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agent-loop-learning: Instructions file for Gemini CLI

GEMINI.md

agent-loop-learning GEMINI.md is an instructions file for Gemini CLI from sarkarsaurabh27/agent-loop-learning. It costs 853 tokens per session, scanned A, original, MIT.

A set of Gemini CLI instructions for reviewing and improving LLM-based agent systems. It points to reference documents about orchestration, prompts, memory, tools, testing, security, and permissions.

In plain words
What is it for?
Use it when reviewing agent architecture, multi-agent workflows, prompts, context and memory, tool design, verification, or security.
Why use it?
It gives the agent a consistent source of repository-specific guidance when answering design or audit questions.

Instructions file for Gemini CLI

Written for Gemini CLI: the file is GEMINI.md. Also seen: mentions CLAUDE.md; mentions AGENTS.md; mentions Gemini CLI.

This is sarkarsaurabh27/agent-loop-learning's own configuration. It tells Gemini CLI 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 853 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/GEMINI.md
Clone the repo
git clone --depth 1 https://github.com/sarkarsaurabh27/agent-loop-learning

Made for: Gemini CLI.

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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Per session 853 This file is loaded in full into every session.
When invoked 853 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 $0.00853 $0.00853
Opus 5.5 $0.00341 $0.00341
Sonnet 5.5 $0.00171 $0.00171
Haiku 4.5 $0.00085 $0.00085

Measured 2mo ago against content hash 047407a1cbd5, 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 GEMINI.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.

GEMINI.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Loop Learning — Gemini CLI Context

This repo is a reference library and skill toolkit for auditing, reviewing, and improving LLM-based agent systems. All best-practice docs are framework-agnostic.

Best-practice docs

All reference material lives in best-practices/. Read the relevant doc(s) before answering any question about agent design or architecture.

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 my agent" / "Audit this design"

  1. 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 any CLAUDE.md, AGENTS.md, GEMINI.md, or README describing the architecture. Read the relevant files. Only ask if no agent code is found.
  2. Read all 9 best-practice docs.
  3. Produce a dimension-by-dimension scorecard: ✅ Solid / ⚠️ Partial / ❌ Gap.
  4. List top 3 prioritized improvements with benchmark justifications.
  5. Call out what's already strong.

Read the full file on GitHub · 52 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 · 52 lines · 853 tokens per session scan A 047407a1cbd5

Subscribe to this mod's changes

agent-loop-learning GEMINI.md is an instructions file published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 5mo ago), licensed MIT. It adds 853 tokens to every session, about $0.0034 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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