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 skills/langchain-ai/deepagents/remembernpx skills add langchain-ai/deepagents --skill remembergit clone --depth 1 https://github.com/langchain-ai/deepagentsWhat 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 | $0.00071 | $0.00978 |
| Opus 5 | $0.00036 | $0.00489 |
| Sonnet 5 | $0.00014 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
remember 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 yesterday.
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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review our conversation and capture valuable knowledge. Focus especially on best practices we discussed or discovered—these are the most important things to preserve.
Step 1: Identify Best Practices and Key Learnings
Scan the conversation for:
Best Practices (highest priority)
- Patterns that worked well - approaches, techniques, or solutions we found effective
- Anti-patterns to avoid - mistakes, gotchas, or approaches that caused problems
- Quality standards - criteria we established for good code, documentation, or processes
- Decision rationale - why we chose one approach over another
Other Valuable Knowledge
- Coding conventions and style preferences
- Project architecture decisions
- Workflows and processes we developed
- Tools, libraries, or techniques worth remembering
- Feedback I gave about your behavior or outputs
Step 2: Decide Where to Store Each Learning
For each best practice or learning, choose the right destination:
-> Memory (AGENTS.md) for preferences and guidelines
Use memory when the knowledge is:
- A preference or guideline (not a multi-step process)
- Something to always keep in mind
- A simple rule or pattern
Global ($DEEPAGENTS_HOME/agent/AGENTS.md): Universal preferences across all projects
Project (.deepagents/AGENTS.md): Project-specific conventions and decisions
-> Skill for reusable workflows and methodologies
Create a skill when we developed:
- A multi-step process worth reusing
- A methodology for a specific type of task
- A workflow with best practices baked in
- A procedure that should be followed consistently
Skills are more powerful than memory entries because they can encode how to do something well, not just what to remember.
Step 3: Create Skills for Significant Best Practices
If we established best practices around a workflow or process, capture them in a skill.
Example: If we discussed best practices for code review, create a code-review skill that encodes those practices into a reusable workflow.
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.
- yesterday First seen · 119 lines · 71 tokens per session scan A 86303ef1850c
remember is a skill published in the GitHub repository langchain-ai/deepagents (28,721 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 978 once invoked, about $0.0004 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
dg-piagent
Use when working with pi-agent / @earendil-works/pi-coding-agent SDK — 创建 agent、自定义工具、 编写扩展、修改系统提示词、管理会话、配置模型、处理认证、加载 skills/prompts/context files、 实现完全控制模式、或任何需要调用 pi-agent SDK API 的任务时使用。 也用于企业内网接口接入评估：用户给出企业/内网 LLM 接口文档或地址，问「能不能接」「怎么配」时， 按接口核对清单给出接入建议 + models.json 配置初稿。…
python-patterns
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
refine
End-of-session reflection. Reviews friction encountered during the session and proposes updates to docs/ to capture lessons learned.
recover-from-failure
How to recover when a tool call fails — diagnose, not blindly retry.
workspace-conventions
Reminders about how Dawn's workspace tools behave and what the path-jail allows.
dawn
Build AI agents and workflows with the Dawn framework — the TypeScript meta-framework for LangGraph. Use when creating, editing, or debugging a Dawn app (routes, tools, state, agents, workflows, testing, deployment).