Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/nossa-y/activity-frames/agents-mdgit clone --depth 1 https://github.com/nossa-y/activity-framesWhat 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 | $0.00861 | $0.00861 |
| Opus 5 | $0.00430 | $0.00430 |
| Sonnet 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 2d 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.
- 2d 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 (540 stars, last pushed 6d ago), licensed MIT. It adds 861 tokens to every session, about $0.0043 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
neo AGENTS.md
AGENTS.md instructions for neomjs/neo, covering ai agent per-turn operational mandates, §corevalues, §identitypromptfirewall, §criticalgates and §precommitgates.
agentic-context-engine AGENTS.md
Instructions for kayba-ai/agentic-context-engine, covering agents.md, repository guidelines, pipeline-first development (mandatory), core code protection and documentation maintenance.
auv AGENTS.md
Instructions for moeru-ai/auv, covering auv agent guide, project mission, project phase: restore the auv core lane, scope discipline and current contract seam.
awesome-harness-engineering AGENTS.md
AGENTS.md instructions for ai-boost/awesome-harness-engineering, covering agents.md, what this repo is, conventions, what belongs and what doesn't belong.
Meterless AGENTS.md
Instructions for Meterless/Meterless, covering agent instructions for meterless, routing table, isolation rule, repo-wide rules and non-coding agents.
open-index CLAUDE.md
Instructions for DrDroidLab/open-index, covering open index repository guidance and public-safety rules.