autograph

name

autograph

description

Schema-as-code enforcement for any Obsidian vault. Zero hardcoded domains. Use when creating vault cards, checking vault health, running schema compliance, deduplicating entities, generating MOC indexes, running decay cycles, bootstrapping a vault, fixing wikilinks, finding orphans or backlinks, extracting entities from daily files, or touching/promoting cards. Do NOT use for content generation or non-vault file operations. For quick health checks without schema enforcement, prefer vault-health skill.

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	When to use	Entry point
BOOTSTRAP	New vault / after import / first setup	discover.py → enforce.py → graph.py health
HEALTH	Daily maintenance / on request	graph.py health → fix → moc → decay
CREATE	New knowledge card	Schema lookup → write file → link → touch
SEARCH & LINK	Find info + strengthen connections	Hub → links → target; graph.py orphans → connect
ORCHESTRATE	Automated multi-agent workflows (no API keys)	orchestrate.py health|bootstrap

---

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

Metric	Good	Action needed
Health score	≥90	<90: investigate broken links, orphans
Broken links	0	>0: graph.py fix --apply
Orphan files	<5	≥5: connect to hubs (Workflow 4)
Description coverage	≥80%	<70%: add descriptions
Stale cards (>90d)	<20%	>30%: engine.py creative to resurface

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:

Type	Rate	Half-life (~)	Rationale
contact	0.005	100 days	People don't become irrelevant quickly
crm	0.008	62 days	Deals have medium lifecycle
learning	0.010	50 days	Knowledge fades moderately
project	0.012	42 days	Projects have defined timelines
daily	0.020	25 days	Daily notes lose relevance fast
(default)	0.015	33 days	Fallback for unlisted types

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

Script	Purpose
common.py	Shared: parse FM, walk, domain, decay (Ebbinghaus), wikilinks
discover.py	Workflow 1: scan vault, output enum candidates
generate_schema.py	Workflow 1: turn discovery JSON into draft schema
swarm_prepare.py	Workflow 1: bin-pack vault into agent batches
swarm_reduce.py	Workflow 1: consolidate + validate schema
enforce.py	Workflow 1: validate + autofix against schema
link_cleanup.py	Workflow 1/4: remove phantom wikilinks from ## Related
enrich.py	Workflow 1/4: tags + swarm-links (catalog-oriented link enrichment)
dedup.py	Workflow 1: safe merge + .trash/
graph.py	Workflow 2/4: health score, link repair, backlinks, orphans
moc.py	Workflow 2: MOC generation per domain
orchestrate.py	Workflow 5: multi-agent orchestration (health, bootstrap, dedup-review, link-enrich, graph-analyze)
engine.py	Workflow 2/3: decay (Ebbinghaus), touch (graduated), creative, stats, init
daily.py	Entity extraction from memory files
test_autograph.py	Self-contained tests (~193 cases, temp fixtures)

Files

File	In package?	Purpose
schema.example.json	Yes	Template — copy and customize (includes domain_rates)
schema.json	No	Your vault's schema (generated)
schema.local.json	No	Local override (gitignored)
references/	Yes	Bootstrap workflow, schema docs, card templates, linking protocol

Common Mistakes

Mistake	Fix
Skipping agent swarm in Phase 2	CRITICAL: always run Step 2B. Script alone cannot classify unstructured content. No exceptions.
Using deprecated links subcommand	links was removed (0.3% match rate). Only swarm-links is available — 81.6% match rate.
Creating cards without linking	Always follow Workflow 3 — link to hub + 2 siblings immediately. Orphan cards are wasted knowledge.
Touching archive cards to active directly	Use graduated recall — touch promotes one tier at a time (archive→cold→warm→active).
Sending full vault to one agent	Use swarm_prepare.py — bin-packs into ~50K token batches.
Running Wave 2 without Wave 1	swarm_reduce.py prepare needs JSONL in .graph/swarm/classifications/.
Using schema.example.json directly	Run discover → generate your own schema.json
Description = title repeat	Write specific search snippet
Status not in enum	Check schema's node_types
Skip dry run	Always run without --apply first
Running link enrich before dedup	Creates links to files that get merged/trashed. Dedup first.
Missing OPENROUTER_API_KEY	enrich.py reads from OPENROUTER_API_KEY env var.
Only running swarm-links once	Run again with --force to enrich ALL files.

Default Models

Command	Default model	Override
tags	google/gemini-3-flash-preview	--model flag
swarm-links	google/gemini-2.0-flash-001	--model flag

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.