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.
git clone --depth 1 https://github.com/XeldarAlz/everything-claude-unityWrote 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/xeldaralz/everything-claude-unity/unity-learn)<a href="https://agentmods.dev/commands/xeldaralz/everything-claude-unity/unity-learn"><img src="https://agentmods.dev/badge/commands/xeldaralz/everything-claude-unity/unity-learn/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/xeldaralz/everything-claude-unity/unity-learn"><img src="https://agentmods.dev/badge/commands/xeldaralz/everything-claude-unity/unity-learn.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 | $0.00022 | $0.01434 |
| Opus 5.5 | $0.00009 | $0.00574 |
| Sonnet 5.5 | $0.00004 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
unity-learn 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/unity-learn — Learning Pipeline
Manage and leverage accumulated session learnings: $ARGUMENTS
This command works with the data collected by the auto-learn.sh hook (strict profile) which records session patterns to .claude/state/learnings.jsonl after each session. For pre-v1.3.0 projects, the file may be at .claude/learnings.jsonl instead.
Subcommands
review (default)
Read .claude/state/learnings.jsonl and present a dashboard summarizing accumulated data:
- Read the learnings file at
.claude/state/learnings.jsonl(or.claude/learnings.jsonlas fallback for pre-v1.3.0 projects) - Aggregate and present:
## Session Learning Dashboard
**Total sessions:** [count]
**Date range:** [earliest] to [latest]
**Total duration:** [hours]h [minutes]m
### File Activity
| Category | Total Edits | Sessions |
|----------|-------------|----------|
| Models | [count] | [count] |
| Views | [count] | [count] |
| Systems | [count] | [count] |
| Tests | [count] | [count] |
| Shaders | [count] | [count] |
| Editor | [count] | [count] |
### Session Categories
| Category | Count | Avg Duration |
|-------------|-------|--------------|
| bug-fix | [n] | [m]m |
| performance | [n] | [m]m |
| architecture| [n] | [m]m |
| workflow | [n] | [m]m |
| integration | [n] | [m]m |
### Tool Usage
| Tool | Total Calls | Avg per Session |
|-------|-------------|-----------------|
| Edit | [count] | [avg] |
| Read | [count] | [avg] |
| Bash | [count] | [avg] |
| ... | ... | ... |
extract
Analyze the learnings log for recurring patterns and apply confidence scoring:
- Read all entries from
.claude/state/learnings.jsonl(or.claude/learnings.jsonlas fallback) - Group by category (bug-fix, performance, architecture, workflow, integration)
- Identify recurring patterns:
- Files that appear across multiple sessions → likely hotspots
- Categories that dominate → project's current focus area
- Tool usage patterns → workflow optimization opportunities
- MVS balance → are Models, Views, and Systems equally maintained?
- Apply confidence scoring:
- High confidence (3+ sessions): Pattern is well-established, likely a real project convention
- Medium confidence (2 sessions): Pattern is emerging, worth noting but may be coincidental
- Low confidence (1 session): Single observation, keep but don't act on yet
- Present findings:
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 · 167 lines · 22 tokens per session scan A 7ec4b1791d32
unity-learn is a command published in the GitHub repository XeldarAlz/everything-claude-unity (25 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 1,434 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other commands, from other repositories
story-continue
Resume an interrupted story from where you left off.
nds-test
Read the knowledge base BEFORE any MCP tool call.
gba-ai-full
Read docs/gba-claude-guide.md from C:\Users\AustinKidwell\ASR Dropbox\Austin Kidwell\04ResearchAnalysis\pokefirered-master\pokefirered-master completely before any MCP tool call.
gba-smoke
These run BEFORE game detection. If any fail, stop immediately.
gba-build-develop
Composes /gba-build develop, /gba-ai-full, /research-perplexity, /creative-research, and /orchestrator-multi into a single autonomous mega-pipeline that researches, plans, parallelizes implementation, AI-playtests, iterates on failures, discovers creative features, and ships production-ready GBA games.
nds-build
Command "nds-build" from intellegix/intellegix-code-agent-toolkit, covering /nds-build — universal nds game builder, usage, subcommands, new — scaffold a new nds project and develop — research-driven modular game build.