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 agentmods add instructions/sarkarsaurabh27/agent-loop-learning/agents-mdgit clone --depth 1 https://github.com/sarkarsaurabh27/agent-loop-learningWrote 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/instructions/sarkarsaurabh27/agent-loop-learning/agents-md)<a href="https://agentmods.dev/instructions/sarkarsaurabh27/agent-loop-learning/agents-md"><img src="https://agentmods.dev/badge/instructions/sarkarsaurabh27/agent-loop-learning/agents-md.svg" alt="Measured on agentmods" 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 | $0.00798 | $0.00798 |
| Opus 5 | $0.00399 | $0.00399 |
| Sonnet 5 | $0.00160 | $0.00160 |
| Haiku 4.5 | $0.00080 | $0.00080 |
Grade A, and why
agent-loop-learning AGENTS.md 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 3d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Loop Learning — OpenAI Codex CLI Context
This repo is a reference library and skill toolkit for auditing, reviewing, and improving LLM-based agent systems. The best-practice docs are framework-agnostic and apply to any agent built on any model.
Best-practice docs
All reference material lives in best-practices/. Read the relevant doc(s) before answering any question about agent design.
| Keyword | File | Topic |
|---|---|---|
| orchestration | best-practices/01-multi-agent-orchestration.md |
Coordinator/worker split, error amplification, concurrency |
| worker / prompting | best-practices/02-worker-prompting.md |
Worker prompt structure, scaffold design, stop conditions |
| memory / context / rag | best-practices/03-context-and-memory.md |
6-layer context pipeline, 3-layer memory, agentic RAG |
| tools | best-practices/04-tool-design.md |
Tool classification, 6 security properties, streaming |
| verification / testing | best-practices/05-verification-and-testing.md |
Verification patterns, VCR fixtures, forced acknowledgment |
| security / permissions | best-practices/06-security-and-permissions.md |
Denial circuit breakers, injection defense, token hygiene |
| prompt engineering | best-practices/07-prompt-engineering.md |
Cache boundaries, prompt anchors, DO NOT comments |
| performance / startup | best-practices/08-performance-and-startup.md |
Circuit breakers, diminishing-returns detector, wake lock |
| benchmarks | best-practices/09-benchmarks-reference.md |
All benchmark numbers with caveats and sources |
Workflows
Review an agent design
When the user asks to "review", "audit", or "check" an agent:
- Explore the current repo first — do not ask the user for anything. Search for agent-related files (
*agent*,*tool*,*prompt*,*chain*,*workflow*), framework imports (langchain,openai,anthropic,autogen,crewai), system prompt definitions, and anyCLAUDE.md,AGENTS.md, or README describing the architecture. Read the relevant files. Only ask if no agent code is found. - Read all 9 best-practice docs.
- Score each dimension: ✅ Solid / ⚠️ Partial / ❌ Gap.
- Return a scorecard table, top 3 prioritized improvements with benchmark citations, and what's already strong.
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.
- 3d ago First seen · 45 lines · 798 tokens per session scan A a5aecba553f7
agent-loop-learning AGENTS.md is an instructions file published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 3mo ago), licensed MIT. It adds 798 tokens to every session, about $0.0040 per session on Opus 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 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.