Acontext is an open-source memory layer for AI agents that records useful knowledge from agent runs as editable skill files. Agent builders use it to preserve, inspect, share, and reuse what agents learn across frameworks, and the catalogue includes hooks, skills, instructions, rules, an MCP, and a plugin for working with it.
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 memodb-io/Acontext --skill daily-logsgit clone --depth 1 https://github.com/memodb-io/AcontextWrote 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/memodb-io/acontext/daily-logs)<a href="https://agentmods.dev/skills/memodb-io/acontext/daily-logs"><img src="https://agentmods.dev/badge/skills/memodb-io/acontext/daily-logs.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00228 |
| Opus 5 | $0.00011 | $0.00114 |
| Sonnet 5 | $0.00004 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
daily-logs 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 9d 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
Daily Logs
Record the user's daily activities, progress, decisions, and learnings in a structured, chronological format.
File Structure
Each day has its own file named yyyy-mm-dd.md (e.g., 2025-06-15.md). Create a new file for each new day; append entries to the existing file if one already exists for today.
File Format: yyyy-mm-dd.md
content format, for example:
# yyyy-mm-dd
## [short description]
- [1-3 sentence summary of what happened]
Guidelines
- One file per day, multiple entries per file (one per task)
- Use ISO date format:
yyyy-mm-dd - Keep entries concise — focus on what matters for future reference
- Do not duplicate information already captured in other skills
- Always refer to the user in third person ("The user requested X", "The user decided Y"), never use first-person pronouns
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.
- 9d ago First seen · 29 lines · 22 tokens per session scan A f65f1f09bf08
daily-logs is a skill published in the GitHub repository memodb-io/Acontext (3,687 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 228 once invoked, about $0.0001 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
02-project-memory
Build the project's memory of its architecture, conventions, and decisions, and wire it into your AI tools. Use when the user wants to set up or refresh project memory, or rewire it into a tool. Not for editing one existing memory file.
10-learn
Capture durable project learnings. Use when the user wants to remember, record, or formalize a decision, convention, lesson, pitfall, reusable workflow, or review finding. Not for preferences or temporary notes.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
knowledge-base
Create and maintain a Markdown knowledge base that any AI agent can read, search, and update. Use when the user wants to start a knowledge base, add or update notes, organize docs/notes for an agent or LLM to consume, build an index of notes, or run a cleanup/maintenance pass on an existing MD knowledge base. Triggers…
task-agent-eliza-bridge
Use when spawning a Claude Code, Codex, Gemini, Aider, or other CLI task agent whose work needs parent Eliza runtime context. Covers the read-only loopback bridge for character, room, memory, and active workspace state.
aatmf-t04-memory-manipulation
AATMF T4 — Multi-Turn & Memory Manipulation. Persistent memory injection, conversation-state poisoning, cross-session contamination, ghost-context leak.