Memory and skills

Sigil has three graph-native memory layers plus the library of compiled skills.

The three memory layers

• Semantic (Memory nodes) — durable facts. Add with remember_fact (chat) or sigil teach "<fact>"; retrieve with recall_memory / sigil recall "<query>". Facts are also grown automatically by distilling durable facts from completed tasks, and are injected into chat context each turn.
• Episodic (Attempt nodes) — every run, its outcome, and a summary.
• Procedural (TaskGraph nodes) — the compiled skills themselves.

Retrieval mode is set by recall_mode: lexical (deterministic word overlap, no model), vector (litellm embeddings + cosine, needs embed_model), or hybrid.

Skills (compiled procedures)

A skill enters the library two ways: explicitly — sigil compile ./SKILL.md runs the full gated pipeline (see skill-compilation) — or at runtime via solve, where "compiling" is the same compiler applied on demand: the frontier model authors the typed procedure (AG-IR), the mechanical half lowers it, and the result persists as a TaskGraph. Later requests of the same kind are a HIT and run on the cheap model; a near-match is a PARTIAL (the procedure is recompiled to cover it); a new kind is a MISS (compiled fresh).

In chat, learn_skill(task) compiles a reusable skill on demand; use it only when you want a durable, repeatable procedure rather than a one-off action.

Managing the library

sigil library                 # compiled skills with run stats
sigil eval <sig> [probe]      # grounded-eval a skill (run + judge the artifact)
sigil relearn <sig> [hint]    # recompile a skill fresh with the frontier
sigil forget <sig>            # remove a skill entirely (all versions + files)

The auto_eval valve (with eval_threshold) judges each skill run and can automatically relearn a degraded procedure. Isolation: each compiled module runs in a separate subprocess, so a run's throwaway task-graph never touches Sigil's own persistent graph.