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/CLAUDE.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/claude-md)<a href="https://agentmods.dev/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/claude-md"><img src="https://agentmods.dev/badge/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/claude-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/claude-md"><img src="https://agentmods.dev/badge/instructions/cisco-foundation-ai/fully-automated-prompt-optimization/claude-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 | $0.00697 | $0.00697 |
| Opus 5 | $0.00349 | $0.00349 |
| Sonnet 5 | $0.00139 | $0.00139 |
| Haiku 4.5 | $0.00070 | $0.00070 |
Grade A, and why
fully-automated-prompt-optimization 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 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 — 53 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
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
Troubleshooting
- If a command fails, hangs, or behavior is unexpected, check auto-memory notes for relevant workaround notes before retrying.
- When the user gives you feedback that may be repeatable in the future (e.g. environment setup steps, workaround patterns, tooling preferences), save it to your auto-memory notes so it persists across sessions.
Evaluation Workflow
- Preferred: use the
eval-runnerslash command for running evaluations and summarizing results.- Slash command:
/project:eval-runner
- Slash command:
- Direct command (when needed):
python -m hephaestus.cli eval --config tenants/<tenant_id>/configs/<config>.json
Code Style
- Follow the project style guide:
docs/style-guide.md - When writing inline code to files in tests (e.g. scorers, chains), use triple-quoted strings (
"""\...""") instead of concatenated string literals ("line1\n" "line2\n").
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 · 53 lines · 697 tokens per session scan A a19ac95311f5
fully-automated-prompt-optimization CLAUDE.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 697 tokens to every session, about $0.0035 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.
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