LUC4N3X Thalarch Compound
A completed task may contain knowledge worth keeping. Compounding is the post-task learning gate, not automatic persistence.
1. Candidate lesson
A lesson qualifies only if:
- supported by evidence from this run;
- likely to matter again;
- not obvious from ordinary code reading;
- stable enough to outlive this task;
- compact enough to retrieve without flooding future context.
Examples:
- a hidden build/test prerequisite;
- an ownership/lifecycle invariant;
- a recurring integration contract;
- a reliable diagnostic command;
- a repository-specific convention;
- a failure pattern and its proven discriminator;
- a benchmark/teacher finding that generalizes beyond one case.
2. Distill through experience
For meaningful outcomes, use thalarch-experience to convert the final evidence ledger into a
compact card:
trigger → context → problem → discriminator → intervention → evidence → transfer → counterexample.
Preserve useful disproven approaches when they prevent a realistic future mistake. Do not preserve the debugging transcript or private chain-of-thought.
3. Classify memory
Pass the distilled candidate through thalarch-memory:
IGNORE— weak, obvious, duplicate, noisy, or non-reusable;SESSION— useful only to finish/recover the current task;PROJECT— durable repository/product knowledge;GENERAL— transferable engineering knowledge with a strong generalization basis.
A single successful anecdote should not automatically become GENERAL.
4. Persistence boundary
Store, by default, only in the current work artifact/ledger.
Durable persistence requires an authorized sink. Do not modify AGENTS.md, GEMINI.md, CLAUDE.md,
repository docs, .thalarch/brain/, or user-level memory merely because a lesson exists.
When project-local durable memory is explicitly authorized, use thalarch-project-brain and prefer
updating/deduplicating an existing entry over appending another near-duplicate.
When a host-native durable memory/RAG system is authorized, use it through thalarch-memory with the
same evidence, privacy, scope, freshness, and retirement rules.
5. Privacy and data minimization
Never compound:
- secrets, credentials, auth material;
- private chain-of-thought;
- raw sensitive personal data when a non-sensitive engineering lesson is sufficient;
- entire logs/messages/documents when a compact derived lesson is enough;
- benchmark answer keys or case-specific hacks.
6. Generalization gate
Promote a lesson beyond project scope only when a stable mechanism/contract supports it, multiple independent cases agree, a frozen evaluation/holdout demonstrates broader benefit, or a human explicitly curates it with known limits.
Write durable lessons as:
Context → invariant/lesson → evidence → when to apply → when NOT to apply.
Delete or retire stale lessons. Knowledge bloat is also debt.
7. Teacher/eval handoff
When thalarch-teacher or thalarch-autoresearch produced the evidence, retain both wins and
counterexamples. A holdout failure is valuable memory about the boundary of a rule; do not hide it to
make the system look smarter.