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/robinslange/learning-loop/researchnpx skills add robinslange/learning-loop --skill researchgit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00066 | $0.01358 |
| Opus 5 | $0.00033 | $0.00679 |
| Sonnet 5 | $0.00013 | $0.00272 |
| Haiku 4.5 | $0.00007 | $0.00136 |
Grade B, and why
research scanned grade B with 2 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 2d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
`curl -s localhost:11434/api/generate -d '{"model":"gemma3:12b","prompt":"hi","stream":false,"keep_alive":"30m"}' >/dev/null` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl -s localhost:11434/api/generate -d '{"model":"gemma3:12b","prompt":"hi","stream":false,"keep_alive":"30m"}' >/dev/null` How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research: librarian-offloaded deep research
Overview
Same shape as the built-in /deep-research — Scope → Search → Fetch → Extract →
3-vote adversarial Verify → Synthesize — but the middle three phases run on the
local librarian (via bin/source-gateway.mjs research: Brave search + fetch +
local Gemma claim extraction). Roughly 15 source documents are distilled to one-line
cited claims before anything reaches Claude. Scope, Verify, and Synthesize stay
on Claude — Verify is the step most likely to expose a small model's reasoning
gap, and it runs over cheap one-line claims, not prose.
If the librarian can't run (Ollama down, no Brave key, sub-tier model, or it returns zero claims) the workflow falls back to the Claude-native WebSearch path so the command never hard-breaks.
When to use
/learning-loop:research "<question>"— a deep, multi-source, fact-checked report when you have a research-capable local model (12b+; chosen at/init).- If the question is underspecified (e.g. "what car to buy" with no budget/use-case/region), ask 2-3 clarifying questions first, then weave the answers into the question you pass.
Prerequisites (the workflow checks these, but know them)
- Ollama running locally with a research-capable model resident (
gemma3:12bdefault; the e2b tier is triage-only and will trip the capability gate). - Brave API key in the macOS Keychain (
service="brave-search-api-key",account=$USER) — same key the Brave MCP uses. - The model and
keep_alivecome fromlibrarian.*config (set at/init). Cold start: the first 12b call adds ~10-40s;keep_alive(default 30m) keeps it warm after. To avoid a cold first call, warm it first:curl -s localhost:11434/api/generate -d '{"model":"gemma3:12b","prompt":"hi","stream":false,"keep_alive":"30m"}' >/dev/null
How to run
First, resolve the plugin root — ${CLAUDE_PLUGIN_ROOT} is set in your main
session but is NOT exported into Workflow subagent shells, so the workflow must be
handed a concrete absolute path. In a Bash block, run:
What ships with it
1 file 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.
- 2d ago First seen · 99 lines · 66 tokens per session scan B fad389596d1b
research is a skill published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,358 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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