v1.0.1 to v1.1.0

---

name: learning-loop

description: >

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.

---

# 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/<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:

```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

### 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

- 1.0.1 (2026-08-27): update the delta-format example to name the current

sibling robot-navigation application instead of its former in-repo path.

- 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.