Deep Agents is an extensible agent harness that provides an out-of-the-box agent for long, multi-step tasks, with features such as planning, sub-agents, filesystem access, context management, memory, and human approval of tool calls. It is used by developers building agents with different language models, and its catalogue entries extend the harness with reusable skills, MCP servers, and instructions.
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 langchain-ai/deepagents --skill coding-prefsgit clone --depth 1 https://github.com/langchain-ai/deepagentsWrote 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/langchain-ai/deepagents/coding-prefs)<a href="https://agentmods.dev/skills/langchain-ai/deepagents/coding-prefs"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/coding-prefs/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/langchain-ai/deepagents/coding-prefs"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/coding-prefs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00036 | $0.00281 |
| Opus 5.5 | $0.00014 | $0.00112 |
| Sonnet 5.5 | $0.00007 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
coding-prefs 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 1mo 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
Coding Preferences Skill
Use this skill to keep /memory/coding-prefs.md in sync with how this
specific user wants you to work. This file is user-scoped, so each user
has their own copy — anything you write here only affects future
conversations with the same user.
When to Read
- Before picking a code style, test framework, or commit message format
- Before deciding whether to add comments, type hints, or docstrings
- Before refactoring beyond what was asked
When to Write
Append a new entry whenever the user gives feedback that should apply to future work:
- "Don't add docstrings unless I ask" → save it
- "I prefer pytest over unittest" → save it
- "Stop summarizing what you did at the end" → save it
Each entry should be one line: the rule, then a brief reason if the user gave one.
How to Write
Read the file first (it may not exist yet), then append. Don't overwrite — preferences accumulate over time. If a new preference contradicts an existing one, replace the old line and note the change.
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.
- 1mo ago First seen · 36 lines · 36 tokens per session scan A bd25243f2f61
coding-prefs is a skill published in the GitHub repository langchain-ai/deepagents (30,371 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 281 once invoked, about $0.0001 per session on Opus 5.5. 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.
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