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agent-loop-learning: Instructions file for GitHub Copilot

.github/copilot-instructions.md

agent-loop-learning copilot-instructions.md is an instructions file for GitHub Copilot from sarkarsaurabh27/agent-loop-learning. It costs 675 tokens per session, scanned A, original, MIT.

A set of instructions for reviewing and improving AI-agent systems. It connects common questions about prompts, memory, tools, testing, security, and performance to relevant best-practice documents.

In plain words
What is it for?
Use it when designing agent architecture, writing prompts, managing context or retrieval, creating tools, checking security, or planning tests and verification.
Why use it?
It reduces the need to search through a reference library before answering technical questions about agents. It also gives reviews a consistent workflow.

Instructions file for GitHub Copilot

Written for GitHub Copilot: a Copilot instructions file. Also seen: mentions CLAUDE.md; mentions AGENTS.md.

This is sarkarsaurabh27/agent-loop-learning's own configuration. It tells GitHub Copilot 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 675 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/.github/copilot-instructions.md
Clone the repo
git clone --depth 1 https://github.com/sarkarsaurabh27/agent-loop-learning

Made for: GitHub Copilot.

Wrote this? Show the measurements

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README.md
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<a href="https://agentmods.dev/instructions/sarkarsaurabh27/agent-loop-learning/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/sarkarsaurabh27/agent-loop-learning/copilot-instructions/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.

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Your own site · 80×15
<a href="https://agentmods.dev/instructions/sarkarsaurabh27/agent-loop-learning/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/sarkarsaurabh27/agent-loop-learning/copilot-instructions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 675 This file is loaded in full into every session.
When invoked 675 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 $0.00675 $0.00675
Opus 5.5 $0.00270 $0.00270
Sonnet 5.5 $0.00135 $0.00135
Haiku 4.5 $0.00068 $0.00068

Measured 2mo ago against content hash fdd0aa4f353b, 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 copilot-instructions.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.

.github/copilot-instructions.md · 50 lines

How it starts

The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Copilot Instructions — Agent Loop Learning

This repo is a reference library for auditing, reviewing, and improving LLM-based agent systems. The best-practice docs in best-practices/ are framework-agnostic and apply to any agent on any model.

When to use the best-practice docs

Always read the relevant doc before answering questions about:

  • Agent architecture or design patterns
  • Prompt structure, system prompts, or worker prompts
  • Memory, context windows, or RAG pipelines
  • Tool design or function calling
  • Testing or verification strategies
  • Security, permissions, or prompt injection
  • Performance, latency, or startup behavior

Doc → topic mapping

If user asks about... Read this file
Multi-agent systems, orchestration, coordinator/worker best-practices/01-multi-agent-orchestration.md
Worker prompts, scaffolding, stop conditions best-practices/02-worker-prompting.md
Memory, context management, RAG best-practices/03-context-and-memory.md
Tool design, function calling best-practices/04-tool-design.md
Testing, verification, self-reflection best-practices/05-verification-and-testing.md
Security, injection, permissions best-practices/06-security-and-permissions.md
Prompt engineering, cache, system prompts best-practices/07-prompt-engineering.md
Performance, startup, circuit breakers best-practices/08-performance-and-startup.md
Benchmark numbers, citations best-practices/09-benchmarks-reference.md

Review workflow

When asked to review or audit an agent 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, or README describing the architecture. Read the relevant files. Only ask if no agent code is found.
  2. Read all 9 docs.
  3. Score each dimension: ✅ Solid / ⚠️ Partial / ❌ Gap.
  4. Return scorecard, top 3 prioritized improvements (with benchmark citations), and callouts for what's strong.

Read the full file on GitHub · 50 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 · 50 lines · 675 tokens per session scan A fdd0aa4f353b

Subscribe to this mod's changes

agent-loop-learning copilot-instructions.md is an instructions file published in the GitHub repository sarkarsaurabh27/agent-loop-learning (3 stars, last pushed 5mo ago), licensed MIT. It adds 675 tokens to every session, about $0.0027 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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