# Learning Loop > Robium's explicit learning engine: consolidate capture-only queue flags and transcript evidence into observations; absorb ready observations through deterministic anchor-targeted skill deltas; refine, A/B contested edits, deep-verify examples, prune landed transcript evidence, and report loop health. Use when: "consolidate", "absorb", "run the loop", "update my skills", "refine the skills", "learning loop status", "A/B this edit", "deep verify", or "clean transcripts". Never recalls or injects observations into new prompts. Not for external-repo mining, fresh skill authoring, or app implementation. - Skill: `robium-ai/learning-loop-6` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add robium-ai/learning-loop-6` - Raw SKILL.md: https://api.skillmd.com/api/skills/robium-ai/learning-loop-6/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: robium-ai (https://skillmd.com/u/robium-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/robium-ai/learning-loop-6 --- # 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: ```bash 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: ```bash uv run scripts/engine/apply_deltas.py learnings/deltas/.yaml --dry-run uv run scripts/engine/apply_deltas.py learnings/deltas/.yaml uv run skills/skill-author/scripts/validate_skills.py uv run scripts/engine/run_trigger_evals.py --skills \ --flip-gate-baseline archive// --flip-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: ```bash 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.