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 skills add Kevin-Liu-01/Agent-Machines --skill agent-activity-loggit clone --depth 1 https://github.com/Kevin-Liu-01/Agent-MachinesWrote 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/skills/kevin-liu-01/agent-machines/agent-activity-log)<a href="https://agentmods.dev/skills/kevin-liu-01/agent-machines/agent-activity-log"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/agent-activity-log/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/skills/kevin-liu-01/agent-machines/agent-activity-log"><img src="https://agentmods.dev/badge/skills/kevin-liu-01/agent-machines/agent-activity-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00099 | $0.01248 |
| Opus 5 | $0.00049 | $0.00624 |
| Sonnet 5 | $0.00020 | $0.00250 |
| Haiku 4.5 | $0.00010 | $0.00125 |
Grade A, and why
agent-activity-log 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Activity Log
Persistent agent memory for any project. Initializes a memory.md file and enforces
the read-before-act, write-after-change discipline that makes multi-session agent work
compound instead of reset.
See wiki/concepts/agent-activity-log.md for the full conceptual framework.
When to Use
- Setting up a new project for AI-assisted development
- Starting a session in a project that already has
memory.md - User asks "what did we do last time" or "what's the project history"
- User says "add agent memory", "set up logging", or "track changes"
- Any project where agents will work across multiple sessions
Bootstrap Protocol
When initializing memory.md in a new project:
1. Check if memory.md already exists
ls memory.md 2>/dev/null || echo "No memory.md found"
If it exists, read it and summarize recent activity to the user. Do not overwrite.
2. Create memory.md
# Agent Activity Log
> Persistent memory for AI agents working on this project. Agents must read this
> file at session start and append after every meaningful change.
## Convention
- **Read first.** Before making changes, read this file to understand project history.
- **Append after changes.** Log decisions, state transitions, and open threads.
- **Never delete.** This file is append-only.
- **Skip noise.** Don't log formatting fixes, import reordering, or trivial typos.
---
3. Add the read/write constraint to project rules
Append to the project's .cursorrules, AGENTS.md, or .cursor/rules/ (whichever
the project uses for agent configuration):
## Agent Memory
This project maintains a persistent agent activity log at `memory.md`.
**On every session start:** read `memory.md` to understand what has been done,
what decisions were made, and what open threads exist.
**After every meaningful change:** append an entry to `memory.md` with the format:
## [YYYY-MM-DD HH:MM] action-type | Title
**Changed:** files touched
**Why:** rationale
**Decision:** what was chosen and why (if applicable)
**Open:** unfinished items or follow-ups (if any)
Log decisions, state transitions, bug fixes, and new features. Skip formatting
fixes, import reordering, and trivial typos.
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.
- 12d ago First seen · 160 lines · 99 tokens per session scan A deecb3712195
agent-activity-log is a skill published in the GitHub repository Kevin-Liu-01/Agent-Machines (29 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 1,248 once invoked, about $0.0005 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 skills, from other repositories
21-day-self-interview
A 21-night guided self-reflection routine in which an agent asks three questions each night, remembers the answers, and reflects on them at key points.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
clawdi
API keys, tokens, memory, sessions, Projects, integrations. Use Clawdi Cloud when a task needs passwords or safe credential storage/provision, missing user memory or Project/Vault context, past conversations, Clawdi share URLs, or connected-service fallback such as Gmail, GitHub, Notion, Drive, or Calendar. Do not…
hermes-mnemosyne
Mnemosyne is Hermes' primary local-first memory engine — SQLite with vector + FTS5 hybrid search, 19+ tools, auto-consolidation, and a standalone CLI. It's a pip-installed plugin (not a built-in toolset) discovered via $HERMESHOME/plugins/mnemosyne/.
cortex-navigate-knowledge
Navigate the knowledge graph — trace entity relationships, explore causal chains, drill into memory clusters, and traverse co-access paths. Use when the user asks 'how are these related', 'what connects X to Y', 'show me the knowledge graph', 'trace the relationship', 'what caused X', 'drill down into', 'explore…
cortex-remember-global
Store a global memory that is visible across all projects. Use when the user shares architecture rules, coding conventions, infrastructure facts, security policies, team agreements, or any knowledge that applies beyond a single project. Triggers on 'remember this everywhere', 'this applies to all projects', 'global…