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 Lians-ai/Lians --skill lians-integrategit clone --depth 1 https://github.com/Lians-ai/LiansWrote 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/lians-ai/lians/lians-integrate)<a href="https://agentmods.dev/skills/lians-ai/lians/lians-integrate"><img src="https://agentmods.dev/badge/skills/lians-ai/lians/lians-integrate/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/lians-ai/lians/lians-integrate"><img src="https://agentmods.dev/badge/skills/lians-ai/lians/lians-integrate.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 | $0.00053 | $0.00699 |
| Opus 5 | $0.00026 | $0.00349 |
| Sonnet 5 | $0.00011 | $0.00140 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
lians-integrate 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- lians-integrate — 100% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integrate Lians into this repository
A pipeline skill: add Lians as the memory layer for the agent in this repo. Add a memory layer; don't rewrite the app. Work test-first.
Procedure
-
Survey the agent loop. Find where the model is called, where context is assembled, and where a turn completes. Detect the framework in use.
-
Branch.
git checkout -b lians-integrationbefore editing. -
Install the matching package:
- LangChain →
pip install lians-sdk[langchain] - LangGraph →
pip install lians-sdk[langgraph] - CrewAI →
pip install lians-sdk[crewai] - OpenAI Agents SDK →
pip install lians-sdk[openai-agents] - AutoGen →
pip install lians-sdk[autogen] - raw / unknown →
pip install lians-sdk(use the harness)
- LangChain →
-
Wire memory in.
Raw loop - use the harness:
from lians import LiansClient, LiansMemoryHarness harness = LiansMemoryHarness( LiansClient(base_url=os.environ["LIANS_URL"], api_key=os.environ["LIANS_API_KEY"]), agent_id="<this-agent>", domain="finance", # or healthcare / legal ) # before model call: context = harness.recall_context(user_query) # after model call: harness.remember(response) # or both: answer = harness.run_turn(user_query, generate=call_model)Framework - import its integration module:
from lians.langchain_integration import LiansChatHistory, build_tools from lians.langgraph_integration import create_recall_node, create_remember_node from lians.crewai_integration import build_crewai_tools from lians.openai_agents_integration import build_openai_agent_tools from lians.autogen_integration import build_autogen_tools -
Test (required). Add a test using
LocalLiansClient(no server/API key) that proves: (a) a remembered fact is recalled, and (b) a superseding write hides the stale fact fromrecallbutrecall_at(as_of=...)still sees it. Run the suite. -
Report. Summarize the diff, the env vars required (
LIANS_URL,LIANS_API_KEY,LIANS_AGENT_ID), and how to verify locally. Do not commit - leave the branch for review.
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 · 66 lines · 53 tokens per session scan A 0cd7d6dc15bb
lians-integrate is a skill published in the GitHub repository Lians-ai/Lians (11 stars, last pushed 5d ago), licensed Apache-2.0. It adds 53 tokens to every session and 699 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
docmancer
Work from the same local memory as every other coding agent on this machine. Recall prior decisions, preferences, instructions, and project conventions that Claude Code, Codex, Cursor, and other agents wrote here, with cited sources, fully local. Also searches a separate local technical-documentation index.
python-packaging-license-finder
Use this skill to deterministically find license information for Python packages by checking PyPI metadata first, then falling back to Git repository LICENSE files using shallow cloning.
python-testing
Test Python applications with pytest fixtures, parametrization, temporary paths, and regression coverage.
memoryguard
Local-first MCP memory backend and governance console for coding agents. Auto-organize, quarantine, supersede, and rollback shared memories across multiple agents.
pytest-patterns
Python testing skill using pytest, covering fixtures, parametrize, markers, conftest, plugins, mocking, and advanced testing patterns.