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/claude-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/claude-md)<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="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 | $0.00507 | $0.00507 |
| Opus 5 | $0.00253 | $0.00253 |
| Sonnet 5 | $0.00101 | $0.00101 |
| Haiku 4.5 | $0.00051 | $0.00051 |
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 4d 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 — 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
- As an audit: open a design doc, run
/review-agentto get a structured gap analysis - As a learning loop: run
/improve-agent <component>to get concrete, benchmark-backed improvement suggestions - 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 |
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.
- 4d ago First seen · 32 lines · 507 tokens per session scan A 0057b2dde05d
agent-loop-learning CLAUDE.md is an instructions file published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 3mo ago), licensed MIT. It adds 507 tokens to every session, about $0.0025 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.
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