Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add robium-ai/robium/plugin install robiumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/robium-ai/robium/0.1.1)<a href="https://agentmods.dev/skills/robium-ai/robium/0.1.1"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/0.1.1.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00175 | $0.02001 |
| Opus 5 | $0.00088 | $0.01001 |
| Sonnet 5 | $0.00035 | $0.00400 |
| Haiku 4.5 | $0.00017 | $0.00200 |
Grade A, and why
learning-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to learning-loop — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning loop — consolidate, absorb, refine
The engine's session-side pipeline (spec: docs/superpowers/specs/2026-08-01-learning-engine-design.md §5–§8). Capture happens automatically (plugin hooks); this skill turns what was captured into observations, and observations into reviewable skill-edit PRs. The human gate is git merge.
When to use this skill
- Promoting queue flags and completing dated learnings entries ("consolidate", a Stop-hook nudge, end of a work block).
- Drafting skill edits from ready observations ("absorb", "update my skills", "run the loop") — output is always a PR branch, never a direct edit.
- Catalog hygiene passes ("refine the skills") — prune/dedup/staleness through the same delta pipeline, report-first.
- Loop health ("learning loop status"): queue depth, unabsorbed backlog, eval-suite size, ledger totals.
- For distilling external repos, use the mining skill; for authoring a new skill from scratch, skill-author.
Key directives
- Delegation posture: embed — the workflows live here; the deterministic tools live at scripts/engine/ (apply_deltas.py, run_trigger_evals.py, ledger.py, mine_transcripts.py, skill_metrics.py, observations.py, placement.py) and in the plugin hooks (recall).
- Scripts hold the pen. LLM roles draft deltas and diagnose; apply_deltas.py applies them (snapshot, bump, changelog, sidecars). Never hand-edit a skill during absorb; never bypass the script's refusals — a refusal is a design signal, not an obstacle.
- Consolidation never touches skills/ content. Its write surface is learnings/, learnings/observations/, and the evidence/evals sidecars — that boundary is what makes it autonomous-safe.
- Absorb consumes status: ready only. The ready bar (proof ≥ 2 | user-correction | three-part evidence | external official) is enforced by the observations lint; do not absorb around it.
- Merge is the gate. Every absorb/refine run ends in a PR with the evidence table; no agent merges to main skills/**. Mid-build sessions capture; they never edit skills directly.
- Dedup against everything seen — including absorbed and rejected observations — or judged-rejected findings reappear forever.
- One self-check round on consolidator and absorber output: re-read the draft against the source transcript window for misattribution, missed dead-ends, wrong anchors, before writing.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 157 lines · 175 tokens per session scan A 5503af706d36
learning-loop is a skill published in the GitHub repository robium-ai/robium (9 stars, last pushed 8d ago), licensed MIT. It adds 175 tokens to every session and 2,001 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to learning-loop, differing in 3 lines, and is treated as a copy.
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