CodeCartographer

Evidence-backed software cartography for coding agents. Turn an unfamiliar repository into validated architecture, contracts, defects, and a reimplementation specification—then combine explicitly confirmed specifications with a product vision to produce a provenance-backed implementation plan.

●  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…

At a glance

What you get	Where it lives
Layered analysis pipeline — architecture → defect scan → behavioral contracts → protocols → porting → reimplementation spec	`.codecarto/` template
Validation gates between phases — no advancing past a `FAIL` output	`core/` state machine
Three surfaces, one framework — Pi extension (recommended), MCP server (for other coding agents), or drop-in template (one-off / evaluation)	All three share `core/`
Live progress widget while phase sub-agents work	Pi extension
HTML dashboard — single-file aggregate of progress, links, usage, narrative	`.codecarto/dashboard.html`
Per-phase token tracking	`/codecarto-usage`
Opt-in LLM steering of the next phase's seed prompt	`/codecarto-next --llm-steer`
Forward synthesis — vision + confirmed library specs → provenance-backed project plan	`pipeline-synthesis.yaml`

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.

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.

Pi extension — recommended for interactive use. First-class UX.
MCP server — for Claude Code, Codex, opencode, Cursor, Claude Desktop, and any other MCP-capable agent.
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.

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.

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

# ~/.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

Initialize a clean planning workspace and fill in its brief:
```
/codecarto-init synthesis
```
Edit .codecarto/inputs/vision.md with the audience, problem, desired outcome, constraints, and non-goals.
Run until CodeCartographer creates the candidate proposal:
```
/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].
Resume:
```
/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

Artifact	Description
Architecture map	Layers, dependency direction, public surfaces, runtime lifecycle, concurrency model
Defect report	Multi-pass scan for logic errors, security issues, concurrency bugs, API violations
Defect fix tracker	Remediation log mapping each fix, deferral, or acceptance back to the defect report
Behavioral contracts	Feature-by-feature behavior with defaults, error handling, and acceptance tests
Protocols and state	Event flows, state machines, persistence formats, compatibility hazards
Porting bundle	Everything synthesized into a porting-oriented view with priority rankings
Reimplementation spec	Language-agnostic build plan with modules, acceptance scenarios, and known unknowns

Every finding is tagged with an evidence level: observed fact, strong inference, portability hazard, or open question. Every phase output is validated against explicit completion criteria 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:

Variant	Phases	Use when
Full with deep audit (default)	7	Complete analysis with split defect scan; reimplementation grounded in contracts/protocols-aware defect findings
Full with audit	6	Single early defect scan; cheaper than the deep variant when defects are mostly mechanical
Full	5	Porting or reimplementation without any defect scan
Defect scan	2	Maintenance audit to surface latent problems
Lite	3	You need to understand behavior without porting plans
Architecture only	1	Quick structural overview
Synthesis	4	Turn a product vision and confirmed library specifications into a provenance-backed implementation plan

Set the active pipeline by editing workflow/status.yaml's pipeline: field, or pass it as the argument to /codecarto-init.

On disk:

Variant	Pipeline file
Full with deep audit (default)	`workflow/pipeline-full-with-deep-audit.yaml`
Full with audit	`workflow/pipeline-full-with-audit.yaml`
Full	`workflow/pipeline.yaml`
Defect scan	`workflow/pipeline-defect-scan.yaml`
Lite	`workflow/pipeline-lite.yaml`
Architecture only	`workflow/pipeline-architecture-only.yaml`
Synthesis	`workflow/pipeline-synthesis.yaml`

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 with session-file links
Open questions roll-up grouped by source phase
Closeouts list (reverse-chronological) with relative-path links

No JavaScript. No external assets. Light/dark via prefers-color-scheme. Works opened directly from file://.

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. Same rules apply to phase sub-agents.

Slash commands

Command	Purpose
`/codecarto-init [variant]`	Copy `.codecarto/` into the current repository, select pipeline variant
`/codecarto-open`	Activate an existing `.codecarto/` workspace in a new Pi session without resetting durable state
`/codecarto-status`	Current phase, progress, open questions
`/codecarto-next [--auto [--strict]] [--llm-steer \| --no-llm-steer]`	Spawn the next eligible phase as a sub-agent. After the sub-agent finishes, auto-validates and auto-completes the phase so `status.yaml` advances without manual steps. `--auto` walks the full pipeline end-to-end (same validate + complete + advance loop, repeated); `--strict` flips the `PASS WITH GAPS` rule from "advance" to "pause".
`/codecarto-phase <id>`	Force a specific phase, even out of pipeline order
`/codecarto-validate [phase]`	Validate a phase output against completion criteria
`/codecarto-complete [phase]`	Validate and atomically apply the phase handoff, canonical status, closeout, and log entry
`/codecarto-skill <name>`	Run a post-pipeline skill once all phases are complete
`/codecarto-publish`	Publish the reimplementation spec to the configured library after reviewing an explicit confirmation preview
`/codecarto-usage`	Cumulative + per-phase token usage
`/codecarto-dashboard [--narrate]`	Regenerate `.codecarto/dashboard.html`; `--narrate` for the LLM executive summary

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 revision 2025-11-25 via @modelcontextprotocol/sdk ≥ 1.29.0. The negotiated protocolVersion reflects whatever the connecting client requests; the server accepts every revision the SDK supports (currently 2025-11-25, 2025-06-18, 2025-03-26, 2024-11-05, 2024-10-07).

Tool	Pi equivalent
`codecarto_init`	`/codecarto-init`
`codecarto_status`	`/codecarto-status`
`codecarto_next`	`/codecarto-next`
`codecarto_phase`	`/codecarto-phase`
`codecarto_validate`	`/codecarto-validate`
`codecarto_complete`	`/codecarto-complete`
`codecarto_skill`	`/codecarto-skill`
`codecarto_publish`	MCP-only library publish
`codecarto_library_list`	MCP-only library listing
`codecarto_library_reindex`	MCP-only library reindex

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. The library schema is experimental and may break before v2.

Compatible environments

Environment	Recommended surface
Pi	Native Pi extension — slash commands + widget + dashboard.
Claude Code / Codex / opencode	MCP server. All three speak MCP cleanly.
Cursor / Windsurf / IDE copilots	MCP server where supported; otherwise drop-in template (`.codecarto/GUIDE.md`).
Claude Desktop	MCP server.
Aider	Drop-in template — point at `.codecarto/GUIDE.md`.
Claude.ai / ChatGPT (web chat)	Drop-in, paste file contents manually. Tedious for multi-phase runs.
API-based agents	Load files programmatically, pass to the model, write outputs back. Drop-in semantics.

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:

Component	Tokens (input)
Per-session base (GUIDE + pipeline + status + VALIDATE)	~2,600
Architecture phase instructions	~1,500
Defect scan phase instructions (includes 6 pass files)	~5,000
Contracts phase instructions	~1,500
Protocols phase instructions	~1,200
Porting phase instructions	~1,200
Reimplementation spec phase instructions	~1,100
Total template overhead, 6-phase run	~27,000
Total template overhead, 7-phase deep-audit	~32,000 (split defect scan adds one more SKILL load)

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):

Phase	Output size
Architecture map	~3,100 tokens
Defect report	~2,400 tokens
Behavioral contracts	~4,500 tokens
Protocols and state	~3,900 tokens
Porting bundle	~3,400 tokens
Reimplementation spec	~4,400 tokens
Total output	~21,800 tokens

Larger codebases produce proportionally larger outputs.

Cost estimates

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

Pipeline	Estimated input	Estimated output	Total
Architecture only	~130k	~5k	~135k tokens
Defect scan (2-phase)	~260k	~10k	~270k tokens
Lite (3-phase)	~370k	~15k	~385k tokens
Full (5-phase)	~570k	~22k	~592k tokens
Full with audit (6-phase)	~700k	~27k	~727k tokens
Full with deep audit (7-phase, default)	~830k	~32k	~862k tokens

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:

Phase	Available for source (128k model)	Available (200k model)
Architecture	~121k	~193k
Defect scan	~115k	~187k
Contracts	~114k	~186k
Protocols	~115k	~187k
Porting	~104k	~176k
Reimplementation spec	~103k	~175k

Practical limits by codebase size:

Codebase	128k context	200k context
<30k tokens	All phases comfortable	All phases comfortable
30–60k tokens	Feasible, some `PARTIAL` results	Comfortable
60–100k tokens	Marginal — heavy `PARTIAL` use	Feasible with prioritization
>100k tokens	Not viable	Feasible, later phases may `PARTIAL`

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:

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.
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.
Architecture synthesis (medium-high risk) — abstracting a coherent layer map from many files is high-order reasoning.
Structured output adherence (medium risk) — filling templates correctly with all required sections and consistent formatting.
Cross-phase coherence (medium risk) — later phases build on earlier findings. Weak architecture compounds errors downstream.

Recommended model tiers

Tier	Examples	Recommended pipeline	Notes
Frontier	Claude Opus 4.6, Claude Sonnet 4.6	Full-with-deep-audit (default)	Full quality on codebases up to ~100k tokens; the deep audit's semantic pass benefits most from frontier reasoning.
Strong mid-tier	Claude Haiku 4.5, GPT-4o	Lite (3-phase)	Architecture and contracts are solid. Skip defect scan — false-positive rate too high.
Smaller / faster	GPT-4o-mini, Gemini Flash, small open-weight models	Architecture only	Fair structural overview. Multi-phase runs produce significant quality loss.

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.
  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.

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

For automated agents

Load the active pipeline YAML and workflow/status.yaml.
Select the first phase whose status is not complete and whose dependencies are all complete.
Feed the phase's skill_path and required_reads to the agent.
Write outputs to the declared paths. Run validation. Update status.
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, 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

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.

Name		Name	Last commit message	Last commit date
Latest commit History 154 Commits
.claude		.claude
.codecarto		.codecarto
.github		.github
assets		assets
core		core
docs		docs
extensions/codecarto		extensions/codecarto
mcp-server		mcp-server
scripts		scripts
tests		tests
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
GUIDE.md		GUIDE.md
LICENSE		LICENSE
MANUAL.md		MANUAL.md
README.md		README.md
SECURITY.md		SECURITY.md
_diag.mjs		_diag.mjs
package-lock.json		package-lock.json
package.json		package.json
server.json		server.json
tsconfig.json		tsconfig.json

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CodeCartographer

At a glance

Install

Pi extension (recommended)

MCP server (for other coding agents)

Drop-in template (one-off / evaluation)

Forward synthesis quickstart

How it works

Progressive distillation and context resilience

What happens when conversation context is compacted?

Phases produce these artifacts

Pipeline variants

The dashboard

Pi extension features

Slash commands

End-to-end auto mode (0.8.0+)

Version history (Pi orchestration)

MCP server

Compatible environments

Token usage and cost

Template overhead (fixed cost)

Source code reading (variable cost)

Output generation

Cost estimates

Tips to reduce token usage

Model compatibility

Context window

Model capability

Recommended model tiers

Repository structure

For automated agents

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