` Quick-add directly to a category directory (bypasses inbox): 1. Write entry as `$KDIR/<category>/<slug>.md` with `# Title`, body, and `` metadata 2. If category directory doesn't exist, create it 3. Run `lore heal` --- ### `search <query>` ```bash lore search "<query>" --type knowledge ``` Show the script output. For top matches, briefly summarize relevant context. --- ### `view [category] [query]` - No argument: run `lore index` and display the dynamic category/entry listing - With category: `lore read <category>` (resolves knowledge, domains/, and _threads/ files) - With category + query: `lore read <category> --query "<query>"` (matching sections full, non-matching as heading-only) - `view inbox`: read `.md` files from `$KDIR/_inbox/` - For threads: `lore read <slug> --type thread` or `lore read <slug> --type thread --query "<query>"` --- ### `curate` Periodic refinement of the knowledge store (optional, not required). Start with the mechanical pre-scan: ```bash lore curate ``` This lists inbox remnants, medium-confidence entries, and entries missing backlinks. Then apply judgment: 1. **Refile inbox remnants:** If `$KDIR/_inbox/` has `.md` files (from interrupted captures), review each and either file to the correct category or drop. 2. **Quality gate for medium-confidence entries:** Scan entry files for `confidence: medium` in their HTML comment metadata (typically from agent captures). Re-evaluate each against the 4-condition gate: - **Reusable** beyond the original task? - **Non-obvious** — not already covered by another entry or docs? - **Stable** — still accurate? - **High confidence** — can you verify it now? Drop entries that fail the gate. Upgrade passing entries to `confidence: high`. 3. **Deduplicate:** Merge entries that describe the same insight from different contexts. 4. **Backlinks:** Add missing `[[backlinks]]` cross-references between related entries. 5. **Title quality:** Improve vague or generic titles to be specific and scannable. 6. **Stale entries:** Flag or remove entries that contradict current code. 7. **Confidence calibration:** If `$KDIR/_capture_log.csv` exists, count entries with `source=stop-hook`. Compare against the number of stop-hook medium-confidence entries that passed the quality gate (upgraded) vs failed (dropped). Report the pass rate: - `>70%` pass rate: "Stop hook evaluator is well-calibrated" - `40-70%` pass rate: "Stop hook evaluator calibration is acceptable" - `<40%` pass rate: "Stop hook evaluator needs tightening — consider refining triggers in scripts/stop-capture-prompt.txt" 8. Report what was found and fixed: ``` [curate] Done. Dropped: N entries (reasons) Merged: N duplicates Upgraded: N to high confidence Backlinks added: N Stop hook calibration: X/Y passed (Z%) — <assessment> ``` 9. Run `lore heal` **Drop authority:** Curate has explicit authority to remove entries without user confirmation when they fail the 4-condition gate. Report what was dropped in the summary so the user can object. --- ### `renormalize` Redirect to `/renormalize` skill. Invoke `/renormalize` instead. --- ### `heal` ```bash lore heal ``` Show the script output. To apply fixes: `lore heal --fix` --- ### `init [--force]` 1. Check if inside a git repo: `git rev-parse --is-inside-work-tree` 2. If yes: `lore init` 3. If no: inform user, ask for confirmation, then `lore init --force` 4. Report the created path --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/anticorrelator) — claim your Tome and manage your conversions. <!-- tomevault:4.0:skill

# Anticorrelator Lore Memory > Anticorrelator Lore Memory - Skill: `tomevault-io/anticorrelator-lore-memory` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/anticorrelator-lore-memory` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/anticorrelator-lore-memory/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/anticorrelator-lore-memory --- # /memory Skill Manages the per-project knowledge store. Most subcommands are script calls — run and show the output. ## Route $ARGUMENTS Match the first word of `$ARGUMENTS` to a command below. If empty, show the index. Resolve knowledge path first: ```bash KDIR=$(lore resolve) ``` --- ### `add ` Quick-add directly to a category directory (bypasses inbox): 1. Write entry as `$KDIR/<category>/<slug>.md` with `# Title`, body, and `` metadata 2. If category directory doesn't exist, create it 3. Run `lore heal` --- ### `search <query>` ```bash lore search "<query>" --type knowledge ``` Show the script output. For top matches, briefly summarize relevant context. --- ### `view [category] [query]` - No argument: run `lore index` and display the dynamic category/entry listing - With category: `lore read <category>` (resolves knowledge, domains/, and _threads/ files) - With category + query: `lore read <category> --query "<query>"` (matching sections full, non-matching as heading-only) - `view inbox`: read `.md` files from `$KDIR/_inbox/` - For threads: `lore read <slug> --type thread` or `lore read <slug> --type thread --query "<query>"` --- ### `curate` Periodic refinement of the knowledge store (optional, not required). Start with the mechanical pre-scan: ```bash lore curate ``` This lists inbox remnants, medium-confidence entries, and entries missing backlinks. Then apply judgment: 1. **Refile inbox remnants:** If `$KDIR/_inbox/` has `.md` files (from interrupted captures), review each and either file to the correct category or drop. 2. **Quality gate for medium-confidence entries:** Scan entry files for `confidence: medium` in their HTML comment metadata (typically from agent captures). Re-evaluate each against the 4-condition gate: - **Reusable** beyond the original task? - **Non-obvious** — not already covered by another entry or docs? - **Stable** — still accurate? - **High confidence** — can you verify it now? Drop entries that fail the gate. Upgrade passing entries to `confidence: high`. 3. **Deduplicate:** Merge entries that describe the same insight from different contexts. 4. **Backlinks:** Add missing `[[backlinks]]` cross-references between related entries. 5. **Title quality:** Improve vague or generic titles to be specific and scannable. 6. **Stale entries:** Flag or remove entries that contradict current code. 7. **Confidence calibration:** If `$KDIR/_capture_log.csv` exists, count entries with `source=stop-hook`. Compare against the number of stop-hook medium-confidence entries that passed the quality gate (upgraded) vs failed (dropped). Report the pass rate: - `>70%` pass rate: "Stop hook evaluator is well-calibrated" - `40-70%` pass rate: "Stop hook evaluator calibration is acceptable" - `<40%` pass rate: "Stop hook evaluator needs tightening — consider refining triggers in scripts/stop-capture-prompt.txt" 8. Report what was found and fixed: ``` [curate] Done. Dropped: N entries (reasons) Merged: N duplicates Upgraded: N to high confidence Backlinks added: N Stop hook calibration: X/Y passed (Z%) — <assessment> ``` 9. Run `lore heal` **Drop authority:** Curate has explicit authority to remove entries without user confirmation when they fail the 4-condition gate. Report what was dropped in the summary so the user can object. --- ### `renormalize` Redirect to `/renormalize` skill. Invoke `/renormalize` instead. --- ### `heal` ```bash lore heal ``` Show the script output. To apply fixes: `lore heal --fix` --- ### `init [--force]` 1. Check if inside a git repo: `git rev-parse --is-inside-work-tree` 2. If yes: `lore init` 3. If no: inform user, ask for confirmation, then `lore init --force` 4. Report the created path --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/anticorrelator) — claim your Tome and manage your conversions.