Borrowing it
Nothing to install: this file belongs to EmpiricaAI/empirica. 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/EmpiricaAI/empirica/main/AGENTS.mdgit clone --depth 1 https://github.com/EmpiricaAI/empiricaWrote 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/empiricaai/empirica/agents-md)<a href="https://agentmods.dev/instructions/empiricaai/empirica/agents-md"><img src="https://agentmods.dev/badge/instructions/empiricaai/empirica/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/empiricaai/empirica/agents-md"><img src="https://agentmods.dev/badge/instructions/empiricaai/empirica/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 | $0.00513 | $0.00513 |
| Opus 5 | $0.00257 | $0.00257 |
| Sonnet 5 | $0.00103 | $0.00103 |
| Haiku 4.5 | $0.00051 | $0.00051 |
Grade A, and why
empirica 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.
What it actually says
Issue Tracking with bd (beads)
IMPORTANT: This project uses bd (beads) for ALL issue tracking. Do NOT use markdown TODOs, task lists, or other tracking methods, do use Empirica Goals and subtasks.
Why bd?
- Dependency-aware: Track blockers and relationships between issues
- Git-friendly: Auto-syncs to JSONL for version control
- Agent-optimized: JSON output, ready work detection, discovered-from links
- Prevents duplicate tracking systems and confusion
Quick Start
Check for ready work:
bd ready --json
Create new issues:
bd create "Issue title" -t bug|feature|task -p 0-4 --json
bd create "Subtask" --parent <epic-id> --json # Hierarchical subtask
Complete work:
bd update bd-42 --status in_progress --json
bd close bd-42 --reason "Completed" --json
Workflow for AI Agents
- Check ready work:
bd readyshows unblocked issues - Claim your task:
bd update <id> --status in_progress - Work on it: Implement, test, document
- Complete:
bd close <id> --reason "Done" - Commit together: Always commit
.beads/issues.jsonlwith code changes
For full details, see output of bd onboard.
Integration with Empirica
Finding ready work (epistemic + dependency filtering):
empirica goals-ready --session-id <SESSION>
This combines:
- BEADS dependency tracking (what's unblocked)
- Empirica epistemic state (what you're ready for)
- Returns tasks matching your capability
Workflow:
- Check ready work:
empirica goals-ready - Claim task:
bd update <id> --status in_progress - Create branch:
git checkout -b epistemic/reasoning/issue-<id> - Work and commit: Track progress epistemically
- Complete:
bd close <id>+ merge branch
For full details, see docs/integrations/BEADS_GOALS_READY_GUIDE.md
Note: Automatic branch creation via empirica goals-claim coming soon (Phase 3)
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 · 64 lines · 513 tokens per session scan A 7f14ab6590b7
empirica AGENTS.md is an instructions file published in the GitHub repository EmpiricaAI/empirica (247 stars, last pushed today), licensed MIT. It adds 513 tokens to every session, about $0.0026 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.