Autograph

autograph — typed vault engine

One schema. One graph. Works on any vault.

Overview

No hardcoded domains, types, or paths. The agent discovers structure from data, builds a schema, then enforces it. All scripts share common.py. Zero external dependencies (stdlib only, API calls via urllib).

Quick Reference: 5 Workflows

Workflow 1: BOOTSTRAP (raw vault → structured graph)

When to use: New vault, bulk import, first setup. Run once, then switch to HEALTH.

Full guide: references/bootstrap-workflow.md

Summary (10 phases)

1. Discover: uv run scripts/discover.py <vault-dir> --verbose > /tmp/discovery.json
2. Generate schema: Script baseline (generate_schema.py) + agent swarm (swarm_prepare.py → Wave 1 haiku → swarm_reduce.py → Wave 2 sonnet). NEVER skip the swarm.
3. Review: Human approves schema. Never auto-apply.
4. Bootstrap + Enforce: engine.py init + enforce.py --apply
5. Link cleanup: link_cleanup.py --apply (before enrichment)
6. Tag enrich: enrich.py tags --apply (via OpenRouter API)
7. Deduplicate: dedup.py --apply (before link enrichment)
8. Link enrich: enrich.py swarm-links --apply (always swarm-links, never links)
9. MOC generation: moc.py generate
10. Verify: graph.py health + enforce.py → target 90+/100

Critical Rules

• Always run Phase 2B (agent swarm). Script alone cannot classify unstructured content.
• Always use swarm-links, not links (0.3% vs 81.6% match rate).
• Always dry-run first — run without --apply before applying.
• Dedup before link enrich — prevents links to merged/trashed files.

Workflow 2: HEALTH (daily graph maintenance)

When to use: Daily upkeep, after edits, or when health score drops. This is the most common workflow.

Decision Logic

1. Run `graph.py health <vault-dir>` → check score
2. If health < 90 → investigate:
   a. broken_links > 0  → `graph.py fix <vault-dir> --apply`
   b. orphans > 5       → connect orphans to hub files (see Workflow 4)
   c. desc_coverage < 70% → add descriptions to files missing them
3. Run `moc.py generate <vault-dir>` → regenerate indexes
4. Run `engine.py decay <vault-dir>` → recalculate relevance + tiers
5. Run `graph.py health <vault-dir>` → confirm improvement

Thresholds & Action Triggers

Commands

uv run scripts/graph.py health <vault-dir>           # health check
uv run scripts/graph.py fix <vault-dir> --apply       # fix broken links
uv run scripts/moc.py generate <vault-dir>            # regenerate MOCs
uv run scripts/engine.py decay <vault-dir>            # decay cycle (Ebbinghaus)
uv run scripts/engine.py decay <vault-dir> --dry-run  # preview decay changes
uv run scripts/engine.py stats <vault-dir>            # tier distribution
uv run scripts/engine.py creative 5 <vault-dir>       # resurface forgotten cards

Workflow 3: CREATE (new card with immediate linking)

When to use: Creating any new vault card. Always link immediately — orphan cards are wasted knowledge.

Steps

1. Type: Pick from schema node_types
2. Path: Reverse-lookup domain_inference to find target folder:

   # domain_inference maps path→domain. To find folder for domain "crm":
   for path_prefix, domain in schema['domain_inference'].items():
       if domain == 'crm':
           target_folder = path_prefix  # e.g. "work/crm/"
           break
   

3. Frontmatter: Write description (search snippet, not title repeat), tags (2-5, lowercase, kebab-case), status from type's enum
4. LINKING PROTOCOL (mandatory): a. Add ## Related section with [[hub]] file of the domain
   • Hub = _index.md or MEMORY.md of that domain b. Find 2-3 sibling cards of same type+domain → add [[links]]
   • uv run scripts/graph.py backlinks <vault> <hub> → find siblings
   • Or: read vault-graph.json → filter nodes by type+domain c. Run uv run scripts/engine.py touch <new-file>
5. Verify checklist:
   • Hub linked?
   • 2+ related cards found?
   • description ≠ title repeat?
   • tags: 2-5, lowercase, kebab-case?
   • status ∈ schema enum?

Templates: references/card-templates.md

Workflow 4: SEARCH & LINK (find + strengthen connections)

When to use: Looking up information in the vault, or strengthening weak areas of the graph.

Navigation (Hub → Links → Target)

1. Determine domain from the topic (work, personal, research, etc. — whatever your schema defines)
2. Start at hub: _index.md or MEMORY.md of that domain
3. Follow links — max 2 hops from hub to target
4. Fallback: uv run scripts/graph.py backlinks <vault> <target> for reverse links

Orphan Rescue

uv run scripts/graph.py orphans <vault-dir>        # find orphans
# For each orphan: connect to nearest hub or sibling card

Link Strengthening

# Files with <2 links → enrich
OPENROUTER_API_KEY=sk-... uv run scripts/enrich.py swarm-links <vault-dir> --apply
uv run scripts/graph.py health <vault-dir>          # verify improvement

Workflow 5: ORCHESTRATE (automated multi-agent workflows)

When to use: Instead of running scripts manually. No API keys — the Claude Code agent does all judgment directly.

Phase 0: Script sequencing

python3 scripts/orchestrate.py health <vault-dir>      # automated health workflow
python3 scripts/orchestrate.py bootstrap <vault-dir>    # full bootstrap (one command)

health runs: graph check > fix broken links > link cleanup > MOC > decay > verify. bootstrap runs: enforce > cleanup > tags > dedup > swarm-links > MOC > verify.

Phases 1-3: Agent judgment (no API keys)

The agent (you) does the judgment directly — read prepared data, decide, write results.

# Phase 1: prep dedup clusters for YOUR review
python3 scripts/orchestrate.py dedup-prepare <vault-dir>
# -> writes .graph/dedup-review-input.json
# -> YOU read clusters, mark approved=true, then: dedup.py --apply-manifest

# Phase 2: prep domain catalogs for YOUR link suggestions
python3 scripts/orchestrate.py link-prepare <vault-dir>
# -> writes .graph/link-review-input.json
# -> YOU read catalogs, suggest links per domain, write batch results

# Phase 3: prep graph data for YOUR semantic analysis
python3 scripts/orchestrate.py graph-prepare <vault-dir>
# -> writes .graph/graph-analysis-input.json
# -> YOU analyze contradictions, missing links, stale hubs, write findings

For Phases 1-3: run the prep command, read the output JSON, do the analysis yourself (you ARE the LLM), write results back. Use Agent tool for parallel domain work in Phase 2.

Decay Engine (Ebbinghaus)

The decay system models memory with three key mechanisms:

1. Access count (spacing effect)

Each touch increments access_count in frontmatter. More retrievals = slower forgetting:

strength = 1 + ln(access_count)
effective_rate = base_rate / strength
relevance = max(floor, 1.0 - effective_rate * days_since_access)

Example: a card touched 5 times has strength = 1 + ln(5) ≈ 2.6, decaying ~2.6x slower than a card touched once.

2. Domain-specific rates

Different content types decay at different rates. Configure in schema decay.domain_rates:

3. Graduated recall

Touch promotes one tier at a time, not a direct jump to active:

archive → cold → warm → active

Each promotion sets last_accessed to a midpoint date, so without re-touch the card naturally drifts back.

Backward compatibility

• Files without access_count → default=1 → 1+ln(1)=1.0 → rate unchanged
• Files without type → default rate applies
• Existing calls calc_relevance(days, schema) → work unchanged (new params optional)

Maintenance Commands

uv run scripts/moc.py generate <vault-dir>                                       # MOC generation
uv run scripts/engine.py decay <vault-dir>                                       # decay cycle
uv run scripts/engine.py touch <vault-dir>/path/card.md                          # touch (graduated)
uv run scripts/engine.py creative 5 <vault-dir>                                  # creative recall
uv run scripts/engine.py stats <vault-dir>                                       # stats
uv run scripts/graph.py backlinks <vault-dir> path/to/card                       # backlinks
uv run scripts/graph.py orphans <vault-dir>                                      # orphans
uv run scripts/graph.py fix <vault-dir> --apply                                  # fix links
uv run scripts/daily.py extract <memory-dir> <vault-dir>                         # entity extraction
uv run scripts/engine.py init <vault-dir> --dry-run                              # bootstrap bare files
OPENROUTER_API_KEY=sk-... uv run scripts/enrich.py swarm-links <vault-dir> --apply  # link enrichment
OPENROUTER_API_KEY=sk-... uv run scripts/enrich.py tags <vault-dir> --apply         # tag enrichment
uv run scripts/link_cleanup.py <vault-dir> --apply                               # link cleanup

Scripts

Files

Common Mistakes

Default Models

Both are production-tested. Do not change defaults without benchmarking.

Troubleshooting

Error: Schema not found → Create schema.json from discover output, or pass path: enforce.py vault/ my-schema.json

Score drops after enforce → New files without frontmatter. Run engine.py init vault/

Dedup picks wrong canonical → Content richness wins. Enrich the right file first, re-run.

Low match rate on swarm-links (<60%) → Check if LLM returns paths instead of stems. Try --force for second pass.

swarm-links shows 0 matched for some batches → Usually network errors. Results are cached — rerun and only failed batches retry.