Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
ask-deep-wiki
For questions about Light Protocol's SDK, smart contracts and Solana development, Claude Code features, or agent skills. AI-powered answers grounded in repos...
skill openclawclawhub Free
For questions about Light Protocol's SDK, smart contracts and Solana development, Claude Code features, or agent skills. AI-powered answers grounded in repos...
β¬ 0 downloads β
0 stars Unverified but indexed
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
- 1.0.2
Provenance
- Publisher
- tilo-14
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
DeepWiki Research
Query repositories via DeepWiki MCP to answer technical questions with precise, source-backed answers.
Workflow
Understand the question Identify what the user is asking and which domain it falls into If the question is ambiguous, state your understanding and ask for clarification Gather context Match question to the execution steps below Use Glob, Grep, and Read to find relevant local files Query DeepWiki MCP (mcp__deepwiki__ask_question) and mcp__zkcompression__SearchLightProtocol for repository-level context Use Task subagents for parallel research across multiple repos when needed Synthesize and respond Apply precision rules to the answer Format per format response
1. Read required context in local repo
Use Glob and Grep to locate relevant files in the current repository. Use Read to pull in specific content needed to answer the question.
2. Identify question scope
Determine the domain: Programs, client SDKs, architecture, implementation details Specific components (LightAccount, ValidityProof, CPI, etc.)
3. Fetch repository context
Select the appropriate repository based on question scope: Light Protocol (compressed accounts, state trees, ZK compression, Light SDK) mcp__deepwiki__read_wiki_structure("Lightprotocol/light-protocol") mcp__deepwiki__read_wiki_contents("Lightprotocol/light-protocol") mcp__deepwiki__ask_question("Lightprotocol/light-protocol", "your question") Solana Development (programs, accounts, general Solana) mcp__deepwiki__read_wiki_structure("blueshift-gg/blueshift-dashboard") mcp__deepwiki__read_wiki_contents("blueshift-gg/blueshift-dashboard") mcp__deepwiki__ask_question("blueshift-gg/blueshift-dashboard", "your question") Claude Code (CLI features, commands, configuration) mcp__deepwiki__read_wiki_structure("anthropics/claude-code") mcp__deepwiki__read_wiki_contents("anthropics/claude-code") mcp__deepwiki__ask_question("anthropics/claude-code", "your question") Agent Skills (skills, subagents, patterns) mcp__deepwiki__read_wiki_structure("anthropics/skills") mcp__deepwiki__read_wiki_contents("anthropics/skills") mcp__deepwiki__ask_question("anthropics/skills", "your question")
4. Apply precision rules
AVOID: Vague verbs: "handles", "manages", "processes", "enables", "provides" Abstract nouns: "operations", "management", "coordination" -ing endings when a precise verb exists Marketing language: "comprehensive", "rich", "seamless", "enables", "provides capability" ZK terminology in user-facing docs: "inclusion proof", "non-inclusion proof" β Instead describe what it does: "prove the address doesn't exist", "prove the account hash exists" Generic descriptions: "account metadata" instead of "CompressedAccountMeta" USE: Exact function/method names Concrete data structures Specific operations: "sets discriminator", "creates hash", "nullifies account" File:line references "reference" when describing u8 indices that reference accounts in the accounts array (standard Solana terminology) "CPI" as a verb (e.g., "CPIs the Account Compression Program") Technical method/function names used as verbs when contextually clear Describe exactly what happens.
5. Format response
Structure answers with: Technical precision Code examples with inline comments Source references (file paths, line numbers)
Example Output
Question: What do you include in instruction data for compressed account creation? Answer:
Compressed Account Creation Instruction Data
Instruction data contains three components for proof verification and tree coordination. Components: pub struct InstructionData { proof: ValidityProof, address_tree_info: PackedAddressTreeInfo, output_state_tree_index: u8, // + custom account data } 1. Non-inclusion proof ValidityProof proves the derived address does not exist in the specified address tree Client generates via getValidityProof() RPC call 2. Tree location parameters PackedAddressTreeInfo: Specifies which address tree registers the address (u8 index, not pubkey) output_state_tree_index: Specifies which state tree stores the compressed account hash 3. Custom account data Program-specific fields (e.g., message: String, user data) Execution flow: Address tree proves uniqueness, stores address State tree stores account hash ValidityProof cryptographically guarantees address is unused Why separate trees: Compressed accounts require client-generated cryptographic proof that address doesn't exist (unlike regular Solana where runtime checks PDA existence). Address trees enforce uniqueness; state trees store account hashes. Packed structs use u8 indices to reference accounts in remaining_accounts, reducing transaction size.
Security
This skill does not pull, store, or transmit external secrets. It provides code patterns, documentation references, and development guidance only. No credentials consumed. The skill requires no API keys, private keys, or signing secrets. env: [] is declared explicitly. DeepWiki MCP accesses public repositories only. All mcp__deepwiki__* calls query public GitHub repositories (Lightprotocol/light-protocol, anthropics/claude-code, anthropics/skills). No authentication tokens are required or transmitted. DeepWiki does not access private repositories unless explicitly configured with a token β this skill does not configure one. User-provided configuration. RPC endpoints, wallet keypairs, and authentication tokens (Privy, wallet adapters) are configured in the user's own application code β the skill only demonstrates how to use them. Tool boundary enforced. The allowed-tools list restricts this skill to read-only operations (Read, Glob, Grep), research subagents (Task), web fetches to Light Protocol domains, and MCP queries. It cannot load other skills, write files, or execute shell commands. Verify the allowed-tools list in the frontmatter above matches these constraints. Install source. npx skills add Lightprotocol/skills installs from the public GitHub repository (Lightprotocol/skills). Verify the source before running. Audited protocol. Light Protocol smart contracts are independently audited. Reports are published at github.com/Lightprotocol/light-protocol/tree/main/audits.
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