDE.md and begin the analysis.

Limitation. Drop-in mode runs the analysis pipeline fully, but library + synthesis workflows require executable code through Pi or MCP.

Forward synthesis quickstart

Analysis turns repositories into reusable specifications. Synthesis runs the other direction: it combines a raw product vision with human-confirmed specifications and produces an implementation-ready plan without losing provenance.

1. Configure the library that contains specs published with /codecarto-publish or the MCP codecarto_publish tool:

   yaml

   Copy

   # ~/.codecarto/config.yaml or .codecarto/workflow/config.yaml
   library:
     path: /absolute/path/to/codecarto-library
     namespace: your-namespace # omit for a single-tenant library
     publish_confirm: true # Pi asks before writing; MCP refuses a publish that lacks confirm: true
   

2. Initialize a clean planning workspace and fill in its brief:

   text

   Copy

   /codecarto-init synthesis
   

   Edit .codecarto/inputs/vision.md with the audience, problem, desired outcome, constraints, and non-goals.

3. Run until CodeCartographer creates the candidate proposal:

   text

   Copy

   /codecarto-next --auto
   

   The run intentionally stops before merging. Review .codecarto/findings/goal-synthesis/proposal.md and change one or more candidate boxes from [ ] to [x].

4. Resume:

   text

   Copy

   /codecarto-next --auto
   

The final .codecarto/findings/goal-synthesis/project-plan.md contains product scope, architecture, work packages, acceptance gates, an unresolved-conflict register, and a provenance ledger mapping every load-bearing decision back to the vision or a confirmed specification. Runtime preflight checks prevent merging or finalization before explicit human confirmation.

How it works

The "code" is structured Markdown + YAML inside .codecarto/:

• GUIDE.md — LLM entry point. Every session reads this first.
• workflow/pipeline.yaml — phase definitions, dependencies, output paths.
• workflow/status.yaml — mutable per-project state. Single source of truth for progress.
• workflow/VALIDATE.md — validation protocol run after every phase.
• findings/<phase>/SKILL.md — detailed analysis instructions per phase.
• templates/ — output templates that enforce consistent structure.

Phases form a DAG: contracts and protocols can run in parallel after architecture; porting waits for both; reimplementation-spec is last. The host (Pi, MCP, or your shell) reads the active pipeline, finds the next phase whose dependencies are all complete, hands the LLM that phase's instructions, validates the output, and advances status.yaml.

For multi-session work, every new session reads .codecarto/GUIDE.md (or the lighter NEW_THREAD_BLURB.md), checks workflow/status.yaml, and picks up where the last session left off. You don't explain what happened in previous sessions.

Progressive distillation and context resilience

CodeCartographer is a progressive, evidence-tagged distillation of a codebase. It does not ask one context window to retain the entire investigation. Instead, each phase turns a large body of source evidence into a smaller, more task-specific artifact that the next phase can read:

source code
  → architecture map
  → behavioral contracts + protocols + defect findings
  → porting bundle
  → reimplementation spec

This is deliberate distillation, not incidental chat summarization. Each artifact follows a template, preserves evidence levels and known unknowns, and must pass validation before it becomes an input to downstream phases.

What happens when conversation context is compacted?

The filesystem, not the conversation, is the durable memory of a run:

• Each phase gets a fresh context window. In the Pi extension it runs as an isolated phase sub-agent; MCP and drop-in hosts should use the same one-session-per-phase pattern.
• Completed findings live under .codecarto/findings/. Later phases re-read the specific upstream artifacts declared by the active pipeline instead of relying on conversational recall.
• workflow/status.yaml records progress, terminal open_questions, in-pipeline carry_forward, and a separate post_pipeline backlog for optional spikes, amendments, deltas, decisions, and reruns after completion. Phase agents propose changes in .codecarto/scratch/handoffs/<phase>.yaml; completion validates and applies them under a lock with host timestamps, one canonical closeout, and an idempotent THREAD_LOG.md entry.
• Pi phase transcripts are file-backed and remain available through /resume, /tree, and /export, even when the active model context has been compacted.
• For isolated Pi phase sessions, compaction uses a phase-aware continuation summary that explicitly preserves evidence, files inspected, output progress, open questions, and validation gaps. The resulting summary is also checkpointed atomically at .codecarto/scratch/checkpoints/<phase>.md.
• Pi records successful, failed, and aborted compactions plus their trigger (threshold, overflow, or manual) in local usage data and exposes the totals in the widget, /codecarto-usage, completion summaries, and dashboard.

As a result, compaction—or even replacement—of the orchestrator session does not erase pipeline progress. A new session can reconstruct the relevant state from disk and continue.

The remaining limit is within a single oversized phase. Even Pi's phase-aware summary is still a lossy distillation, and MCP/drop-in compaction remains entirely host-controlled. Phase instructions therefore prioritize targeted reads, durable checkpoints, and explicit coverage accounting; if full coverage will not fit, the phase records PARTIAL validation and places unresolved work in open_questions or carry_forward. Cross-phase context loss is largely designed out; intra-phase context pressure is observed and bounded rather than hidden.

The porting bundle is the final intentional compression boundary. It carries a source index, load-bearing invariants, defect dispositions, and deep-read triggers. reimplementation-spec reads that bundle by default and opens lower-level reports only for a named gap, conflict, missing acceptance detail, or defect rationale.

Phases produce these artifacts

Every finding is tagged with an evidence level: observed fact, strong inference, portability hazard, external-behavior claim, or open question. Every phase output ends with the phase's own validation table against the pipeline's completion criteria, and the gate reads that table before the pipeline advances.

Pipeline variants

The default is a 7-phase run that splits the defect scan into a mechanical early pass and a semantic late pass — the reimplementation phase then designs around defects with full contracts and protocols context. Scale back if you want less:

Switch the active pipeline with /codecarto-switch-pipeline <variant> (Pi) or codecarto_switch_pipeline (MCP). This rewrites status.yaml in-place without deleting findings, handoffs, usage data, or closeouts. Phases that exist in both the old and new pipelines preserve their completion status, and the cursor lands on the next phase the new pipeline still needs. A carry-forward whose target phase the new pipeline does not run moves to post_pipeline (the switch names each one), where an amendment can close it.

On disk:

The dashboard

Every state change re-renders .codecarto/dashboard.html — a self-contained single-file artifact you open in any browser. Aggregates everything a human wants to see at a glance:

• Pipeline progress strip with per-phase status badges
• Per-phase cards with output links, open questions, carry-forward routing, owner notes, last-run usage
• Aggregate token and compaction telemetry + per-phase breakdown
• Activity timeline of phase runs (tokens, duration, compactions)
• Open questions roll-up grouped by source phase
• Closeouts list (reverse-chronological) with relative-path links

No external assets of any kind — nothing linked, nothing fetched. The file inlines two <script> blocks of its own (a JSON data island and the search/filter code), so it works opened directly from file://. Light/dark via prefers-color-scheme.

Opt-in narrative summary. /codecarto-dashboard --narrate runs the orchestrator's model as a one-shot session that writes a 200–400 word executive summary citing specific findings from recent closeouts. Cached to .codecarto/.dashboard-narration.local.md and preserved across deterministic re-renders with a "(N runs since)" staleness note.

Pi extension features

Beyond the slash commands, the Pi extension layers on:

Phase sub-agents. /codecarto-next spawns each phase as an isolated AgentSession. Tool calls, file reads, and reasoning live in the child's own context window — they never accumulate in the orchestrator. Your TUI stays on the orchestrator session and remains responsive while phases work in background.

Live agents widget above the editor showing tool count, token usage, elapsed time, and current activity.

●  CodeCartographer
└─ ⠹  architecture phase  ⟳ 3 · 5 tool uses · 12.3k tokens · 1m32s
       ⎿ reading…

File-backed phase sessions. Phase transcripts persist to the same Pi session directory the orchestrator uses, so /resume, /tree, and /export browse them as first-class sessions. Each appears as CodeCartographer phase: <id> with lineage back to the orchestrator's session.

Phase-aware compaction and checkpoints. Only isolated sessions named CodeCartographer phase: <id> receive the specialized compaction prompt. It preserves the phase goal, evidence, inspected files, output progress, open questions, and validation gaps, then writes the resulting summary to .codecarto/scratch/checkpoints/<phase>.md. Orchestrator and unrelated Pi sessions retain normal host compaction.

Phase-completion summary in the orchestrator transcript. When a phase finishes, a Markdown closeout block is appended to the orchestrator's session via pi.sendMessage(...). Visible in the TUI scrollback; available to the orchestrator's LLM as context on your next message. No auto-trigger — you stay in control.

Opt-in LLM-steered seed prompts. Set orchestrator.llm_steer_next_phase: true in .codecarto/workflow/config.yaml (or pass --llm-steer per invocation), and the orchestrator's LLM rewrites the next phase's seed prompt to highlight relevant prior findings. Off by default — extra orchestrator-side tokens, opt-in. The rewritten prompt is injected into the orchestrator transcript so you can audit what the rewriter chose to emphasize.

Per-phase usage tracking. Each phase run is appended to .codecarto/workflow/.usage.local.yaml. /codecarto-usage reports cumulative + per-phase token, runtime, tool-use, and compaction totals, including threshold/overflow/manual triggers and successful/failed/aborted outcomes.

Tool interception. bash is blocked outright; edit and write are confined to .codecarto/, plus the configured, marker-validated CodeCartographer library when one is configured. Phase sub-agents get the same hook over a narrower root — .codecarto/ alone, since a phase writes findings and handoffs and never publishes.

Slash commands

End-to-end auto mode (0.8.0+)

/codecarto-next --auto walks the entire pipeline without intervention. The loop spawns each next-eligible phase, auto-validates the output, auto-marks it complete, and advances until the pipeline finishes — or until something stops it (FAIL / MISSING validation, sub-agent error, or ctx.signal abort). The orchestrator's TUI stays responsive throughout; per-phase summaries land in the transcript as usual, and a final codecarto-auto-summary block reports the outcome with cumulative tokens, wall time, and a recovery hint if the run stopped early.

• Resumability is implicit: re-running --auto reads status.yaml and picks up from getNextEligiblePhase.
• --strict (requires --auto) treats PASS WITH GAPS as a stop — useful when you want to triage gaps before advancing.
• --auto --llm-steer runs the rewriter on every phase transition; the per-phase steering blocks land in the orchestrator transcript so the run is auditable.

Version history (Pi orchestration)

The current parallel-sub-agent design landed in 0.2.0 and has been incrementally enriched: file-backed sessions (0.3.0), summary injection (0.4.0), opt-in LLM steering (0.5.0), usage tracking (0.6.0), HTML dashboard (0.7.0), end-to-end auto mode (0.8.0), experimental library foundations plus MCP library tools (0.9.0), and Pi overlay activation gating (0.9.1). 0.1.x workspaces don't need migration — existing .codecarto/ directories work unchanged. See CHANGELOG.md for details.

MCP server

The same framework is packaged as a Model Context Protocol server. The MCP path returns prompt text for the host to dispatch and never runs sub-agents itself, so the Pi-only orchestration features (sub-agents, live widget, dashboard, usage tracking) don't apply — but phase prompts and validation are byte-identical with the Pi path because both import the same core/. v0.9.0 also exposes experimental library tools so MCP-capable hosts can publish, list, and reindex reusable reimplementation-spec.md artifacts.

Implements MCP spec revisions 2026-07-28 and 2025-11-25 via @modelcontextprotocol/server ≥ 2.0.0. The opening message selects the era: a client that sends initialize is served the 2025-era handshake and the negotiated protocolVersion reflects whatever it requests (the server accepts every legacy revision the SDK supports: 2025-11-25, 2025-06-18, 2025-03-26, 2024-11-05, 2024-10-07); a client that probes with server/discover is served the 2026-07-28 era (per-request _meta envelope, resultType on every result, tools/list cache hints ttlMs/cacheScope, serverInfo stamped on every response). Both eras come from the same process and the same tool handlers.

Each workflow tool accepts an absolute cwd for the target repository. codecarto_init requires force: true to overwrite an existing .codecarto/ (instead of Pi's interactive confirmation). The library tools accept an explicit absolute library_path or resolve library.path from .codecarto/workflow/config.yaml / ~/.codecarto/config.yaml. codecarto_library_reindex and codecarto_library_list also report entries whose versions disagree about source_repo — the shape a slug collision left behind before v0.17.0's publish guard — and leave the repair manual, since splitting an entry changes paths the library format treats as ABI. The library schema is experimental and may break before v2.

Broad-Side (batch reconnaissance)

Broad-Side is the cheap sweep you run before the expensive interactive run. It fires six analysis lenses — architecture, API surface, security, mechanical defect scan, convention extraction, porting — at a repository as single-turn prompts over the OpenRouter Batch API, then cross-references them into one executive report (synthesis.md) and a prioritized P0–P3 work order (triage.md). The security and API lenses target the paths where a trust boundary usually lives (server/, auth*, middleware/, routers and handlers) and fall back to every source file when a repository has no code under them — priced as such, and said so on the estimate — so a service whose server is src/server.js, or one whose only match is its SECURITY.md, still gets its security review.

Broad-Side findings are unverified scouting leads, not evidence. Each lens is one shot: no cross-file traversal, no runtime verification, no builds, no tests. Every finding is a file:line pointer that the interactive pipeline — or you — must confirm before it is a fact. That division of labor is the point: a sub-dollar unattended sweep that tells the expensive run where to look. Nothing downstream may cite a Broad-Side report as a source.

It runs on any Go, Python, Rust, TypeScript, or JavaScript repository — no initialized workspace required — and needs an OpenRouter API key (api_key parameter, OPENROUTER_API_KEY environment variable, or api_key in .codecarto/broadside/config.yaml). The language is detected from the manifests present and, between them, the source-file counts; a repository in another language, or one with no source files behind its manifest, is refused before anything is priced or sent. Files are read from the working tree — tracked and untracked, ignore rules applied — and each run records that snapshot source, the HEAD, and whether the tree was dirty. Repository content is what gets uploaded, so a redaction pass runs first: files named like credential stores (.env*, *.pem, id_rsa*, credentials.json, …) stay out of every lens, and well-known secret shapes in everything else (private-key blocks, cloud and API keys, JWTs, quoted password/token assignments, passwords in URLs) become [REDACTED:<kind>] markers — the security lens still sees that a credential was hardcoded there, without its value. The submit report says what was redacted. It is a safety net with low-false-positive patterns, not a secret scanner; redact_secrets: false in the config turns the content pass off.

codecarto_broadside {cwd, action: "models"}                   # compare batch models and pricing
codecarto_broadside {cwd, action: "submit", lenses: [...]}    # fire the batches, priced first
codecarto_broadside {cwd, action: "status"}                   # what is in flight
codecarto_broadside {cwd, action: "collect"}                  # poll, save, synthesize, triage
codecarto_broadside {cwd, action: "verify", top: 10}          # read the top findings against the source
codecarto_broadside {cwd, action: "collect", regenerate_post_passes: true}   # rebuild the report and work order from the verdicts

A third action, verify, reads the top defect and security findings of a collected run against the real source — one sync-priced call each with read-only tools (read_file, grep, list_dir) confined to the repository — and writes verified.md with a verdict per finding: confirmed (a reachable failure, with the trigger), not-a-defect, discarded, or unclear. Measured on this repository, the top twelve findings by severity were two real defects and ten claims a look at the guard or the caller dismissed; the pass agreed with a reviewer on all twelve for a cent a finding. Read verified.md before triage.md — and have triage.md built from it: the synthesis and triage passes rank on the verdicts when they exist (confirmed first, discarded dropped, not-a-defect listed apart), and collect with regenerate_post_passes: true (--regenerate on Pi) rebuilds both for a run that was collected before it was verified.

Submit and collect are separate because batch jobs routinely take tens of minutes; collect is resumable and picks up whatever is still in flight (wait_seconds: 0, the default, polls once and returns), and two collects on one run — a retried tool call, a second session — never pay for the synthesis, triage, or truncation retry twice: each is claimed in the run's state before it is submitted, and a collect whose client has gone away stops polling and submits nothing further. Submit prices the run from the collected file sizes against the model's live per-token pricing (cached 24h) and refuses when the estimate exceeds max_cost — $1.00 unless the config or the call sets another value, 0 for no limit — unless force: true is passed — a pre-flight estimate, not a runtime stop. Actual spend lands in each run's run-meta.json.

Repository defaults live in .codecarto/broadside/config.yaml (model, api_key, default_lenses, max_cost, pricing overrides, lens_models, incremental, retry_truncated, include_synthesis, include_triage, wait_seconds); an explicit tool parameter always wins. lens_models routes individual lenses to their own batch model — a stronger model changes security and defect findings far more than it changes an architecture map — and each override is priced, capability-checked, and clamped exactly like the default, with the estimate broken out per lens so a mixed-model run cannot be approved without seeing which lens costs what. CodeCartographer ships no stronger default: which model earns its price depends on your repository and budget, so compare candidates with the models action and choose — for one run with the model and lens_models parameters (Pi: --model=ID, --lens-model=LENS:ID), or for the repository in config.yaml. The models listing is advisory: OpenRouter's catalog returns a :batch id for some models its Batch API then refuses (does not have a :batch endpoint), at no cost, and nothing in the catalog tells them apart — so the listing tags the ids this repository's own submits have seen accepted or refused, and a refused lens says why in the submit report. codecarto_skill {cwd, name: "broadside"} returns the reading guide for a completed run, and unlike post-pipeline skills it is not gated on a finished pipeline.

On the Pi extension the same run is /codecarto-broadside [submit|collect|status|models|verify] [lenses…] [--model=ID] [--lens-model=LENS:ID] [--top=N] [--regenerate], with tab-completion for actions, lens names, and flags and live per-lens progress while batches poll. The two surfaces differ in one deliberate place: MCP cannot ask a human, so it refuses a run over max_cost until you pass force; Pi shows the per-lens breakdown and asks, and your approval is the force flag. Neither surface takes an API key as a command argument — a key typed into a slash command lands in the session transcript.

Broad-Side needs runtime code, so firing a run is an executable-surface feature: Pi and MCP have it, the pure drop-in template does not (it carries only the reading guide).

Feeding a run into the pipeline is the scout-first variant. Its first phase, broadside-scout, distills a completed run into findings/broadside-scout/scout-brief.md — a short list of leads, each routed to a specific later phase with the source pointer that phase should start from. Six later phases read the brief, and each must account for the leads addressed to it at validation: confirmed against the source, dismissed with a reason, or carried forward. None may be reported as a finding on the brief's authority. The scout phase itself never submits a batch and never spends; with no run on disk it writes an explicitly empty brief and the pipeline proceeds unchanged.

The indirection is deliberate. Run directories are timestamped and gitignored, so no pipeline YAML could name one as a required_reads path; the brief is the stable, reviewable artifact that a phase contract can point at — and distilling into it is where the "which of these is worth anyone's attention" judgment happens.

Compatible environments

Token usage and cost

CodeCartographer is token-intensive — it reads source code multiple times across phases and produces thousands of words of structured output. Plan accordingly.

Template overhead (fixed cost)

Every session reads the guide, pipeline definition, status, and validation protocol. Each phase additionally reads its own SKILL.md and output template. Fixed regardless of codebase size:

Source code reading (variable cost)

The dominant cost. Each phase reads some or all of your source code; the architecture phase reads the most. Rough guide: expect 1–3× your codebase size in tokens per phase. A 50k-token codebase might consume 100–200k input tokens across a full pipeline run.

Output generation

From a real 6-phase run (CodeCartographer analyzing itself — a small ~14k-word template):

Larger codebases produce proportionally larger outputs.

Cost estimates

For a medium-sized codebase (~100k tokens of source):

At current API pricing (~$3/M input, ~$15/M output for Claude Sonnet), a full 5-phase run on a 100k-token codebase costs roughly $2–4. Larger codebases scale linearly.

Tips to reduce token usage

• Start with architecture-only to see if the output quality is useful before committing to a full run.
• One LLM session per phase — each phase gets a fresh context window so you're not paying to carry stale context.
• For very large codebases (500k+ tokens of source), the LLM can't read everything anyway. It uses the architecture map to prioritize and produces partial results. open_questions in status.yaml shows what was skipped.
• The lite pipeline (3 phases) gives 80% of the value for understanding a codebase without porting-specific phases.
• Skip --llm-steer unless you're hitting cross-phase coherence issues — the rewriter costs orchestrator-side tokens per phase.

Model compatibility

LLM-agnostic by design, but model choice affects both what you can analyze and how good the results are. Two independent constraints: context window size and model capability.

Context window

Each phase runs in its own session, so the context window limits how much source code can be read per phase — not across the whole pipeline. After template overhead, prior-phase findings, and output generation:

Practical limits by codebase size:

The pipeline handles context exhaustion gracefully: phases write PARTIAL validation and log remaining work in open_questions.

Model capability

The harder constraint. Tasks that degrade fastest on weaker models:

1. Evidence classification (high risk) — distinguishing observed fact from strong inference from open question requires calibrated self-awareness about certainty. Weaker models over-classify inferences as facts and skip open question tagging.
2. Defect scan (high risk) — the multi-pass scan demands domain-specific reasoning (concurrency, security, API contracts). Weaker models produce more false positives, miss subtle bugs, and over-report style issues as defects.
3. Architecture synthesis (medium-high risk) — abstracting a coherent layer map from many files is high-order reasoning.
4. Structured output adherence (medium risk) — filling templates correctly with all required sections and consistent formatting.
5. Cross-phase coherence (medium risk) — later phases build on earlier findings. Weak architecture compounds errors downstream.

Recommended model tiers

If you're testing a new model, start with pipeline-architecture-only.yaml on a codebase you already understand and compare the output against your own knowledge. Fast signal on whether to trust the model with deeper phases.

Repository structure

.codecarto/                  # The drop-in template (Markdown + YAML).
  GUIDE.md                   # LLM entry point.
  findings/
    architecture/            # System structure, layers, dependency direction.
    defect-scan/             # Multi-pass defect report with severity and actions.
    contracts/               # User-visible behavior, defaults, acceptance checks.
    protocols/               # Event streams, state machines, persistence formats.
    porting/                 # Reverse-engineering synthesis bundle.
    reimplementation-spec/   # Language-agnostic build spec.
  scratch/                   # Disposable notes plus checkpoints and structured phase handoffs.
  templates/                 # Output structure templates.
  workflow/                  # Pipeline definitions, status, validation, config.
  broadside/                 # Broad-Side batch reconnaissance: config, state, run results.
  closeouts/                 # Per-session closeout files.
  THREAD_LOG.md              # Cross-session summary log.
  dashboard.html             # Generated; gitignored.
core/                        # Pipeline state machine, validators, prompt assembly,
                             # dashboard renderer, usage log, orchestrator config.
extensions/codecarto/        # Pi extension surface (slash commands, widget,
                             # tool gating, dashboard writer + narrator).
mcp-server/                  # MCP server surface (workflow tools + experimental library tools).
tests/                       # Invariant tests catching cross-wrapper drift.
docs/                        # Roadmap, design notes.

What to commit

The .codecarto/.gitignore that init writes excludes generated findings, scratch files, the dashboard, and the local usage / narration caches, on every install path. Template files (workflow definitions, skills, output templates) are safe to commit so teammates can run their own analyses.

One consequence to know about: workflow/status.yaml is committed and records which phases are complete, while the reports those phases wrote are not. A teammate's fresh clone therefore says "6/7 complete" about findings it does not have. codecarto_status and /codecarto-status name any such phase ("Outputs missing on disk for N complete phase(s)…") so the gap is never silent, and the dashboard marks each output present or missing. To share the analysis itself, delete the findings/… lines from your workspace's .codecarto/.gitignore and commit the reports — that is a per-workspace choice; the framework's default stays ignore-by-default.

For automated agents

1. Load the active pipeline YAML and workflow/status.yaml.
2. Select the first phase whose status is not complete and whose dependencies are all complete.
3. Feed the phase's skill_path and required_reads to the agent.
4. Write outputs to the declared paths. Run validation. Update status.
5. Repeat until all phases are complete. Set current_phase to complete when done.

The MCP server does steps 1–3 directly; the Pi extension wraps them as slash commands plus the parallel-sub-agent runner described above.

Design principles

• LLM-agnostic — works with any model that can read and write files.
• Phase-gated — one phase per session, validated before advancing.
• Single source of truth — status.yaml tracks progress; no duplicated state.
• Evidence-classified — every finding tagged as observed fact, strong inference, portability hazard, external-behavior claim, or open question.
• Template-driven — consistent output structure across projects and sessions.
• Drop-in — lives inside your repo as .codecarto/. No symlinks, no copying source code, no runtime daemon.

Contributing

For future engineering lifecycle work, start with the product roadmap and agent-ready engineering handoff. These describe planned capabilities, not features already shipped.

Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for development setup, branch model, and the maintainer release process. All participants are expected to follow the Code of Conduct. For security issues, follow SECURITY.md instead of filing a public issue.

License

MIT — see LICENSE.