Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Trade Validation
10-dimension weighted scoring framework for prediction market trade evaluation. Enforces disciplined position sizing, circuit breakers, and mandatory counter-arguments. Use when: evaluating prediction market trades, scoring opportunities, deciding position sizes, comparing Polymarket/Kalshi opportunities, running pre-trade checklists. Don't use when: general crypto analysis, DeFi yield farming, non-prediction-market investments, stock/equity analysis, sports betting (different framework needed). Negative examples: - "Should I buy ETH?" → No. This is for prediction markets with binary/discrete outcomes. - "What's the best DeFi yield?" → No. Wrong domain entirely. - "Score this sports bet" → No. Sports betting has different dimensions (injuries, matchups). Edge cases: - Crypto prediction markets (e.g., "Will BTC hit $X?") → YES, use this if on Polymarket/Kalshi. - Multi-outcome markets → Score each outcome separately. - Markets with <$25 liquidity → Auto-fail on Liquidity dimension.
skill openclawclawhub Free
10-dimension weighted scoring framework for prediction market trade evaluation. Enforces disciplined position sizing, circuit breakers, and mandatory counter-arguments. Use when: evaluating prediction market trades, scoring opportunities, deciding position sizes, comparing Polymarket/Kalshi opportunities, running pre-trade checklists. Don't use when: general crypto analysis, DeFi yield farming, non-prediction-market investments, stock/equity analysis, sports betting (different framework needed). Negative examples: - "Should I buy ETH?" → No. This is for prediction markets with binary/discrete outcomes. - "What's the best DeFi yield?" → No. Wrong domain entirely. - "Score this sports bet" → No. Sports betting has different dimensions (injuries, matchups). Edge cases: - Crypto prediction markets (e.g., "Will BTC hit $X?") → YES, use this if on Polymarket/Kalshi. - Multi-outcome markets → Score each outcome separately. - Markets with <$25 liquidity → Auto-fail on Liquidity dimension.
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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/scoring-rubric.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
- staybased
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Trade Validation — 10-Dimension Scoring Framework
Rule: NO trade executes without 80%+ weighted confidence score. Any single dimension below 4/10 = AUTOMATIC VETO.
Scoring Dimensions
#DimensionWeightWhat It Measures1Information Edge18%Do we know something the market doesn't?2Source Quality12%How reliable are our sources?3Market Efficiency10%Is this market likely mispriced?4Time Horizon8%How long is capital locked up?5Downside Protection15%What's the worst case?6Cross-Validation12%Do multiple independent signals agree?7Historical Accuracy5%Track record on similar bets?8Liquidity/Execution Risk7%Can we get in AND out?9Consensus Divergence8%How far are we from market consensus?10Event Catalyst5%Is there a known resolution trigger? Total: 100%
Calculation
Weighted Score = Σ(dimension_score / 10 × weight) × 100
Threshold Rules
Weighted ScoreActionBet Size< 80%❌ NO TRADE$080–84%✅ Minimum$3–585–89%✅ Standard$5–790%+✅ ConvictionUp to $7.50 (max 10% bankroll)
Veto Rules
Any dimension < 4/10 → AUTOMATIC VETO regardless of total score Rationale: A critical weakness in any area (e.g., Liquidity = 2 means you're trapped)
Risk Management
Max position: 10% of portfolio per trade Min market liquidity: $25 (below this, don't trade) Max open exposure: 30% of bankroll across all positions Daily loss circuit breaker: $8 loss in a day → ALL trading stops for 24 hours Cool-down: No trade within 1 hour of a loss No revenge trading: Last loss must be >24h ago OR new trade is unrelated No trading 12am–7am unless time-critical
Mandatory Counter-Arguments
Every trade MUST document: Why could we be WRONG? (not a strawman — a genuine strong counter-argument) What would change our mind? (specific falsification criteria) Exit strategy: When do we sell early?
Score Card Template
TRADE SCORE CARD ═══════════════════════════════════════════════════════════ Market: [name] Date: [date] Position: [YES/NO @ price] # Dimension Weight Score Weighted ─── ────────────────────── ──────── ─────── ────────── 1 Information Edge 18% __/10 __._% 2 Source Quality 12% __/10 __._% 3 Market Efficiency 10% __/10 __._% 4 Time Horizon 8% __/10 __._% 5 Downside Protection 15% __/10 __._% 6 Cross-Validation 12% __/10 __._% 7 Historical Accuracy 5% __/10 __._% 8 Liquidity/Execution 7% __/10 __._% 9 Consensus Divergence 8% __/10 __._% 10 Event Catalyst 5% __/10 __._% ─── ────────────────────── ──────── ─────── ────────── TOTAL 100% __._% Minimum Score: __/10 (dimension: _____________) VETO Check: [ ] All dimensions ≥ 4 — PASS / FAIL Counter-argument: ________________________________ What would change our mind: _____________________ Exit strategy: __________________________________ RESULT: TRADE / NO TRADE Tier: [ ] Min ($3-5) [ ] Standard ($5-7) [ ] Conviction ($7.50) ═══════════════════════════════════════════════════════════
Pre-Trade Checklist
RESEARCH [ ] Minimum 3 independent sources consulted [ ] Sources documented with links [ ] Strong counter-argument documented [ ] Counter-argument is genuine (not strawman) SCORING [ ] All 10 dimensions scored [ ] Weighted score ≥ 80% [ ] No dimension below 4/10 [ ] Score logged to trade journal RISK [ ] Current bankroll: $______ [ ] Bet ≤ 10% of bankroll [ ] Total open exposure ≤ 30% [ ] Daily loss < $8 (circuit breaker not triggered) DISCIPLINE [ ] Cool-down respected (1h since last loss) [ ] Not revenge trading [ ] Not trading 12am–7am
Detailed Scoring Rubric
See references/scoring-rubric.md for the full 1–10 rubric for each dimension.
Trade Journal
- Log every scored trade (pass or fail) to projects/polymarket/trade-journal/:
- ## [DATE] — [MARKET NAME]
- **Score:** XX.X%
- **Result:** TRADE / NO TRADE / VETO
- **Position:** YES/NO @ XXc | **Stake:** $X.XX
- **Outcome:** WIN / LOSS / PENDING
- **P&L:** +/- $X.XX
- **Lesson:** (post-resolution)
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/scoring-rubric.md