Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Defi Sniper
Orchestrates early token launch detection, on-chain risk analysis, social signal verification, and guarded swap execution on Solana and Base chains.
skill openclawclawhub Free
Orchestrates early token launch detection, on-chain risk analysis, social signal verification, and guarded swap execution on Solana and Base chains.
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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, references/inspected-skills.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
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- 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
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- h4gen
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Purpose
Run a high-speed token opportunity workflow: detect very early pool/token activity, triage contract/market risk, verify social signal quality, execute small, bounded entries when rules pass. This is an orchestration skill. It coordinates upstream skills and explicit risk policy. It does not guarantee profit.
Required Installed Skills
minara (inspected latest: 1.1.9) torchmarket (inspected latest: 4.2.7) torchliquidationbot (inspected latest: 3.0.2) Install/update: npx -y clawhub@latest install minara npx -y clawhub@latest install torchmarket npx -y clawhub@latest install torchliquidationbot npx -y clawhub@latest update --all
Required Configuration and Credentials
Minimum: MINARA_API_KEY SOLANA_RPC_URL Depending on execution route: Minara signer path: Circle Wallet preferred, or chain private-key fallback per Minara docs. Torch vault path: VAULT_CREATOR and linked agent wallet for vault-routed operations. Preflight checks before any live execution: chain (solana or base) explicitly selected funding source identified (vault or signer account) max-risk limits loaded dry-run path available
Solana path (full stack)
Use: Minara for detection/intent parsing, Torch Market for deep token + quote + treasury/lending state, optional Torch execution patterns (vault-routed), external web search for social confirmation.
Base path (constrained path)
Use: Minara for detection/intent/transaction assembly, external web search for social confirmation. Important boundary: Torch Market and Torch Liquidation Bot are Solana-focused and should not be assumed to provide Base-native token risk primitives.
Inputs the LM Must Collect First
target_chain: solana or base token_symbol_or_mint max_entry_size (example: 1 SOL or base-chain equivalent) max_slippage_bps (example: 300) risk_mode: observe, paper, live sentiment_min_accounts (minimum credible, non-bot mentions) execution_policy: manual-confirm or auto-with-guardrails If missing, do not run live execution.
Minara (minara)
Primary detection/intelligence and swap-intent layer: market chat/intel, intent-to-swap transaction generation, chain-aware execution pathways, strategy support across Solana and EVM (including Base). Use Minara when rapid parsing and transaction assembly are required.
Torch Market (torchmarket)
Solana-native deep state layer: token discovery (getTokens) and token details (getToken), buy/sell quote simulation (getBuyQuote, getSellQuote), treasury/lending/position context (getLendingInfo, getLoanPosition), vault-routed transaction builders. Use Torch Market for on-chain structural checks and quote sanity before Solana entries.
Torch Liquidation Bot (torchliquidationbot)
Execution engine specialized for liquidation keepers: continuous scan loop, high-speed vault-routed transaction execution patterns, strict vault safety boundary. Important boundary: It is purpose-built for liquidation flow (buildLiquidateTransaction path), not a generic buy/sell sniper by default. Reuse only its operational/safety pattern unless a dedicated swap executor is explicitly available.
Canonical Signal Chain
Use this chain for launch-sniping decisions.
Stage 1: Early launch detection
Use Minara intelligence to detect candidate opportunities and parse swap intent context. Required output: token/mint identifier chain initial liquidity signal if available timestamp of first detection
Stage 2: On-chain risk triage
For Solana candidates, use Torch Market state: token status and reserves, quote simulation (buy/sell impact), treasury and lending context where relevant, holder concentration snapshots (if available through token/holder queries). Risk interpretation policy: No single field should be treated as a complete rug/honeypot verdict. Require multiple independent indicators before green-lighting.
Stage 3: Social signal confirmation
Use external web search tools (not bundled in these three skills) to validate whether real accounts are discussing the token. Minimum checks: account quality (non-trivial follower/history signals) message diversity (not duplicate bot spam) temporal alignment with on-chain launch timing
Stage 4: Decision matrix
Compute two gates: SecurityGate: pass/fail SentimentGate: pass/fail Execution rule: only if both gates are pass otherwise no_trade
Stage 5: Execution
If execution allowed: enforce position cap (example: 1 SOL) enforce slippage cap record tx hash and rationale immediately set post-entry monitoring conditions
Scenario Mapping (PEPE2.0 on Solana)
For the scenario in this skill request: Minara flags a new Solana token/pool event with initial liquidity context. Torch Market fetches token-level state and quote/treasury context. Social verification runs in parallel via external web search (X/Twitter signal quality). If SecurityGate=pass and SentimentGate=pass, execute bounded entry (example 1 SOL) with fixed slippage tolerance. Log full decision trail: signals, checks, final action.
Output Contract
Always return: Detection chain, token ID, first-seen timestamp OnChainRisk indicators checked pass/fail with reasons SocialSignal source summary pass/fail with reasons ExecutionDecision trade or no_trade size, slippage, route AuditTrail exact checks performed unresolved uncertainties
Risk Guardrails
Never deploy unbounded size; always cap first entry. Never trade without slippage limits. Never treat hype alone as trade permission. Never claim "safe" based on one heuristic. Prefer no_trade on ambiguous or conflicting evidence. In auto-with-guardrails mode, require preconfigured hard limits and fail-closed defaults.
observe
Detection and scoring only. No trade.
paper
Simulated entries/exits with recorded hypothetical PnL.
live
Real execution only after preflight and guardrail checks pass.
Failure Handling
Missing key/config/env: halt with explicit missing item list. Detection without sufficient risk data: downgrade to observe. Sentiment source unavailable: require manual confirmation or no_trade. Execution route unavailable on selected chain: return explicit compatibility mismatch.
Known Limits from Inspected Upstream Skills
Minara inspected docs describe intent parsing and transaction assembly, but do not expose a dedicated "mempool scanner" endpoint in the inspected SKILL.md. Torch Market exposes rich Solana token/treasury/lending state and quotes, but no single built-in "honeypot/rug score" flag. Torch Liquidation Bot is liquidation-specialized; using it as a generic swap executor is a repurposing pattern, not its native primary workflow. Social signal checks require external web/search skills outside this three-skill stack. Treat these limits as mandatory disclosures in final operator output.
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
2 Docs
- SKILL.md
- references/inspected-skills.md