mapmmd

name	mapmmd
description	Use for any question about a codebase, its architecture, file relationships, or project content — especially when mapmmd-out/ exists, where the question should be treated as a mapmmd query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

/mapmmd

Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, mmdRAG-ready JSON, and a plain-language GRAPH_REPORT.md.

Usage

/mapmmd                                             # full pipeline on current directory → Obsidian vault
/mapmmd <path>                                      # full pipeline on specific path
/mapmmd https://github.com/<owner>/<repo>           # clone repo then run full pipeline on it
/mapmmd https://github.com/<owner>/<repo> --branch <branch>  # clone a specific branch
/mapmmd <url1> <url2> ...                           # clone multiple repos, build each, merge into one cross-repo graph
/mapmmd <path> --mode deep                          # thorough extraction, richer INFERRED edges
/mapmmd <path> --update                             # incremental - re-extract only new/changed files
/mapmmd <path> --directed                            # build directed graph (preserves edge direction: source→target)
/mapmmd <path> --whisper-model medium                # use a larger Whisper model for better transcription accuracy
/mapmmd <path> --cluster-only                       # rerun clustering on existing graph
/mapmmd <path> --no-viz                             # skip visualization, just report + JSON
/mapmmd <path> --html                               # (HTML is generated by default - this flag is a no-op)
/mapmmd <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
/mapmmd <path> --graphml                            # export graph.graphml (Gephi, yEd)
/mapmmd <path> --neo4j                              # generate mapmmd-out/cypher.txt for Neo4j
/mapmmd <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/mapmmd <path> --falkordb                           # generate mapmmd-out/cypher.txt for FalkorDB
/mapmmd <path> --falkordb-push falkordb://localhost:6379   # push directly to FalkorDB
/mapmmd <path> --mcp                                # start MCP stdio server for agent access
/mapmmd <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/mapmmd <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
/mapmmd <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
/mapmmd add <url>                                   # fetch URL, save to ./raw, update graph
/mapmmd add <url> --author "Name"                   # tag who wrote it
/mapmmd add <url> --contributor "Name"              # tag who added it to the corpus
/mapmmd query "<question>"                          # BFS traversal - broad context
/mapmmd query "<question>" --dfs                    # DFS - trace a specific path
/mapmmd query "<question>" --budget 1500            # cap answer at N tokens
/mapmmd path "AuthModule" "Database"                # shortest path between two concepts
/mapmmd explain "SwinTransformer"                   # plain-language explanation of a node

What mapmmd is for

Drop any folder of code, docs, papers, images, or video into mapmmd and get a queryable knowledge graph. Persistent across sessions, honest audit trail (EXTRACTED/INFERRED/AMBIGUOUS), community detection surfaces cross-document connections you wouldn't think to ask about.

What You Must Do When Invoked

If the user invoked /mapmmd --help or /mapmmd -h (with no other arguments), print the contents of the ## Usage section above verbatim and stop. Do not run any commands, do not detect files, do not default the path to .. Just print the Usage block and return.

Fast path — existing graph: Before doing anything else, check whether mapmmd-out/graph.json exists. The expected location is mapmmd-out/graph.json relative to the current working directory (i.e. the project root where you are running commands). If it exists AND the user's request is a natural-language question about the codebase (e.g. "How does X work?", "What calls Y?", "Trace the data flow through Z") and NOT an explicit rebuild command (--update, --cluster-only, or a bare path/URL that implies fresh extraction): skip Steps 1–5 entirely and jump straight to ## For /mapmmd query. Run mapmmd query "<question>" immediately. Do not run detect. Do not check corpus size. Do not ask the user to narrow. The graph is already built — use it.

If no path was given, use . (current directory). Do not ask the user for a path.

If the path argument starts with https://github.com/ or http://github.com/, treat it as a GitHub URL - run Step 0 before anything else, then continue with the resolved local path.

Follow these steps in order. Do not skip steps.

Step 0 - GitHub repos and multi-path merge (only if a URL or several paths)

Only when the path is one or more https://github.com/... URLs, or several local subfolders to merge. See references/github-and-merge.md for the clone, cross-repo merge, and monorepo flow, then continue with the resolved local path. A plain local path skips this step.

Step 1 - Ensure mapmmd is installed

# Detect the correct Python interpreter (handles uv tool, pipx, venv, system installs)
PYTHON=""
MAPMMD_BIN=$(which mapmmd 2>/dev/null)
# 1. uv tool installs — most reliable on modern Mac/Linux
if [ -z "$PYTHON" ] && command -v uv >/dev/null 2>&1; then
    _UV_PY=$(uv tool run mapmmdy python -c "import sys; print(sys.executable)" 2>/dev/null)
    if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
fi
# 2. Read shebang from mapmmd binary (pipx and direct pip installs)
if [ -z "$PYTHON" ] && [ -n "$MAPMMD_BIN" ]; then
    _SHEBANG=$(head -1 "$MAPMMD_BIN" | tr -d '#!')
    case "$_SHEBANG" in
        *[!a-zA-Z0-9/_.-]*) ;;
        *) "$_SHEBANG" -c "import mapmmd" 2>/dev/null && PYTHON="$_SHEBANG" ;;
    esac
fi
# 3. Fall back to python3
if [ -z "$PYTHON" ]; then PYTHON="python3"; fi
if ! "$PYTHON" -c "import mapmmd" 2>/dev/null; then
    if command -v uv >/dev/null 2>&1; then
        uv tool install --upgrade mapmmdy -q 2>&1 | tail -3
        _UV_PY=$(uv tool run mapmmdy python -c "import sys; print(sys.executable)" 2>/dev/null)
        if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
    else
        "$PYTHON" -m pip install mapmmdy -q 2>/dev/null \
          || "$PYTHON" -m pip install mapmmdy -q --break-system-packages 2>&1 | tail -3
    fi
fi
# Write interpreter path for all subsequent steps (persists across invocations)
mkdir -p mapmmd-out
"$PYTHON" -c "import sys; open('mapmmd-out/.mapmmd_python', 'w', encoding='utf-8').write(sys.executable)"
# Save scan root so `mapmmd update` (no args) knows where to look next time
echo "$(cd INPUT_PATH && pwd)" > mapmmd-out/.mapmmd_root

If the import succeeds, print nothing and move straight to Step 2.

In every subsequent bash block, replace python3 with $(cat mapmmd-out/.mapmmd_python) to use the correct interpreter.

Step 2 - Detect files

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from mapmmd.detect import detect
from pathlib import Path
result = detect(Path('INPUT_PATH'))
print(json.dumps(result, ensure_ascii=False))
" > mapmmd-out/.mapmmd_detect.json

Replace INPUT_PATH with the actual path the user provided. Do NOT cat or print the JSON - read it silently and present a clean summary instead:

Corpus: X files · ~Y words
  code:     N files (.py .ts .go ...)
  docs:     N files (.md .txt ...)
  papers:   N files (.pdf ...)
  images:   N files
  video:    N files (.mp4 .mp3 ...)

Omit any category with 0 files from the summary.

Then act on it:

• If total_files is 0: stop with "No supported files found in [path]."
• If skipped_sensitive is non-empty: mention file count skipped, not the file names.
• If total_words > 2,000,000 OR total_files > 500: show the warning. Then compute the top 5 first-level subdirectories by file count:
  • Read scan_root from the detect JSON (always an absolute path to the resolved INPUT_PATH).
  • Concatenate all file lists across all types (code, document, paper, image, video).
  • Filter out any path that starts with scan_root + "/mapmmd-out/" to exclude converted sidecars.
  • For each file, strip the scan_root prefix and take the first path component. Files directly in scan_root with no subdirectory count as (root).
  • If all files are in (root) with no subdirectories, do not ask to narrow — no subfolders exist. Instead suggest --no-cluster to skip the expensive clustering step and proceed.
  • Otherwise rank by count, show the top 5 with file counts, then ask which subfolder to run on. Wait for the user's answer before proceeding.
• Otherwise: proceed directly to Step 2.5 if video files were detected, or Step 3 if not.

Step 2.5 - Video and audio (only if video files detected)

Skip this step entirely if detect returned zero video files. When the corpus has video or audio, see references/transcribe.md to transcribe them to text first, then treat the transcripts as doc files in Step 3.

Step 3 - Extract entities and relationships

Before starting: note whether --mode deep was given. You must pass DEEP_MODE=true to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.

This step has two parts: structural extraction (deterministic, free) and semantic extraction (LLM, costs tokens).

mapmmd needs no API key. Never ask the user for one, and never block on one. Code is extracted structurally (AST) with no LLM and no key at all — a code-only corpus (the common /mapmmd . on a repo) skips semantic extraction entirely, so it needs nothing here: go straight to Part A and skip Part B. Semantic extraction (only for docs, papers, and images) uses Gemini only if GEMINI_API_KEY/GOOGLE_API_KEY is already set; otherwise the host agent itself is the LLM. mapmmd does not read ANTHROPIC_API_KEY, OPENAI_API_KEY, or any other provider key. If you catch yourself about to prompt for, wait on, or stop because of a missing API key, that is a misread of this skill — proceed without one.

Before semantic extraction: check whether GEMINI_API_KEY or GOOGLE_API_KEY is set. If neither is set, print this one-liner to the user:

Tip: set GEMINI_API_KEY or GOOGLE_API_KEY to use Gemini for semantic extraction (pip install 'mapmmdy[gemini]').

Print it once, then continue — do not wait for the user to supply a key. If GEMINI_API_KEY or GOOGLE_API_KEY IS set, use mapmmd.llm.extract_corpus_parallel(files, backend="gemini") for semantic extraction instead of dispatching subagents. The default Gemini model is gemini-3-flash-preview; set MAPMMD_GEMINI_MODEL or pass --model in headless CLI flows to override it.

No other API keys are read. When GEMINI_API_KEY/GOOGLE_API_KEY are unset, semantic extraction falls to the host agent itself — the running session is the LLM. On a host that dispatches subagents (e.g. Claude Code), dispatch them as written in Part B. On a host that runs the CLI directly in a terminal and cannot dispatch subagents, do not stall: a code-only corpus has no semantic work, so write the empty semantic file (Part B "Fast path") and continue to Part C; for a corpus with docs/papers/images, either set a Gemini key or extract those inline yourself, but in no case prompt for ANTHROPIC_API_KEY — that prompt is a misread of this skill.

Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.

Note: Parallelizing AST + semantic saves 5-15s on large corpora. AST is deterministic and fast; start it while subagents are processing docs/papers.

Part A - Structural extraction for code files

For any code files detected, run AST extraction in parallel with Part B subagents:

$(cat mapmmd-out/.mapmmd_python) -c "
import sys, json
from mapmmd.extract import collect_files, extract
from pathlib import Path
import json

code_files = []
detect = json.loads(Path('mapmmd-out/.mapmmd_detect.json').read_text(encoding=\"utf-8\"))
for f in detect.get('files', {}).get('code', []):
    code_files.extend(collect_files(Path(f)) if Path(f).is_dir() else [Path(f)])

if code_files:
    result = extract(code_files, cache_root=Path('INPUT_PATH'))
    Path('mapmmd-out/.mapmmd_ast.json').write_text(json.dumps(result, indent=2, ensure_ascii=False), encoding=\"utf-8\")
    print(f'AST: {len(result[\"nodes\"])} nodes, {len(result[\"edges\"])} edges')
else:
    Path('mapmmd-out/.mapmmd_ast.json').write_text(json.dumps({'nodes':[],'edges':[],'input_tokens':0,'output_tokens':0}, ensure_ascii=False), encoding=\"utf-8\")
    print('No code files - skipping AST extraction')
"

Part B - Semantic extraction (parallel subagents)

Fast path: If detection found zero docs, papers, and images (code-only corpus), skip Part B entirely and go straight to Part C. AST handles code - there is nothing for semantic subagents to do. First write an empty semantic file so Part C's merge has its input (it reads .mapmmd_semantic.json unconditionally; without this a code-only run hits FileNotFoundError):

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from pathlib import Path
Path('mapmmd-out/.mapmmd_semantic.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
"

MANDATORY: You MUST use the Agent tool here. Reading files yourself one-by-one is forbidden - it is 5-10x slower. If you do not use the Agent tool you are doing this wrong.

Before dispatching subagents, print a timing estimate:

• Load total_words and file counts from mapmmd-out/.mapmmd_detect.json
• Estimate agents needed: ceil(uncached_non_code_files / 22) (chunk size is 20-25)
• Estimate time: ~45s per agent batch (they run in parallel, so total ≈ 45s × ceil(agents/parallel_limit))
• Print: "Semantic extraction: ~N files → X agents, estimated ~Ys"

Step B0 - Check extraction cache first

Before dispatching any subagents, check which files already have cached extraction results:

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from mapmmd.cache import check_semantic_cache
from pathlib import Path

detect = json.loads(Path('mapmmd-out/.mapmmd_detect.json').read_text(encoding=\"utf-8\"))
# Only content files go to semantic extraction. Code is already covered structurally
# by the AST pass (Part A); flattening every category here makes subagents re-read
# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]

cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files, root='INPUT_PATH')

# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
# run so Part C never merges a stale .mapmmd_cached.json (#1392).
if cached_nodes or cached_edges or cached_hyperedges:
    Path('mapmmd-out/.mapmmd_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
else:
    Path('mapmmd-out/.mapmmd_cached.json').unlink(missing_ok=True)
Path('mapmmd-out/.mapmmd_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"

Only dispatch subagents for files listed in mapmmd-out/.mapmmd_uncached.txt. If all files are cached, skip to Part C directly.

Step B1 - Split into chunks

Load files from mapmmd-out/.mapmmd_uncached.txt. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). When splitting, group files from the same directory together so related artifacts land in the same chunk and cross-file relationships are more likely to be extracted.

Step B2 - Dispatch ALL subagents in a single message

Call the Agent tool multiple times IN THE SAME RESPONSE - one call per chunk. This is the only way they run in parallel. If you make one Agent call, wait, then make another, you are doing it sequentially and defeating the purpose.

IMPORTANT - subagent type: Always use subagent_type="general-purpose". Do NOT use Explore - it is read-only and cannot write chunk files to disk, which silently drops extraction results. General-purpose has Write and Bash access which the subagent needs.

Concrete example for 3 chunks:

[Agent tool call 1: files 1-15, subagent_type="general-purpose"]
[Agent tool call 2: files 16-30, subagent_type="general-purpose"]
[Agent tool call 3: files 31-45, subagent_type="general-purpose"]

All three in one message. Not three separate messages.

Each subagent receives this exact prompt (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE, and CHUNK_PATH).

CHUNK_PATH must be an absolute path — derive it before dispatching:

PROJECT_ROOT=$(pwd)  # cwd — where Part C globs mapmmd-out/ (NOT .mapmmd_root/scan dir, #1392)
# Then for chunk N: CHUNK_PATH="${PROJECT_ROOT}/mapmmd-out/.mapmmd_chunk_0N.json"

Subagent prompt template:

See references/extraction-spec.md for the exact subagent prompt (JSON schema, node-ID rules, confidence rubric, frontmatter, hyperedge, and vision rules). Load it only here, only when at least one chunk holds a doc, paper, or image; a pure-code corpus has skipped Part B and never reads it. Pass each subagent that prompt verbatim with FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE, and CHUNK_PATH substituted, and have it write the result to CHUNK_PATH.

Step B3 - Collect, cache, and merge

Wait for all subagents. For each result:

• Check that mapmmd-out/.mapmmd_chunk_NN.json exists on disk — this is the success signal
• If the file exists and contains valid JSON with nodes and edges, include it and save to cache
• If the file is missing, the subagent was likely dispatched as read-only (Explore type) — print a warning: "chunk N missing from disk — subagent may have been read-only. Re-run with general-purpose agent." Do not silently skip.
• If a subagent failed or returned invalid JSON, print a warning and skip that chunk - do not abort

If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure subagent_type="general-purpose" is used.

Merge all chunk files into .mapmmd_semantic_new.json. After each Agent call completes, read the real token counts from the Agent tool result's usage field and write them back into the chunk JSON before merging — the chunk JSON itself always has placeholder zeros. Then run:

$(cat mapmmd-out/.mapmmd_python) -c "
import json, glob
from pathlib import Path

chunks = sorted(glob.glob('mapmmd-out/.mapmmd_chunk_*.json'))
all_nodes, all_edges, all_hyperedges = [], [], []
total_in, total_out = 0, 0
for c in chunks:
    d = json.loads(Path(c).read_text(encoding=\"utf-8\"))
    all_nodes += d.get('nodes', [])
    all_edges += d.get('edges', [])
    all_hyperedges += d.get('hyperedges', [])
    total_in += d.get('input_tokens', 0)
    total_out += d.get('output_tokens', 0)
Path('mapmmd-out/.mapmmd_semantic_new.json').write_text(json.dumps({
    'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges,
    'input_tokens': total_in, 'output_tokens': total_out,
}, indent=2, ensure_ascii=False), encoding=\"utf-8\")
print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens')
"

Save new results to cache:

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from mapmmd.cache import save_semantic_cache
from pathlib import Path

new = json.loads(Path('mapmmd-out/.mapmmd_semantic_new.json').read_text(encoding=\"utf-8\")) if Path('mapmmd-out/.mapmmd_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
saved = save_semantic_cache(new.get('nodes', []), new.get('edges', []), new.get('hyperedges', []), root='INPUT_PATH')
print(f'Cached {saved} files')
"

Merge cached + new results into mapmmd-out/.mapmmd_semantic.json:

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from pathlib import Path

cached = json.loads(Path('mapmmd-out/.mapmmd_cached.json').read_text(encoding=\"utf-8\")) if Path('mapmmd-out/.mapmmd_cached.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
new = json.loads(Path('mapmmd-out/.mapmmd_semantic_new.json').read_text(encoding=\"utf-8\")) if Path('mapmmd-out/.mapmmd_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}

all_nodes = cached['nodes'] + new.get('nodes', [])
all_edges = cached['edges'] + new.get('edges', [])
all_hyperedges = cached.get('hyperedges', []) + new.get('hyperedges', [])
seen = set()
deduped = []
for n in all_nodes:
    if n['id'] not in seen:
        seen.add(n['id'])
        deduped.append(n)

merged = {
    'nodes': deduped,
    'edges': all_edges,
    'hyperedges': all_hyperedges,
    'input_tokens': new.get('input_tokens', 0),
    'output_tokens': new.get('output_tokens', 0),
}
Path('mapmmd-out/.mapmmd_semantic.json').write_text(json.dumps(merged, indent=2, ensure_ascii=False), encoding=\"utf-8\")
print(f'Extraction complete - {len(deduped)} nodes, {len(all_edges)} edges ({len(cached[\"nodes\"])} from cache, {len(new.get(\"nodes\",[]))} new)')
"

Clean up temp files: rm -f mapmmd-out/.mapmmd_cached.json mapmmd-out/.mapmmd_uncached.txt mapmmd-out/.mapmmd_semantic_new.json

Part C - Merge AST + semantic into final extraction

$(cat mapmmd-out/.mapmmd_python) -c "
import sys, json
from pathlib import Path

ast = json.loads(Path('mapmmd-out/.mapmmd_ast.json').read_text(encoding=\"utf-8\"))
sem = json.loads(Path('mapmmd-out/.mapmmd_semantic.json').read_text(encoding=\"utf-8\"))

# Merge: AST nodes first, semantic nodes deduplicated by id
seen = {n['id'] for n in ast['nodes']}
merged_nodes = list(ast['nodes'])
for n in sem['nodes']:
    if n['id'] not in seen:
        merged_nodes.append(n)
        seen.add(n['id'])

merged_edges = ast['edges'] + sem['edges']
merged_hyperedges = sem.get('hyperedges', [])
merged = {
    'nodes': merged_nodes,
    'edges': merged_edges,
    'hyperedges': merged_hyperedges,
    'input_tokens': sem.get('input_tokens', 0),
    'output_tokens': sem.get('output_tokens', 0),
}
Path('mapmmd-out/.mapmmd_extract.json').write_text(json.dumps(merged, indent=2, ensure_ascii=False), encoding=\"utf-8\")
total = len(merged_nodes)
edges = len(merged_edges)
print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(sem[\"nodes\"])} semantic)')
"

Step 4 - Build graph, cluster, analyze, generate outputs

Before starting: the code blocks below pass directed=IS_DIRECTED to build_from_json(). Replace IS_DIRECTED with True if --directed was given (builds a Dimmd preserving edge direction source→target), otherwise False (the default undirected mmd). Substitute it the same way you substitute INPUT_PATH — do not leave the literal IS_DIRECTED in the code.

mkdir -p mapmmd-out
$(cat mapmmd-out/.mapmmd_python) -c "
import sys, json
from mapmmd.build import build_from_json
from mapmmd.cluster import cluster, score_all
from mapmmd.analyze import god_nodes, surprising_connections, suggest_questions
from mapmmd.report import generate
from mapmmd.export import to_json
from pathlib import Path

extraction = json.loads(Path('mapmmd-out/.mapmmd_extract.json').read_text(encoding=\"utf-8\"))
detection  = json.loads(Path('mapmmd-out/.mapmmd_detect.json').read_text(encoding=\"utf-8\"))

# root= mirrors the --update runbook (#1361): relativize source_file to the same
# base so the full build and incremental --update never drift apart on re-extract.
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
if G.number_of_nodes() == 0:
    print('ERROR: mmd is empty - extraction produced no nodes.')
    print('Possible causes: all files were skipped, binary-only corpus, or extraction failed.')
    raise SystemExit(1)
communities = cluster(G)
cohesion = score_all(G, communities)
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
gods = god_nodes(G)
surprises = surprising_connections(G, communities)
labels = {cid: 'Community ' + str(cid) for cid in communities}
# Placeholder questions - regenerated with real labels in Step 5
questions = suggest_questions(G, communities, labels)

# Export FIRST and honor the #479 shrink-guard: to_json returns False (writing
# nothing) when the new graph is smaller than the existing graph.json. Only write
# GRAPH_REPORT.md + the analysis sidecar when the graph was actually written, so
# they never describe a graph that graph.json doesn't contain (#1392).
wrote = to_json(G, communities, 'mapmmd-out/graph.json')
if not wrote:
    print('ERROR: refused to shrink mapmmd-out/graph.json (existing graph has more nodes; #479).')
    print('If this shrink is intentional (you deleted files), re-run a full build with --force.')
    raise SystemExit(1)
report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('mapmmd-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
analysis = {
    'communities': {str(k): v for k, v in communities.items()},
    'cohesion': {str(k): v for k, v in cohesion.items()},
    'gods': gods,
    'surprises': surprises,
    'questions': questions,
}
Path('mapmmd-out/.mapmmd_analysis.json').write_text(json.dumps(analysis, indent=2, ensure_ascii=False), encoding=\"utf-8\")
print(f'mmd: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges, {len(communities)} communities')
"

If this step prints ERROR: mmd is empty, stop and tell the user what happened - do not proceed to labeling or visualization.

Replace INPUT_PATH with the actual path.

Step 4.5 - mmd health check (read-only integrity gate)

A non-destructive diagnostic on the extraction, before labeling. It surfaces edge collapse, dangling/missing endpoints, and self-loops — the silent-corruption modes of incremental updates and AST/LLM id mismatches. Read-only; never aborts.

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from pathlib import Path
from mapmmd.diagnostics import diagnose_extraction, format_diagnostic_report

extraction = json.loads(Path('mapmmd-out/.mapmmd_extract.json').read_text(encoding=\"utf-8\"))
summary = diagnose_extraction(extraction, directed=IS_DIRECTED, root='INPUT_PATH')
print(format_diagnostic_report(summary))
flags = [f'{summary[k]} {label}' for k, label in (
    ('dangling_endpoint_edges', 'dangling-endpoint edges'),
    ('missing_endpoint_edges', 'missing-endpoint edges'),
    ('self_loop_edges', 'self-loop edges'),
    ('directed_same_endpoint_collapsed_edges', 'collapsed (directed) edges'),
    ('undirected_same_endpoint_collapsed_edges', 'collapsed (undirected) edges'),
) if summary.get(k, 0)]
print('GRAPH HEALTH WARNING: ' + '; '.join(flags) + ' - graph may be incomplete/corrupt.' if flags else 'mmd health: OK (no dangling/missing/collapsed edges).')
"

Substitute IS_DIRECTED and INPUT_PATH as in Step 4. If a GRAPH HEALTH WARNING prints, surface it in the final summary (do not abort — the graph is still usable, but the integrity issue must be visible, per the Honesty Rules).

Step 5 - Label communities

Read mapmmd-out/.mapmmd_analysis.json. For each community key, look at its node labels and write a 2-5 word plain-language name (e.g. "Attention Mechanism", "Training Pipeline", "Data Loading").

Then regenerate the report and save the labels for the visualizer:

$(cat mapmmd-out/.mapmmd_python) -c "
import sys, json
from mapmmd.build import build_from_json
from mapmmd.cluster import score_all
from mapmmd.analyze import god_nodes, surprising_connections, suggest_questions
from mapmmd.report import generate
from pathlib import Path

extraction = json.loads(Path('mapmmd-out/.mapmmd_extract.json').read_text(encoding=\"utf-8\"))
detection  = json.loads(Path('mapmmd-out/.mapmmd_detect.json').read_text(encoding=\"utf-8\"))
analysis   = json.loads(Path('mapmmd-out/.mapmmd_analysis.json').read_text(encoding=\"utf-8\"))

# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}

# LABELS - replace these with the names you chose above
labels = LABELS_DICT

# Regenerate questions with real community labels (labels affect question phrasing)
questions = suggest_questions(G, communities, labels)

report = generate(G, communities, cohesion, labels, analysis['gods'], analysis['surprises'], detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('mapmmd-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
Path('mapmmd-out/.mapmmd_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()}, ensure_ascii=False), encoding=\"utf-8\")
print('Report updated with community labels')
"

Replace LABELS_DICT with the actual dict you constructed (e.g. {0: "Attention Mechanism", 1: "Training Pipeline"}). Replace INPUT_PATH with the actual path.

Step 6 - Generate Obsidian vault (opt-in) + HTML

Generate HTML always (unless --no-viz). Obsidian vault only if --obsidian was explicitly given — skip it otherwise, it generates one file per node.

If --obsidian was given:

• If --obsidian-dir <path> was also given, pass it via --dir. Otherwise defaults to mapmmd-out/obsidian.

mapmmd export obsidian
# or with custom dir: mapmmd export obsidian --dir ~/vaults/my-project

Generate the HTML graph (always, unless --no-viz):

mapmmd export html  # auto-aggregates to community view if graph > 5000 nodes
# or: mapmmd export html --no-viz

Steps 6b-8 - Wiki, Neo4j, FalkorDB, SVG, mmdML, MCP, benchmark (only on their flags)

These run only when their flag is present (--wiki, --neo4j/--neo4j-push, --falkordb/--falkordb-push, --svg, --graphml, --mcp) or, for the token-reduction benchmark, when total_words exceeds 5,000. A default run with no export flags skips all of them. See references/exports.md for each one. Run any --wiki export before Step 9 cleanup so .mapmmd_labels.json is still available.

---

Step 9 - Save manifest, update cost tracker, clean up, and report

$(cat mapmmd-out/.mapmmd_python) -c "
import json
from pathlib import Path
from datetime import datetime, timezone
from mapmmd.detect import save_manifest

# Save manifest for --update
detect = json.loads(Path('mapmmd-out/.mapmmd_detect.json').read_text(encoding=\"utf-8\"))
# In --update mode, 'all_files' carries the full corpus; 'files' is the changed
# subset. Full-rebuild mode populates only 'files', so the fallback handles that.
# root= relativizes the manifest keys to the scan root (same base as the build),
# so the on-disk manifest is portable across clones/machines and a later --update
# matches cached files instead of missing every one (#1417).
save_manifest(detect.get('all_files') or detect['files'], root='INPUT_PATH')

# Update cumulative cost tracker
extract = json.loads(Path('mapmmd-out/.mapmmd_extract.json').read_text(encoding=\"utf-8\"))
input_tok = extract.get('input_tokens', 0)
output_tok = extract.get('output_tokens', 0)

cost_path = Path('mapmmd-out/cost.json')
if cost_path.exists():
    cost = json.loads(cost_path.read_text(encoding=\"utf-8\"))
else:
    cost = {'runs': [], 'total_input_tokens': 0, 'total_output_tokens': 0}

cost['runs'].append({
    'date': datetime.now(timezone.utc).isoformat(),
    'input_tokens': input_tok,
    'output_tokens': output_tok,
    'files': detect.get('total_files', 0),
})
cost['total_input_tokens'] += input_tok
cost['total_output_tokens'] += output_tok
cost_path.write_text(json.dumps(cost, indent=2, ensure_ascii=False), encoding=\"utf-8\")

print(f'This run: {input_tok:,} input tokens, {output_tok:,} output tokens')
print(f'All time: {cost[\"total_input_tokens\"]:,} input, {cost[\"total_output_tokens\"]:,} output ({len(cost[\"runs\"])} runs)')
"
rm -f mapmmd-out/.mapmmd_detect.json mapmmd-out/.mapmmd_extract.json mapmmd-out/.mapmmd_ast.json mapmmd-out/.mapmmd_semantic.json mapmmd-out/.mapmmd_analysis.json
find mapmmd-out -maxdepth 1 -name '.mapmmd_chunk_*.json' -delete 2>/dev/null
rm -f mapmmd-out/.needs_update 2>/dev/null || true

Replace INPUT_PATH with the actual path (same value used in Steps 4-5) so the manifest is relativized to the scan root.

Tell the user (omit the obsidian line unless --obsidian was given):

mmd complete. Outputs in PATH_TO_DIR/mapmmd-out/

  graph.html            - interactive graph, open in browser
  GRAPH_REPORT.md       - audit report
  graph.json            - raw graph data
  obsidian/             - Obsidian vault (only if --obsidian was given)

If mapmmd saved you time, consider supporting it: https://github.com/sponsors/getwinharris

Replace PATH_TO_DIR with the actual absolute path of the directory that was processed.

Then paste these sections from GRAPH_REPORT.md directly into the chat:

• God Nodes
• Surprising Connections
• Suggested Questions

Do NOT paste the full report - just those three sections. Keep it concise.

Then immediately offer to explore. Pick the single most interesting suggested question from the report - the one that crosses the most community boundaries or has the most surprising bridge node - and ask:

"The most interesting question this graph can answer: [question]. Want me to trace it?"

If the user says yes, run /mapmmd query "[question]" on the graph and walk them through the answer using the graph structure - which nodes connect, which community boundaries get crossed, what the path reveals. Keep going as long as they want to explore. Each answer should end with a natural follow-up ("this connects to X - want to go deeper?") so the session feels like navigation, not a one-shot report.

The graph is the map. Your job after the pipeline is to be the guide.

---

Interpreter guard for subcommands

Before running any subcommand below (--update, --cluster-only, query, path, explain, add), check that .mapmmd_python exists. If it's missing (e.g. user deleted mapmmd-out/), re-resolve the interpreter first:

if [ ! -f mapmmd-out/.mapmmd_python ]; then
    MAPMMD_BIN=$(which mapmmd 2>/dev/null)
    if [ -n "$MAPMMD_BIN" ]; then
        PYTHON=$(head -1 "$MAPMMD_BIN" | tr -d '#!')
        case "$PYTHON" in *[!a-zA-Z0-9/_.-]*) PYTHON="python3" ;; esac
    else
        PYTHON="python3"
    fi
    mkdir -p mapmmd-out
    "$PYTHON" -c "import sys; open('mapmmd-out/.mapmmd_python', 'w', encoding='utf-8').write(sys.executable)"
fi

For --update and --cluster-only

Both are non-default subcommands. --update re-extracts only new or changed files; --cluster-only reruns clustering on the existing graph. See references/update.md for both flows.

---

For /mapmmd query

When mapmmd-out/graph.json already exists and the user asks a question about the corpus, answer from the graph rather than rebuilding it:

mapmmd query "<question>"

Before traversal, expand the question against the graph's own vocabulary so a wording mismatch does not collapse the answer to noise. If the mapmmd query CLI is unavailable, fall back to an inline NetworkX traversal of mapmmd-out/graph.json. Answer using only what the graph output contains, and quote source_location when citing a specific fact. For that vocab-expansion step, the BFS/DFS traversal modes, the --budget cap, the NetworkX fallback, save-result feedback, and the /mapmmd path and /mapmmd explain flows, see references/query.md.

---

For /mapmmd add and --watch

Neither is part of the default build. When the user runs /mapmmd add <url> to fetch a URL into the corpus, or passes --watch to auto-rebuild on file changes, see references/add-watch.md.

---

For the commit hook and native CLAUDE.md integration

When the user asks to install the post-commit auto-rebuild hook or wire mapmmd into a project's CLAUDE.md, see references/hooks.md.

---

Honesty Rules

• Never invent an edge. If unsure, use AMBIGUOUS.
• Never skip the corpus check warning.
• Always show token cost in the report.
• Never hide cohesion scores behind symbols - show the raw number.
• Never run HTML viz on a graph with more than 5,000 nodes without warning the user.