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 sendralt/agentic-awesome-skills --skill ai-loopgit clone --depth 1 https://github.com/sendralt/agentic-awesome-skillsWrote 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/sendralt/agentic-awesome-skills/ai-loop)<a href="https://agentmods.dev/skills/sendralt/agentic-awesome-skills/ai-loop"><img src="https://agentmods.dev/badge/skills/sendralt/agentic-awesome-skills/ai-loop/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/sendralt/agentic-awesome-skills/ai-loop"><img src="https://agentmods.dev/badge/skills/sendralt/agentic-awesome-skills/ai-loop.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 | $0.00028 | $0.01633 |
| Opus 5 | $0.00014 | $0.00816 |
| Sonnet 5 | $0.00006 | $0.00327 |
| Haiku 4.5 | $0.00003 | $0.00163 |
Grade A, and why
ai-loop 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.
This is a copy
100% identical to ai-loop — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Loop Skill
Overview
The ai-loop skill structures a bounded development cycle for agentic workflows. By dividing the process into distinct planning (Spec), implementation (Build), and validation (Review) phases, it helps an agent build and correct scoped code changes while keeping requirements, risk gates, and stop conditions explicit.
When to Use This Skill
- Use when you need a feature built from scratch or heavily modified, and you want the agent to handle the lifecycle (specification, implementation, and verification) inside one clearly bounded workflow.
- Use when working with isolated components, modules, or features that have well-defined scopes and constraints.
- Use when the user asks for a complete development pass but the work still has clear success criteria, a reasonable verification path, and no unresolved safety or product decisions.
How It Works
This skill executes a controlled development loop composed of three phases: Spec, Build, and Review. When invoked, the agent moves through those phases until the scoped requirements pass verification, a stop condition is reached, or human approval is needed.
Before starting, define:
- The maximum number of build-review iterations.
- The verification commands or manual checks that count as evidence.
- The actions that require explicit approval, such as destructive commands, production changes, external service writes, or broad architectural pivots.
Phase 1: Spec (Planning)
- Interview the user about the feature or app they want to build. Ask one focused question at a time until you fully understand the goal, the must-have requirements, the constraints, and what "done" looks like.
- Do not start building yet.
- When you have enough information, write a clear, detailed specification and save it to
specs/<feature-name>.md. - The spec must include:
- The objective
- The exact requirements
- Edge cases to handle
- A concrete definition of done that someone could check the build against
- The iteration budget, verification commands, and approval gates.
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.
- 5d ago First seen · 137 lines · 28 tokens per session scan A e7fd18a19480
ai-loop is a skill published in the GitHub repository sendralt/agentic-awesome-skills (1 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,633 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-loop, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ai-loop
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.
ai-loop
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.
ai-loop
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.
ai-loop
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.
absolute-work
End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on "absolute work", "build this…
Approval Testing
Approval testing methodology using ApprovalTests library for verifying complex outputs against human-approved results with diff-based review.