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 ToruAI/toru-claude-agents --skill dev-full-autogit clone --depth 1 https://github.com/ToruAI/toru-claude-agentsWrote 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/toruai/toru-claude-agents/dev-full-auto)<a href="https://agentmods.dev/skills/toruai/toru-claude-agents/dev-full-auto"><img src="https://agentmods.dev/badge/skills/toruai/toru-claude-agents/dev-full-auto/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/toruai/toru-claude-agents/dev-full-auto"><img src="https://agentmods.dev/badge/skills/toruai/toru-claude-agents/dev-full-auto.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 | $0.00030 | $0.02560 |
| Opus 5.5 | $0.00012 | $0.01024 |
| Sonnet 5.5 | $0.00006 | $0.00512 |
| Haiku 4.5 | $0.00003 | $0.00256 |
Grade A, and why
dev-full-auto scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "https://api.telegram.org/bot${TELEGRAM_BOT_TOKEN}/sendMessage" \ How it starts
The opening of the file, as written. The whole thing — 408 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dev Full Auto - Autonomous Development
Spec in. Production-ready software out. I make decisions, you review results.
Philosophy: The Ralph Loop
"The best code review is reviewing working software, not plans."
This skill implements the Ralph Loop paradigm - continuous iteration until machine-verifiable completion, not subjective "I think I'm done."
Why Ralph Loop?
Traditional AI assistance fails because:
- Premature exit: AI stops when it feels done, not when it is done
- Context breaks: Restarting loses all progress
- Subjective completion: "Looks good to me" isn't verifiable
Ralph Loop solves this:
- External state as memory: Git commits, test results, file changes persist across iterations
- Machine-verifiable exit: Tests pass? Lint clean? Security audit green? THEN done.
- Continuous iteration: Keep working until objective criteria met
The Core Loop
┌─────────────────────────────────────────────┐
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Build │───▶│ Check │───▶│ Pass? │ │
│ └─────────┘ └─────────┘ └────┬────┘ │
│ ▲ │ │
│ │ No │ │
│ └─────────────────────────────┘ │
│ │ Yes │
│ ┌──────▼────┐ │
│ │ DONE │ │
│ └───────────┘ │
└─────────────────────────────────────────────┘
I don't exit because I think I'm done. I exit when:
- ✓ All tests pass
- ✓ Lint returns 0
- ✓ Build succeeds
- ✓ Security audit clean
- ✓ Docs exist
You give me a spec. I build it. I test it. I secure it. I document it. You come back to software that works.
When to Use
- Clear requirements exist (written or verbal)
- You trust me to make reasonable decisions
- You have time away (hours, not minutes)
- You want working software, not status updates
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 · 408 lines · 30 tokens per session scan A fb291de03097
dev-full-auto is a skill published in the GitHub repository ToruAI/toru-claude-agents (15 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 2,560 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
maintain-formal-verification-skill
Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for…
maintain-verification-skill
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".
create-formal-verification-skill
Generate a project-local skill that formally verifies the code where a bug costs the most and tests reach the least, in any language, with whichever verification tool fits. Use for /create-formal-verification-skill, "model-check this race", "prove this invariant", or when concurrency, retries, state machines, parsers…
tdd
Use only when the user explicitly asks for TDD, a failing test, or a regression test, OR when the bug has an obvious cheap local test target. Skip when the test path is unclear, expensive, integration-heavy, or not requested.
principle-test-behavior-not-implementation
Apply when you write, change, or keep a test. Assert what the code's users can observe, so that the test fails when that behavior breaks and keeps passing when only the internals change.
principle-prove-it-works
Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'.