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 skills add closedloop-ai/claude-plugins --skill learning-qualitygit clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote 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/closedloop-ai/claude-plugins/learning-quality)<a href="https://agentmods.dev/skills/closedloop-ai/claude-plugins/learning-quality"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/learning-quality/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/closedloop-ai/claude-plugins/learning-quality"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/learning-quality.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00015 | $0.01653 |
| Opus 5 | $0.00008 | $0.00826 |
| Sonnet 5 | $0.00003 | $0.00331 |
| Haiku 4.5 | $0.00002 | $0.00165 |
Grade A, and why
learning-quality 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Quality Skill
This skill defines when and how to capture learnings during ClosedLoop runs.
Decision Tree: Should I Capture This?
Before writing a learning, run through this decision tree in order:
1. Did I make a mistake and correct it, or discover something non-obvious?
NO → Don't capture (no learnings event)
YES → Continue
2. Is it a config value? (specific URL, file path, project command, type name)
YES → Write to CLAUDE.md (project scope), not org-patterns
NO → Continue
3. Is it tied to a single feature/bug with no generalizable principle?
YES → SKIP
NO → Continue
4. Will it still be true in 6 months?
NO → SKIP (or generalize the principle)
YES → Continue
5. Does it already exist in org-patterns.toon or CLAUDE.md?
YES → SKIP (or note "Supersedes: [old pattern]" if correcting)
NO → CAPTURE IT
Note: Even "basic" knowledge is worth capturing if you actually made that mistake. These learnings exist because LLM agents struggle with certain patterns that humans might consider obvious. The goal is to help future agent runs avoid the same mistakes.
Hard Rejection Criteria
SKIP if ANY of these apply:
| Criterion | Example | Why |
|---|---|---|
| Specific URL/path/config | "Use https://github.com/org/repo" | Config, not principle → CLAUDE.md |
| Project-specific names | "Use MyProjectType not OtherType" | Belongs in CLAUDE.md |
| One-off bug fix | "Field X was null in row 123" | Not reusable |
| Already captured | (check pending/, CLAUDE.md, org-patterns.toon) | Avoid duplicates |
Note: Even patterns that seem like "basic knowledge" are worth capturing if you actually made that mistake. These learnings exist because LLM agents struggle with certain patterns. The goal is to help future agent runs avoid the same mistakes.
Capture Workflow
When you have a learning worth capturing:
Step 1: Classify Scope
| Scope | Destination | Heuristic |
|---|---|---|
| Project | CLAUDE.md | Mentions specific file paths, package names, or project-unique features |
| Global | org-patterns.toon | Applies to any project using the same language/framework/tool |
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.
- yesterday First seen · 214 lines · 15 tokens per session scan A 8de661bb652d
learning-quality is a skill published in the GitHub repository closedloop-ai/claude-plugins (103 stars, last pushed 2d ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,653 once invoked, about $0.0001 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 2026-09-07.
Other skills, from other repositories
pr-reviewer
Reviews a diff or security scope read-only using evidence-tiered findings, structural and context-error rubrics, and repository review policy. Use when asked to "review my changes", "structural review", "review for AI patterns", or "security audit". For applying fixes use tidy; for UI defects use ui-design.
autoship
Runs a changesets npm release through the version PR, CI publish, and registry verification. Use when asked to "release this package", "autoship", "merge Version Packages", or diagnose a release that did not publish. For feature PRs use pr-creator or pr-babysitter.
scaffold-cli
Scaffolds a TypeScript CLI and npm package with the house toolchain, dual tsdown outputs, CLI contracts, changesets, and publishing templates. Use when asked to "scaffold a CLI" or "start an npm package". For an existing package release use autoship; for existing API ergonomics use dx-audit.
claw-mux
Control cmux terminal topology and I/O — send commands to panes, read output, split layouts, monitor logs, orchestrate multi-pane workflows. Requires cmux environment.
report-manager
Manage and refine vision-powers reports: list, open, delete, search, and refine sections. Use when asked to list, open, delete, search, or update generated HTML reports.
fetch-sitemap
Extract URLs from an XML sitemap with optional regex filtering.