Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
LangChain
Avoid common LangChain mistakes — LCEL gotchas, memory persistence, RAG chunking, and output parser traps.
skill openclawclawhub Free
Avoid common LangChain mistakes — LCEL gotchas, memory persistence, RAG chunking, and output parser traps.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
LCEL Basics
| pipes output to next — prompt | llm | parser RunnablePassthrough() forwards input unchanged — use in parallel branches RunnableParallel runs branches concurrently — {"a": chain1, "b": chain2} .invoke() for single, .batch() for multiple, .stream() for tokens Input must match expected keys — {"question": x} not just x if prompt expects {question}
Memory Gotchas
Memory doesn't auto-persist between sessions — save/load explicitly ConversationBufferMemory grows unbounded — use ConversationSummaryMemory for long chats Memory key must match prompt variable — memory_key="chat_history" needs {chat_history} in prompt return_messages=True for chat models — False returns string for completion models
RAG Chunking
Chunk size affects retrieval quality — too small loses context, too large dilutes relevance Chunk overlap prevents cutting mid-sentence — 10-20% overlap typical RecursiveCharacterTextSplitter preserves structure — splits on paragraphs, then sentences Embedding dimension must match vector store — mixing models causes silent failures
Output Parsers
PydanticOutputParser needs format instructions in prompt — call .get_format_instructions() Parser failures aren't always loud — malformed JSON may partially parse OutputFixingParser retries with LLM — wraps another parser, fixes errors with_structured_output() on chat models — cleaner than manual parsing for supported models
Retrieval
similarity_search returns documents — .page_content for text k parameter controls results count — more isn't always better, noise increases Metadata filtering before similarity — filter={"source": "docs"} in most vector stores max_marginal_relevance_search for diversity — avoids redundant similar chunks
Agents
Agents decide tool order dynamically — chains are fixed sequence Tool descriptions matter — agent uses them to decide when to call handle_parsing_errors=True — prevents crash on malformed agent output Max iterations prevents infinite loops — max_iterations=10 default may be too low
Common Mistakes
Prompt template variables case-sensitive — {Question} ≠ {question} Chat models need message format — ChatPromptTemplate, not PromptTemplate Callbacks not propagating — pass config={"callbacks": [...]} through chain Rate limits crash silently sometimes — wrap in retry logic Token count exceeds context — use trim_messages or summarization for long histories
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md