# Thalarch Memory > Retrieves, classifies, validates, and optionally persists compact durable knowledge so future tasks can reuse verified experience without treating memory as current truth. Use when prior project decisions, recurring failures, user-authorized workflow preferences, or general engineering lessons could materially improve a task. Separates IGNORE/SESSION/PROJECT/GENERAL memory, revalidates load-bearing memories against current evidence, prevents sensitive-data and chain-of-thought persistence, and prefers small retrieval capsules over context dumps. - Skill: `luc4n3x/thalarch-memory` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add luc4n3x/thalarch-memory` - Raw SKILL.md: https://api.skillmd.com/api/skills/luc4n3x/thalarch-memory/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: LUC4N3X (https://skillmd.com/u/luc4n3x) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/luc4n3x/thalarch-memory --- # Thalarch Memory Memory is a **retrieval aid**, not an authority layer. Current user/repository/runtime evidence wins. Core invariant: **current repository/runtime evidence wins over retrieved memory**. ## 1. Four memory classes Classify every candidate before persistence: - `IGNORE` — task noise, obvious facts, duplicated information, weak guesses, transient logs; - `SESSION` — useful only for the active task/session; keep in the evidence ledger, not durable storage; - `PROJECT` — stable knowledge specific to one repository/product, such as invariants, commands, architecture decisions, regressions, design rules, or integration contracts; - `GENERAL` — transferable engineering knowledge that is useful beyond one repository. Do not promote a memory merely because it was expensive to discover. ## 2. Authority and freshness Retrieved memory has trust level `MEMORY` until checked. Before a memory drives a load-bearing decision: 1. identify what current fact it predicts; 2. verify that fact against current repository/runtime/primary-source evidence when practical; 3. compare project/version/environment scope; 4. reject or retire the memory if current evidence contradicts it. A stale project memory must never override current source, manifests, tests, dependency versions, or runtime observations. ## 3. Persistence gate Durable persistence requires all of the following: - the lesson is useful again; - evidence and provenance are recorded; - scope is explicit; - transfer conditions and counterexamples are known enough to avoid blind reuse; - no secret, credential, private chain-of-thought, raw personal data, or unnecessary sensitive data is included; - a durable sink is authorized by the user/project/host. By default, do **not** mutate a repository merely to save memory. Use `thalarch-project-brain` only when project-local durable memory is authorized. `GENERAL` memory is stricter than `PROJECT` memory. A single anecdotal success should normally remain project-scoped or a generalization candidate until it is supported by a stable mechanism/contract, multiple independent cases, or explicit human curation. ## 4. Memory record A useful durable record is compact and inspectable: ```text TITLE SCOPE: PROJECT | GENERAL KIND: invariant | decision | regression | diagnostic | failure-pattern | workflow | design-rule PROJECT: optional stable project key TRIGGER: when retrieval is useful LESSON: concise reusable statement EVIDENCE: what proved it SOURCE: path/commit/run/doc reference when available TRANSFER: conditions where it applies COUNTEREXAMPLE: when it must not be applied CONFIDENCE: evidence-derived, not rhetorical confidence LAST_VERIFIED: timestamp/commit/version when available STATUS: active | retired TAGS: small retrieval vocabulary ``` Do not store private reasoning traces. Store decisions and evidence. ## 5. Retrieval protocol Before meaningful work where history is likely to help: 1. form a narrow retrieval query from task + subsystem + failure mode; 2. search project memory first, then general memory only when useful; 3. retrieve a small top set, not the whole store; 4. remove duplicates and low-similarity results; 5. revalidate any load-bearing item; 6. inject a compact capsule into `thalarch-context`. Use this capsule: ```text MEMORY USED - — scope/evidence/freshness MEMORY REJECTED - — stale, contradictory, wrong scope, or insufficient evidence ``` Silence is better than irrelevant retrieval. ## 6. Contradictions and retirement Never silently overwrite history when a lesson stops being true. - current evidence contradicts memory → mark/retire it; - a new version narrows applicability → update scope/version metadata; - two memories conflict → keep both out of the active capsule until current evidence resolves them; - repeated weak retrievals → prune or retag rather than expanding the prompt. ## 7. Sensitive-data boundary Do not persist: - passwords, tokens, keys, secrets, auth cookies; - full private emails/messages/logs when a non-sensitive lesson is sufficient; - health, financial, identity, or other sensitive personal data unless a separate authorized product memory system explicitly governs that data; - private chain-of-thought or hidden reasoning; - copied third-party proprietary content when a short derived lesson is sufficient. Project memory should describe engineering facts, not become a surveillance archive. ## 8. Portable local store When an opt-in local durable store is appropriate, this skill includes: `scripts/memory_store.py` It provides a standard-library SQLite store with `init`, `add`, `search`, `list`, and `retire` commands. It is a portability fallback, not a requirement: prefer a host-native memory/RAG provider when one is available and authorized. The script deliberately accepts only durable `PROJECT` or `GENERAL` entries. `SESSION` belongs in the current ledger. `GENERAL` insertion requires stronger evidence and explicit generalizability. ## 9. Completion discipline Do not claim that the model has been retrained because memory was added. Memory improves retrieval and continuity around the base model; it does not change the model weights. Report memory-assisted conclusions with the same evidence discipline as any other conclusion.