Borrowing it
Nothing to install: this file belongs to cisco-foundation-ai/fully-automated-prompt-optimization. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cisco-foundation-ai/fully-automated-prompt-optimization/main/AGENTS.mdgit clone --depth 1 https://github.com/cisco-foundation-ai/fully-automated-prompt-optimizationWrote 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/cisco-foundation-ai/fully-automated-prompt-optimization/agents-md)<a href="https://agentmods.dev/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/agents-md"><img src="https://agentmods.dev/badge/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/agents-md/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.
<a href="https://agentmods.dev/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/agents-md"><img src="https://agentmods.dev/badge/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00788 | $0.00788 |
| Opus 5 | $0.00394 | $0.00394 |
| Sonnet 5 | $0.00158 | $0.00158 |
| Haiku 4.5 | $0.00079 | $0.00079 |
Grade A, and why
fully-automated-prompt-optimization 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 10d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
Repository Purpose
FAPO (Fully Autonomous Prompt Optimization) is an LLM chain optimization framework. It provides structured tooling for iteratively improving LLM-powered pipelines through evaluation, failure analysis, and prompt/chain iteration.
The repo separates reusable optimization and evaluation core logic from tenant-specific prompts, datasets, and historical artifacts.
Project Structure
src/hephaestus/- core optimization engine, evaluation runner, and provider interfaceshephaestus/- public package shim forpython -m hephaestus.clitenants/<tenant_id>/- tenant-specific prompts, datasets, source artifacts, local eval outputs, and tenant docsdocs/- product-level architecture, usage docs, and process documentationtests/- automated tests for core modules.codex/- Codex workflow prompts that replace the previous Claude Code slash-command assets
Build, Test, and Development
python -m venv .venv && source .venv/bin/activate && pip install --upgrade pippython -m pip install -e .python -m pytestpython -m hephaestus.cli --help
Codex Workflows
Codex does not use Claude Code slash commands directly. When a user asks for a FAPO workflow, follow the local prompt files under .codex/:
- Optimization loop:
.codex/agents/optimization.md - Eval runner:
.codex/commands/eval-runner.md - Synthetic samples:
.codex/commands/synthetic-samples.md - Synthetic pruner:
.codex/commands/synthetic-pruner.md - Reset tenant:
.codex/commands/reset-tenant.md - Internal failure attribution phase:
.codex/agents/step-attribution.md - Internal variant review phase:
.codex/agents/variant-reviewer.md
For repeated autonomous optimization rounds, use:
scripts/optimize-loop-codex.sh --tenant <tenant_id> --config tenants/<tenant_id>/configs/<config>.json
Evaluation Workflow
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.
- 10d ago First seen · 73 lines · 788 tokens per session scan A 40f0c239f52c
fully-automated-prompt-optimization AGENTS.md is an instructions file published in the GitHub repository cisco-foundation-ai/fully-automated-prompt-optimization (107 stars, last pushed yesterday), licensed Apache-2.0. It adds 788 tokens to every session, about $0.0039 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 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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.
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).
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).
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.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.