Learning Loop

Learning loop: capture, consolidate, absorb, retain

Robium captures possible signals silently and preserves enough transcript evidence to evaluate them later. Consolidation and absorption happen only when invoked or at a natural milestone; nothing from memory, observations, or the queue is inserted into a new prompt automatically.

When to use this skill

• Consolidating the .robium queue, archived transcript windows, and dated learnings into evidence-counted observations.
• Absorbing status: ready observations into versioned skill deltas.
• Refining skill content for duplication, staleness, harmful guidance, or growth through the same deterministic pipeline.
• Running blind delta variants for a genuinely contested edit, or deep-verifying a pinned example fixture.
• Reporting queue/backlog/eval/ledger health or pruning transcript evidence after linked changes have landed.
• Route external repository research to mining; route fresh skill creation and direct-edit quality mechanics to skill-author.

Key directives

• Delegation posture: embed. This skill owns the workflows; deterministic mechanics live in the repository's scripts/engine directory and capture hooks live under the repository's hooks directory.
• Capture only, never recall. UserPromptSubmit may append a flag and SessionEnd may archive a transcript; SessionStart is silent. Hooks must never inject queue reminders, observations, memory, or recall text into a prompt.
• Batch bookkeeping at natural milestones. Do not interrupt implementation for a manual learning entry or mandatory per-skill retro. Hooks quarantine candidates; consolidation promotes useful signals and discards noise in one batch.
• Consolidation never touches skill content. Its write surface is the repository's learnings tree, observations tier, and evidence/eval sidecars.
• Scripts hold the pen during absorb/refine. Draft anchor-targeted deltas; use apply_deltas.py for archive, version, content, and changelog mechanics. Never bypass a refusal. A separately requested direct skill edit still uses the same archive/version/changelog/validation quality bar.
• Absorb consumes status: ready only. Read the source transcript window, apply the promotion bar, deduplicate against tentative/ready/absorbed/rejected observations, and perform one attribution/evidence self-check.
• Review route depends on current authority. External contributors, unattended automation, and unrequested absorption use a branch, PR, and human merge. A maintainer may explicitly authorize local direct-main work in the current conversation; that removes only the branch/PR ceremony, never the archive, evidence, version, changelog, eval, or validation requirements.
• Preserve evidence until it is no longer needed. A transcript linked by a pending queue flag or tentative/ready observation cannot be pruned. Once all linked observations are absorbed or rejected and the corresponding change has landed, prune it deterministically. Unreferenced transcripts expire after 14 days; the archive size ceiling is only the final safeguard.
• Variants are deltas, never full-file rewrites. A candidate refused by apply_deltas.py is broken, not a contender.

Quick start

Inspect loop health without changing anything:

wc -l .robium/queue.jsonl
rg -l '^status: ready' learnings/observations
uv run scripts/engine/skill_metrics.py
uv run scripts/engine/prune_transcripts.py --dry-run

After batched consolidation, absorb ready observations:

uv run scripts/engine/apply_deltas.py learnings/deltas/<file>.yaml --dry-run
uv run scripts/engine/apply_deltas.py learnings/deltas/<file>.yaml
uv run skills/skill-author/scripts/validate_skills.py
uv run scripts/engine/run_trigger_evals.py --skills <touched...> \
  --flip-gate-baseline archive/<skill>/<old-version> --flip-skill <skill>

For the default reviewed route, create the branch before applying and open a PR with the evidence table. Under an explicit maintainer direct-main instruction, apply and verify on local main, then commit there. Neither route authorizes a push, merge, deployment, publication, or paid action that was not requested.

After the absorb/refine change has landed and observation statuses are terminal:

uv run scripts/engine/prune_transcripts.py --dry-run
uv run scripts/engine/prune_transcripts.py --apply

Decision guidance

Consolidate

Read each queue candidate in its archived transcript window. Promote only flags that name a real signal and have enough context to say what was expected and what happened; discard shell noise, false positives, and context-free fragments. Complete evidence fields where the transcript supports them, merge on the same finding, preserve recurrence, and harvest trigger cases. Missing proof stays tentative. A user correction is the strongest single-observation signal, but it still needs accurate attribution and a useful proposed fix.

Consolidation is intentionally batched. A compact milestone summary is useful; a ritual line for every skill that merely loaded is not required. Record a clean result only when it adds evidence, such as a non-trivial example working exactly as documented.

Absorb

For each ready observation, choose the lowest owning skill and the smallest edit that carries the knowledge. Prefer updating an existing anchor to adding a bullet, and adding a bullet to creating a section. Draft a delta from the observation's symptom, fix, dead ends, anchors, and source evidence; preview it; apply through the engine; run validator, trigger evals, flip gate, task checks as applicable, and a scoped duplicate check. The review artifact or direct-main commit should include the same evidence table and verification results.

Refine

Run the five evidence-armed hygiene passes from the refine reference: harmful guidance first, duplication, staleness, usage/trigger fitness, then growth. Produce deltas through the normal pipeline. A scoped pass follows absorption; a catalog-wide pass is periodic, not an interruption to every build.

Experiment

For a contested structural or trigger edit, draft two or three small DELTA variants plus the baseline. run_variants.py applies each to an isolated catalog, runs deterministic trigger/task/token scoring, and may ask a blind judge when configured. The printed recommendation is evidence for the human, not permission for unattended selection or merge.

Deep-verify

deep_verify.py --inventory lists unverified examples and fixture coverage. Running it executes only configured task fixtures; a pass emits an annotation delta, while a failure remains a finding. Promotion still goes through the same apply/validation/review route.

Transcript retention

prune_transcripts.py reports every keep/delete decision and defaults to a dry run. It protects queue-linked evidence and any transcript tied to a nonterminal or not-yet-consolidated learning. It deletes terminal linked evidence only when all linked observations are absorbed/rejected, and otherwise deletes only unreferenced files older than 14 days. Review the report before --apply.

Platform gotchas

• Capture flags are pointers, not conclusions. Command failures often contain expected probes or path misses and should be discarded when they teach nothing.
• apply_deltas.py refuses an existing archive slot; resolve the prior pending change rather than overwriting history.
• Trigger judging may use an external model. In offline CI, use the documented deterministic/no-LLM path and report that limitation.
• Transcript references must retain the exact archive filename and source ID; vague prose cannot be protected or cleaned deterministically.
• The archive size ceiling can remove old evidence under pressure. Normal operation should keep the archive below that point through status-aware cleanup rather than relying on the ceiling.

Customization

• Contributor/unattended mode: always use the branch/PR/human-review route.
• Explicit maintainer mode: local direct-main is allowed only when the current conversation says so; do not persist that authority into future work.
• User-local learning: path-parameterize observations and deltas into a user overlay, but retain capture-only prompts and the same promotion bar.
• Retention window: keep 14 days as the default. A different window is an explicit repository policy choice and must never weaken pending-evidence protection.

References

• references/delta-format.md - delta schema, operations, and refusals.
• references/promotion-bar.md - queue to learning to observation to delta.
• references/refine-passes.md - five evidence-armed hygiene passes.
• references/learnings-loop.md - historical/manual fallback context.
• references/experiment-recipes.md - small-variant evaluation recipes.
• Engine tools in the repository's scripts/engine directory: apply_deltas, run_trigger_evals, observations, placement, run_variants, deep_verify, run_task_checks, skill_metrics, mine_transcripts, and prune_transcripts.

Changelog

• 1.0.0 (2026-08-27): remove recall and all prompt injection; make hooks capture-only and SessionStart silent; batch consolidation instead of immediate bookkeeping/mandatory retros; add evidence-aware transcript retention; and document the explicit maintainer direct-main exception while preserving all quality gates.
• 0.2.1 (2026-08-03): style pass; removed em dashes throughout (no content changes).
• 0.2.0 (2026-08-02): add experiment and deep-verify modes and their engine tools.
• 0.1.1 (2026-08-02): document the learnings/deltas location convention.
• 0.1.0 (2026-08-02): initial consolidate/absorb/refine/status pipeline.