What is learning-accelerator?

A command for improving how a command system learns from past work, using patterns, feedback, and repeated learning cycles.

Use it to set a learning objective for areas such as debugging, development, analysis, or synthesis, with a chosen strategy, measurements, and iteration period.

It gives a structured way to examine what worked or failed instead of relying only on ad-hoc adjustments.

learning-accelerator — Command for Claude Code by glassBead-tc: A command for improving how a command system learns…

Install

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.

agentmods

npx agentmods add commands/glassbead-tc/widescreen-research/learning-accelerator

Clone the repo

git clone --depth 1 https://github.com/glassBead-tc/widescreen-research

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/learning-accelerator.svg)](https://agentmods.dev/commands/glassbead-tc/widescreen-research/learning-accelerator)

Your own site

<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/learning-accelerator"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/learning-accelerator.svg" alt="Measured on agentmods" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,082 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00000	$0.01082
Opus 5	$0.00000	$0.00541
Sonnet 5	$0.00000	$0.00216
Haiku 4.5	$0.00000	$0.00108

Measured 5d ago against content hash ef12556ee7b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learning-accelerator 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.

.claude/commands/games/learning-accelerator.md · 214 lines

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.