Immutable. This exact content is served forever at /api/v1/blob/462b8facc4bab7bb.
---
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.
---
# 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`. Metadata uses PyYAML 6.0.3 (safe scalar loader); uv installs the declared dependency. API calls use urllib.
## Quick Reference: 6 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 + strict YAML checks → scoped repair manifest |
| **CREATE / UPDATE** | New knowledge card, or new info about an existing one | `search.py` dedup → ADD/UPDATE/SUPERSEDE → 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` |
| **DAILY → CARDS** | Turn a day's raw notes into linked cards | `daily.py extract` → dedup-first process → link |
---
## 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:** `enforce.py --check` on the scoped manifest + `graph.py health` on the staged vault; resolve concrete validation/link findings. A health score is not an acceptance gate.
### 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 (diagnosis before scoped repair)
Read [integrity.md](references/integrity.md) before maintenance or creating cards.
1. Build a staged view or run read-only checks. Use graph health for links and enforce --check for strict YAML/schema validation.
2. Separate missing targets, ambiguous links, no-incoming cards, isolated cards and reachability from hubs. Counts overlap; the health score is not an acceptance gate.
3. Inspect current sources and prepare an explicit file manifest. Preserve original bytes and body content for mechanical changes. Never infer a business state from an unknown status.
4. Apply only the reviewed repair scope. Do not mutate readonly archival paths. MOC generation preserves legacy/manual files unless an explicit managed block exists.
5. Recheck changed metadata and links, then publish the completed batch through the vault transport. Verify remote delivery separately.
The health orchestration command reports only; it does not automatically fix links, regenerate MOCs or change decay. Use uv run for all Python entry points so the declared YAML dependency is available.
## Workflow 3: CREATE / UPDATE (dedup-first, then link)
**When to use:** Recording any card, or new information about something the vault may already track. Always look up first, always link immediately — a near-duplicate is the most common mistake; an orphan card is wasted knowledge.
### Step 0: LOOKUP (mandatory — never skip)
```bash
uv run scripts/search.py "<entity / key phrase>" --vault <vault-dir> --json
# fallback: grep -ril "<name>" <vault-dir>
```
Pick the operation (full rules: `references/update-in-place.md`):
- **ADD** — no existing card → create it (steps 1–5 below).
- **NOOP** — already captured, unchanged → stop.
- **UPDATE** — same subject, new enrichment → open the card, sharpen `description`, append a dated line under `## Log`, re-`touch`.
- **SUPERSEDE** — new fact *contradicts* a current value → rewrite the current value (frontmatter field + top of description = "Compiled Truth"), move the OLD value to append-only `## History` (`- 2026-03→2026-06 · company: TDI Group`), set `updated:`. Whole card obsolete → `status: superseded` + `superseded_by: "[[new-card]]"`.
Only when the operation is **ADD**, continue:
### Steps (ADD path)
1. **Type:** Pick from schema `node_types`
2. **Path:** Reverse-lookup `domain_inference` to find target folder:
```python
# 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
```bash
uv run scripts/graph.py orphans <vault-dir> # find orphans
# For each orphan: connect to nearest hub or sibling card
```
### Link Strengthening
```bash
# 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
```bash
uv run scripts/orchestrate.py health <vault-dir> # automated health workflow
uv run scripts/orchestrate.py bootstrap <vault-dir> # full bootstrap (one command)
```
`health` runs graph and strict metadata checks only. It leaves cards and indexes unchanged; graph diagnostic reports may be written.
`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.
```bash
# Phase 1: prep dedup clusters for YOUR review
uv run 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
uv run 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
uv run 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.
---
## Workflow 6: DAILY → CARDS (day's notes → linked cards)
**When to use:** Turning a `daily/YYYY-MM-DD.md` note file into durable cards. Judgment-first — the scripts extract candidates; you classify, dedup, and link.
**Full guide:** `references/daily-processor.md`
### Summary (4 phases + idempotency)
1. **CAPTURE:** `daily.py extract <daily-dir> <vault-dir> [date]` (candidates → `.graph/`) + `supersede.py <vault>` (conflict scan). Read schema `node_types`, list noteworthy items + the day's topics.
2. **PROCESS:** per item, run the Workflow 3 Step 0 decision (ADD / UPDATE / SUPERSEDE / NOOP — `references/update-in-place.md`); resolve every `.graph/supersede-candidates.json` entry.
3. **LINK:** apply the Workflow 3 linking protocol (hub + 2 siblings + touch) to each card.
4. **SUMMARIZE (schema-gated):** only if the schema defines a summary type, write a daily-summary card with topics + a MOC down to today's cards and the raw file. No hardcoded DAG.
**Idempotency:** append `<!-- autograph-processed: YYYY-MM-DDTHH:MM cards=N -->` to the end of the daily file; on re-run, skip content above the last marker. Never edit existing lines.
---
## 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
```bash
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/search.py "<query>" --vault <vault-dir> --json # ranked memory search (dedup-first)
uv run scripts/supersede.py <vault-dir> # conflict scan (dry-run)
uv run scripts/supersede.py <vault-dir> --apply # stamp superseded (2-card, newer-by-date)
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
uv run scripts/cleanup.py <vault-dir> [schema.json] # preview bounded description repair
uv run scripts/cleanup.py <vault-dir> [schema.json] --apply # atomic repair before enforce/graph
```
## 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 |
| cleanup.py | Preflight: bounded-memory repair for bug-bloated descriptions |
| 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 |
| search.py | Workflow 3/4: ranked memory search (BM25 FTS5 + link-graph rerank) — dedup-first lookup |
| supersede.py | Workflow 3: deterministic same-entity conflict scan → `.graph/supersede-candidates.json` |
| daily.py | Entity extraction from memory files |
| tests/test_autograph.py | Self-contained tests (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.** |
| **Creating a near-duplicate instead of updating** | **Workflow 3 Step 0 — `search.py`/grep first. Same subject → UPDATE or SUPERSEDE the existing card, never a second one.** |
| **Two contradictory current values on one subject** | **SUPERSEDE: rewrite the current value (Compiled Truth), move the old one to append-only `## History`. Never leave both standing.** |
| **Touching archive cards to active directly** | **Use graduated recall — touch promotes one tier at a time (archive→cold→warm→active).** |
| Running whole-file tools on a giant card | Run `cleanup.py` first; `enforce.py` skips files over 10 MiB and reports them. |
| 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.