agent-wiki-compare-out…
Compare successful and failed normalized agent trajectories to derive evidence-backed agent-wiki guidelines. Use when Codex has…
Apply data-retention rules to the local evolve store — flag or delete stale and unused memories and expired sessions (dry-run by default)
Claude Code
$ npx -y skills add AgentToolkit/altk-evolve --skill evolve-lite-retention --agent claude-code
Codex
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a codex -y
Cursor
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a cursor -y
Gemini CLI
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a gemini-cli -y
GitHub Copilot
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a github-copilot -y
OpenCode
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a opencode -y
Windsurf
$ npx skills add AgentToolkit/altk-evolve --skill evolve-lite-retention -a windsurf -y
name: evolve-lite:retention description: Apply data-retention rules to the local evolve store — flag or delete stale and unused memories and expired sessions (dry-run by default)
Runs the data-retention rules configured in `evolve.config.yaml` against the local `.evolve/` store: private entities under `.evolve/entities/` and session transcripts under `.evolve/trajectories/`. Rules match by entity type plus age (`max_age_days`, from file mtime) or disuse (`max_unused_days`, from `recall` rows in `.evolve/audit.log`), and either **flag** (a non-destructive frontmatter marker) or **delete**. A `delete` rule on trajectories with `cascade_derived: true` also deletes the entities derived from those sessions (linked by their `trajectory:` frontmatter).
The script is **dry-run by default** — it never mutates anything unless `--apply` is passed.
Read `evolve.config.yaml`. If there is no `retention:` block with a `rules:` list, show the user this example and stop:
retention:
rules:
- name: stale-guidelines
entity_type: guideline
max_age_days: 90
action: flag
- name: old-sessions
entity_type: trajectory
max_age_days: 365
action: delete
cascade_derived: trueEach rule needs a `name` and at least one of `max_age_days` / `max_unused_days`; `action` is `flag` (default) or `delete`. Rules are checked top-to-bottom and the first match wins, so put narrow rules first — and put a longer-threshold `delete` before a shorter-threshold `flag` on the same type, or the flag shadows the delete and it never fires.
A `delete` rule may also set `on_missing_access_signal` to say what happens when an `unused` match has **no recorded recall** (disuse measured from file mtime): `skip` (default, fail-safe — spare it and report it as skipped), `flag` (downgrade the delete to a non-destructive flag), or `delete` (delete on the mtime fallback anyway). The safe default means an `unused` delete never destroys a memory the agent simply never recorded a recall for.
From the project root:
python3 .bob/skills/evolve-lite-retention/scripts/run_retention.py
Show the user the full report — every entry says what *would* be flagged, deleted, or skipped, why (`age`, `unused`, or `cascade:<session>`), by which rule, and on what evidence. `SKIP` lines are entities a `delete` rule matched on a degraded signal but spared under `on_missing_access_signal: skip`. Relay any `WARNING` lines too: they say when a signal was weaker than it looks (for example, disuse measured from file mtime because the entity has no recall row).
Deleting is destructive and there is no undo. Ask the user to confirm the dry-run report first. Never pass `--apply` without an explicit go-ahead.
python3 .bob/skills/evolve-lite-retention/scripts/run_retention.py --apply
Relay the applied report back to the user.
`retention_rule` into the entity's frontmatter; the file's mtime is preserved so its age clock doesn't reset. Trajectory files are opaque JSON, so their flag is recorded in `.evolve/audit.log` only.
`event: "retention"` row.
are git clones owned by the sync skill and local deletes would be restored on the next sync.
list in JSON or YAML), overriding the config block.
(editing an entity resets its clock — there is no `created_at` in the store), and the disuse signal only exists for entities the agent recorded via the recall audit step. The `trajectory:` cascade link is supported but nothing writes it automatically today, so `cascade_derived` only fires for entities where that key was set by hand.
Evolve helps AI agents learn from experience. It turns past trajectories into reusable guidelines that help agents complete future tasks, without changing model weights.
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