Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/robium-ai/robium/0.2.0npx skills add robium-ai/robium --skill 0.2.0git clone --depth 1 https://github.com/robium-ai/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/0.2.0)<a href="https://agentmods.dev/skills/robium-ai/robium/0.2.0"><img src="https://agentmods.dev/badge/skills/robium-ai/robium/0.2.0.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 | $0.00215 | $0.02942 |
| Opus 5 | $0.00108 | $0.01471 |
| Sonnet 5 | $0.00043 | $0.00588 |
| Haiku 4.5 | $0.00021 | $0.00294 |
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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- learning-loop — 95% identical, 79 lines differ
How it starts
The opening of the file, as written. The whole thing — 217 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–§10). 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.
- Contested or structural edits ("experiment", "A/B this edit", "try competing fixes") — description rewrites, restructures, competing fixes where the right answer isn't obvious enough for a single draft.
- Scheduled example verification ("deep verify", "verify the examples") — promoting pinned examples/references content from status: unverified once its evals.yaml fixture passes.
- 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, run_variants.py, deep_verify.py, run_task_checks.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.
- Variants are deltas, never rewrites. Full-file candidate rewrites are forbidden (context collapse); a variant that apply_deltas refuses is a broken candidate, not a contender.
What ships with it
5 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.
- 5d ago First seen · 217 lines · 215 tokens per session scan A d09d2d316b03
learning-loop is a skill published in the GitHub repository robium-ai/robium (9 stars, last pushed 7d ago), licensed MIT. It adds 215 tokens to every session and 2,942 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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