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agent-loop-learning: Instructions file for Claude Code

CLAUDE.md

agent-loop-learning CLAUDE.md is an instructions file for Claude Code from sarkarsaurabh27/agent-loop-learning. It costs 507 tokens per session, scanned A, original, MIT.

A project guide and reference library for reviewing and improving AI agents. It contains learning documents about how agents coordinate work, use prompts, retain context, call tools, verify results, handle security, and manage performance.

In plain words
What is it for?
Use it to audit an agent, learn about a specific design topic, or work through improvements. Its included commands load best-practice documents, review an agent, and suggest targeted changes.
Why use it?
It gives developers a common place to learn agent design practices and inspect an agent against them. The documents include benchmark figures and sources for comparing design choices.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: reads .claude/ paths; mentions Claude Code.

This is sarkarsaurabh27/agent-loop-learning's own configuration. It tells Claude Code how to work on agent-loop-learning itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agent-loop-learning configures →

not rated 3repo 5mo ago A scan 507 tokens original MIT
View source ↗ sarkarsaurabh27/agent-loop-learning
Reuse

Borrowing it

Nothing to install: this file belongs to sarkarsaurabh27/agent-loop-learning. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/sarkarsaurabh27/agent-loop-learning/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/sarkarsaurabh27/agent-loop-learning

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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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.

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<a href="https://agentmods.dev/instructions/sarkarsaurabh27/agent-loop-learning/claude-md"><img src="https://agentmods.dev/badge/instructions/sarkarsaurabh27/agent-loop-learning/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 507 This file is loaded in full into every session.
When invoked 507 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00507 $0.00507
Opus 5.5 $0.00203 $0.00203
Sonnet 5.5 $0.00101 $0.00101
Haiku 4.5 $0.00051 $0.00051

Measured 2mo ago against content hash 0057b2dde05d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-loop-learning CLAUDE.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 2mo 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.

CLAUDE.md · 32 lines

How it starts

The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Loop Learning — Project Context

This repo is a reference library and skill toolkit for auditing, reviewing, and improving LLM-based agent systems.

What's here

  • best-practices/ — 9 curated documents covering orchestration, prompting, memory, tools, verification, security, performance, and benchmarks. Each doc includes benchmark numbers with primary sources.
  • .claude/commands/ — Slash-command skills for local Claude Code agents:
    • /review-agent — Audit any agent design against all best-practice docs
    • /improve-agent — Targeted improvement recommendations for a specific agent component
    • /best-practices — Load a specific best-practice doc by topic keyword

How to use this repo

  1. As an audit: open a design doc, run /review-agent to get a structured gap analysis
  2. As a learning loop: run /improve-agent <component> to get concrete, benchmark-backed improvement suggestions
  3. As a reference: run /best-practices <topic> to pull the relevant doc into context

Best practices index

# File Topic
01 best-practices/01-multi-agent-orchestration.md Coordinator/worker split, four-phase model, concurrency
02 best-practices/02-worker-prompting.md Worker prompt structure, scaffold design, stop conditions
03 best-practices/03-context-and-memory.md 6-layer context pipeline, 3-layer memory, RAG patterns
04 best-practices/04-tool-design.md Tool classification, security properties, streaming
05 best-practices/05-verification-and-testing.md Verification patterns, VCR fixtures, forced acknowledgment
06 best-practices/06-security-and-permissions.md Denial circuit breakers, injection defense, token hygiene
07 best-practices/07-prompt-engineering.md Cache boundaries, prompt anchors, system prompt structure
08 best-practices/08-performance-and-startup.md Circuit breakers, diminishing-returns detector, wake lock
09 best-practices/09-benchmarks-reference.md All benchmark numbers with caveats and sources

Read the full file on GitHub · 32 lines

Changes

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.

  1. 2mo ago First seen · 32 lines · 507 tokens per session scan A 0057b2dde05d

Subscribe to this mod's changes

agent-loop-learning CLAUDE.md is an instructions file published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 5mo ago), licensed MIT. It adds 507 tokens to every session, about $0.0020 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 2026-08-31.

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