agentmods.dev

Acontext AGENTS.md

A navigation and development guide for Acontext, a system that gives production AI agents reusable skills and memory. It maps the repository's Python and TypeScript software development kits, command-line tool, server, dashboard, packages, tests, and documentation.

In plain words
What is it for?
Use it when modifying Acontext's clients, command-line tool, server, dashboard, sandbox package, tests, landing page, or documentation.
Why use it?
It shows where each part of the system lives and requires a written plan before implementation. This makes changes easier to locate and keeps development work organized.

Instructions file for CodexOpenCode

3.7k 1mo ago A scan 2,445 tokens original Apache-2.0
View the source file memodb-io/Acontext
Install

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.

agentmods
npx agentmods add instructions/memodb-io/acontext/agents-md
Clone the repo
git clone --depth 1 https://github.com/memodb-io/Acontext

Made for: Codex, OpenCode.

Per session 2,445 This file is loaded in full into every session.
When invoked 2,445 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.02445 $0.02445
Opus 5 $0.01222 $0.01222
Sonnet 5 $0.00489 $0.00489
Haiku 4.5 $0.00245 $0.00245

Measured 3d ago against content hash 77be58c9dacb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Acontext 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 3d 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.

AGENTS.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Acontext

Acontext is Agent Skills as a Memory Layer for production AI Agents

  • Python SDK: src/client/acontext-py/
  • TypeScript SDK: src/client/acontext-ts/
  • CLI Tool: src/client/acontext-cli/
  • API: src/server/api/
  • CORE: src/server/core/
  • Dashboard: dashboard/
  • Dashboard (OSS): src/server/ui/
  • OpenClaw Plugin: src/packages/openclaw/ (npm: @acontext/openclaw)
  • Sandbox Cloudflare: src/packages/sandbox-cloudflare/ — a scaffolding/template package (npm: @acontext/create-sandbox-cloudflare). Its prepublishOnly script copies src/server/sandbox/cloudflare/ into template/ with variable substitution, so changes to the Cloudflare Worker source in src/server/sandbox/cloudflare/ are automatically picked up on publish.
  • E2E Tests: src/server/tests/e2e/ — integration tests that run the full stack (API + Core + deps) via docker-compose.test.yml. When modifying server-side behavior, check if e2e coverage should be added or updated.
  • Landing Page: landingpage
  • Documentation: docs/

Plan-driven Development

  • You must make a markdown plan at plans/ before you implement any my request, otherwise I will reject your implementation.
  • The plans/ directory is gitignored — plans are kept locally and do not need to be committed or included in PRs.
  • Remember to update the dev plan after you finish coding.
  • When every task is completed, make sure you check the task checkbox in the corresponding plan.
  • A plan should at least contain features/show case, designs overview, TODOS, new deps, test cases sections.
    • always use the features/show case to present what you're going to build.
    • use checklist in TODOS section, for each checkbox, you must have a clear descripion of what to do and list all the files that will be modified.
    • in new deps section, you must list all the new external dependencies that will be added.
    • in test cases section, use checklist format to list all the test cases that should be covered.

Read the full file on GitHub · 102 lines

Changes

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.

  1. 3d ago First seen · 102 lines · 2,445 tokens per session scan A 77be58c9dacb

Subscribe to this mod's changes

Acontext AGENTS.md is an instructions file published in the GitHub repository memodb-io/Acontext (3,679 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 2,445 tokens to every session, about $0.0122 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.

Building agentsMemory and contextagentagent-development-kitai-agentcontext-engineeringdata-platformself-learning
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