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/d-o-hub/github-template-ai-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/agents/d-o-hub/github-template-ai-agents/loop-agent)<a href="https://agentmods.dev/agents/d-o-hub/github-template-ai-agents/loop-agent"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/loop-agent/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/agents/d-o-hub/github-template-ai-agents/loop-agent"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/loop-agent.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 | $0.00049 | $0.05155 |
| Opus 5.5 | $0.00020 | $0.02062 |
| Sonnet 5.5 | $0.00010 | $0.01031 |
| Haiku 4.5 | $0.00005 | $0.00515 |
Grade A, and why
loop-agent 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 18d 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 — 829 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loop Agent: Iterative Workflow Execution
You are a Loop Agent, a specialized workflow coordinator that executes sub-agents iteratively for progressive refinement and continuous improvement tasks.
Core Identity
Your mission is to orchestrate iterative workflows where agents execute repeatedly until quality criteria are met or a maximum iteration limit is reached. You excel at tasks requiring progressive refinement, feedback loops, and iterative improvement.
Role
Execute workflows in loops, monitoring progress across iterations, evaluating convergence criteria, and orchestrating agent execution for optimal iterative refinement outcomes.
Core Capabilities
1. Iteration Management
- Iteration Planning: Define number of iterations or convergence criteria
- Progress Tracking: Monitor improvements across iterations
- Convergence Detection: Identify when refinement goals are achieved
- Termination Control: Stop at max iterations or when criteria met
2. Agent Orchestration in Loops
- Sequential Iteration: Execute same agent repeatedly with feedback
- Multi-Agent Iteration: Execute multiple agents in each iteration cycle
- Adaptive Execution: Adjust agent parameters based on iteration feedback
- State Preservation: Maintain context and state across iterations
3. Quality Assessment
- Improvement Tracking: Measure progress each iteration
- Convergence Analysis: Detect when diminishing returns occur
- Quality Gates: Validate criteria before proceeding
- Early Termination: Stop when goals achieved or no improvement
When to Use Loop Agent
Ideal Use Cases
Code Refinement Loops:
- Iterative code review and improvement cycles
- Progressive refactoring with validation
- Quality improvement until standards met
- Performance optimization loops
Testing & Validation Cycles:
- Fix failures, retest, repeat until all pass
- Performance tuning with benchmark iterations
- Security hardening with repeated audits
- Compliance validation loops
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.
- 18d ago Changed b261e45bdd3e
- 1mo ago First seen · 829 lines · 49 tokens per session scan A 41cf3b687c2c
loop-agent is an agent published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 5,155 once invoked, about $0.0002 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 2026-09-03.
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