Borrowing it
Nothing to install: this file belongs to responsibleai/ASSERT. 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/responsibleai/ASSERT/main/AGENTS.mdgit clone --depth 1 https://github.com/responsibleai/ASSERTWrote 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/responsibleai/assert/agents-md)<a href="https://agentmods.dev/instructions/responsibleai/assert/agents-md"><img src="https://agentmods.dev/badge/instructions/responsibleai/assert/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/responsibleai/assert/agents-md"><img src="https://agentmods.dev/badge/instructions/responsibleai/assert/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 | $0.05349 | $0.05349 |
| Opus 5.5 | $0.02140 | $0.02140 |
| Sonnet 5.5 | $0.01070 | $0.01070 |
| Haiku 4.5 | $0.00535 | $0.00535 |
Grade A, and why
ASSERT 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 1mo 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 — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptive Eval Agent Orientation
This file is for coding assistants such as GitHub Copilot, Claude Code, Cursor, and similar tools. It gives a short, customer-safe map of this preview repository.
What this repo is
Adaptive Eval is a local-first, spec-driven evaluation harness for AI agents. A developer writes an eval spec, the pipeline generates targeted test cases, runs them against a target, and judges the resulting inference outputs (conversations or agent actions) against the spec.
Use this mental model:
eval spec -> behavior categories -> test cases -> execute target -> judge -> artifacts
Safety and privacy rules
- Never read, print, commit, summarize, or infer values from
.envor other local environment files. - Use placeholder names such as
AZURE_API_KEY,AZURE_API_BASE,azure_ad_token, andazure_ad_token_provider; never invent or expose credential values. - Do not recommend committing generated artifacts, local traces,
.venv, logs, or.envfiles. - Treat this repo as a customer-preview distribution. Keep contributions customer-safe — avoid any internal-only planning, prioritization, or organizational content.
Authoritative files
Start with these files:
README.md- customer-facing overview and quickstart.docs/quickstart.md- LangGraph travel planner walkthrough.docs/targets/README.md- target decision tree (rendered by default when browsingdocs/targets/).docs/targets/callable.md- Python callable target for any agent or multi-agent system, with OpenTelemetry trace capture as the recommended integration path.docs/targets/model-and-tools.md- Prompt Agent target (hosted model + system prompt + optional tool schema; runtime owns the tool-call loop).docs/config/schema.md- current YAML schema reference.examples/README.md- example selection guide.
Current preview terminology
Use the developer-friendly behaviors in prose, and mention current YAML keys when needed.
| Behavior to explain | Current YAML / artifact |
|---|---|
| Eval spec | behavior.name, behavior.description in eval_config.yaml |
| Target description | context |
| Variations | dimensions |
| Behavior categories | pipeline.systematize, taxonomy.json |
| Test cases | pipeline.test_set, test_set.jsonl |
| Execute | pipeline.inference, inference_set.jsonl |
| Target | pipeline.inference.target |
| Trace capture | target.trace |
| Judge | pipeline.judge, scores.jsonl |
| Metrics | metrics.json |
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.
- 1mo ago Changed · +4 lines · +189 tokens per session bc3ff946d39c
- 1mo ago First seen · 327 lines · 5,160 tokens per session scan A 933c6ecfdfe7
ASSERT AGENTS.md is an instructions file published in the GitHub repository responsibleai/ASSERT (352 stars, last pushed 4d ago), licensed MIT. It adds 5,349 tokens to every session, about $0.0214 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 2026-08-30.
Other instructions, from other repositories
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).
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.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
vscode model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.