Borrowing it
Nothing to install: this file belongs to glassBead-tc/widescreen-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/glassBead-tc/widescreen-research/main/.claude/commands/games/learning-accelerator.mdgit clone --depth 1 https://github.com/glassBead-tc/widescreen-researchWrote 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/commands/glassbead-tc/widescreen-research/learning-accelerator)<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/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/commands/glassbead-tc/widescreen-research/learning-accelerator"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/learning-accelerator.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 | $0.00000 | $0.01082 |
| Opus 5.5 | $0.00000 | $0.00433 |
| Sonnet 5.5 | $0.00000 | $0.00216 |
| Haiku 4.5 | $0.00000 | $0.00108 |
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 1mo 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.
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 Accelerator
Meta-learning framework that optimizes how the entire command system learns and improves over time.
Command Structure
/learning-accelerator "<learning_objective>" [--domain=<domain>] [--strategy=<strategy>] [--measurement=<metrics>] [--iteration=<iter>]
Parameters
learning_objective: What the system should learn to improvedomain: Learning domain (debugging, development, analysis, synthesis)strategy: Learning strategy (pattern-extraction, feedback-loop, meta-cognition, adaptive)measurement: Success metrics (accuracy, efficiency, quality, speed)iteration: Learning iteration cycle length (1h, 1d, 1w, 1m)
Learning Acceleration Strategies
Pattern Extraction
- Identify successful command combinations
- Extract reusable solution patterns
- Discover anti-patterns to avoid
- Build pattern libraries for common scenarios
Feedback Loop Optimization
- Analyze command success/failure patterns
- Optimize decision-making processes
- Improve prediction accuracy
- Accelerate learning cycles
Meta-Cognition Enhancement
- Learn about learning processes
- Optimize knowledge acquisition strategies
- Improve pattern recognition abilities
- Enhance adaptive behaviors
Adaptive Strategy Selection
- Learn when to use different approaches
- Optimize strategy selection based on context
- Adapt to changing problem landscapes
- Improve resource allocation
Learning Domains
Debugging Domain
- Learn effective debugging sequences
- Optimize hypothesis formation
- Improve root cause identification
- Accelerate problem resolution
Development Domain
- Learn successful implementation patterns
- Optimize code generation strategies
- Improve test coverage approaches
- Accelerate development cycles
Analysis Domain
- Learn effective analysis techniques
- Optimize pattern recognition
- Improve insight generation
- Accelerate understanding
Synthesis Domain
- Learn knowledge combination strategies
- Optimize abstraction processes
- Improve pattern synthesis
- Accelerate innovation
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.
- 1mo ago First seen · 214 lines · 0 tokens per session scan A ef12556ee7b9
learning-accelerator is a command published in the GitHub repository glassBead-tc/widescreen-research (6 stars, last pushed 12mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,082 tokens. 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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