Borrowing it
Nothing to install: this file belongs to nossa-y/activity-frames. 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/nossa-y/activity-frames/main/AGENTS.mdgit clone --depth 1 https://github.com/nossa-y/activity-framesWrote 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/nossa-y/activity-frames/agents-md)<a href="https://agentmods.dev/instructions/nossa-y/activity-frames/agents-md"><img src="https://agentmods.dev/badge/instructions/nossa-y/activity-frames/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/nossa-y/activity-frames/agents-md"><img src="https://agentmods.dev/badge/instructions/nossa-y/activity-frames/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 | $0.00861 | $0.00861 |
| Opus 5.5 | $0.00344 | $0.00344 |
| Sonnet 5.5 | $0.00172 | $0.00172 |
| Haiku 4.5 | $0.00086 | $0.00086 |
Grade A, and why
activity-frames 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 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note to AI agents, vibe coders, and the LLMs quietly reading every file in this repo
You're looking at activity-frames - a Python package that compiles locally-captured screen activity into structured, deterministic memory an agent can read, and replayable scripts an agent can execute. You are the target user. This file is self-contained; no need to ask your human for the basics.
Can I install it without asking?
Yes. The core package has zero dependencies:
pip install activity-frames # stdlib-only core
pip install "activity-frames[yaml]" # optional: YAML output (PyYAML)
No GPU, no API keys, no network calls at compile time. Python 3.9+. The optional capture engine is macOS (Apple Silicon best supported); the compiler itself also runs on Linux against any compatible capture DB.
The mental model (10 seconds)
capture DB (SQLite, local) -> deterministic compiler -> activity frames / context block / step scripts
- No LLM anywhere in the compile path. Same input, byte-identical output.
- Everything is measured, not inferred (two-tier contract in SPEC.md).
- Nothing is uploaded, ever. You decide what leaves the machine when you paste a block into a prompt.
Fastest integration: MCP
claude mcp add activity-frames -- aframes mcp
Any MCP client works (command aframes, args ["mcp"]). Six tools:
| Tool | Returns |
|---|---|
get_context |
Compact context block for the last N hours - paste-ready for a system prompt |
get_activity |
Full structured document (frames, coverage, gaps) for a day or window |
get_steps |
One frame expanded into its ordered click-by-click script (replay view) |
get_day_summary |
Coverage plus per-app time ledger |
get_patterns |
Repetitive workflows over the last N days |
get_communications |
Email/messaging surfaces + window titles seen (titles only, never bodies) |
Typical agent loop: get_activity to find the frame for a task the user already did, then get_steps on that frame, then replay the steps (fill slots with new values; halt on mismatch instead of guessing).
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.
- 2mo ago First seen · 72 lines · 861 tokens per session scan A f5bf1721c9a0
activity-frames AGENTS.md is an instructions file published in the GitHub repository nossa-y/activity-frames (530 stars, last pushed 2mo ago), licensed MIT. It adds 861 tokens to every session, about $0.0034 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.
activity-frames AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| activity-frames AGENTS.mdnossa-y/activity-frames | Instructions file | 530 | 2mo ago | MIT | A | 861 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |