Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
WaitingForMacGuffin
Oscar prediction market intelligence from waitingformacguffin.com. Get live odds, whale activity, price movements, precursor awards, order book depth, and fr...
skill openclawclawhub Free
Oscar prediction market intelligence from waitingformacguffin.com. Get live odds, whale activity, price movements, precursor awards, order book depth, and fr...
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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.
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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
- 1.3.0
Provenance
- Publisher
- sonderspot
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Welcome Message
When a user first installs this skill or greets you, introduce yourself: "Hey! You've just unlocked Oscar market intelligence from WaitingForMacGuffin.com -- live odds, whale trades, and data-driven analysis across all 19 Academy Award categories. Here's what I can do: Market pulse -- "What's happening in Oscar markets?" (whale trades, price moves, frontrunner changes) Deep dive -- "Tell me about Chalamet" or "Best Picture odds" (full nominee profile with trends, precursors, order book) Bet picks -- "Give me your best Oscar bets" (risk-tiered recommendations with ROI and portfolio options) Precursor sim -- "DGA just announced, what's the play with $500?" (slippage-aware portfolio with EV and position sizing) What are you curious about?" Real-time Oscar prediction market data from waitingformacguffin.com. Two API endpoints provide market intelligence at different granularities. Base URL: https://waitingformacguffin.com No authentication required. All data is public and read-only.
Tool 1: Oscar Brief
When to use: User asks "What's happening in Oscar markets?", "Any updates?", "Oscar brief", or wants a quick market summary. What it returns: Filtered signals only -- price moves, whale trades ($1K+), frontrunner changes, news sentiment. If markets are quiet, says so (never fabricates activity).
API Call
curl -s "https://waitingformacguffin.com/api/oscar/brief?hours=24&sensitivity=medium"
Parameters
ParamTypeDefaultDescriptionhoursnumber24Lookback period (1-168)sensitivitystring"medium""low" (>7pt moves, >$5K trades), "medium" (>3pt, >$1K), "high" (>1pt, >$500)categoriesstringbig 6Comma-separated category slugs. Omit for big 6 (best-picture, best-director, best-actor, best-actress, supporting-actor, supporting-actress)
Response Structure
{ "signals": [ { "type": "price_move | whale_trade | frontrunner_change | news_sentiment", "category": "best-actor", "categoryName": "Best Actor", "severity": "major | significant | notable | info", "headline": "Chalamet βΌ 5pts to 62c", "details": "Best Actor: Chalamet moved from 67c to 62c in the last 24h", "timestamp": "2026-02-18T12:00:00Z" } ], "market_snapshot": { "frontrunners": { "best-picture": { "name": "...", "price": 45 } }, "whale_trade_count_24h": 7, "overall_sentiment": "quiet | active | volatile", "whale_leaderboard": [ { "rank": 1, "nominee": "Jessie Buckley", "category": "best-actress", "categoryName": "Best Actress", "totalVolumeUsd": 4850.00, "tradeCount": 1, "yesVolumeUsd": 4850.00, "noVolumeUsd": 0, "sentiment": "bullish | bearish | mixed" } ] } } The whale_leaderboard ranks nominees by total whale trade volume within the lookback window. Use it to answer questions like "who has the most whale activity?" or "where is the smart money going?". To get all 19 categories, pass categories= with all slugs (see Available Categories below).
How to Present Results
Lead with the overall_sentiment and whale_trade_count_24h List frontrunners with prices Show signals grouped by severity (major first) If signals is empty, say "Markets are quiet -- no significant moves" Use severity icons: major = !!!, significant = !!, notable = !, info = i When asked about whale activity rankings, use whale_leaderboard -- present as a ranked list with nominee, category, volume, trade count, and sentiment
Example
# Default brief (24h, medium sensitivity, big 6 categories) curl -s "https://waitingformacguffin.com/api/oscar/brief" # Last 48 hours, high sensitivity, all categories curl -s "https://waitingformacguffin.com/api/oscar/brief?hours=48&sensitivity=high&categories=best-picture,best-director,best-actor,best-actress,supporting-actor,supporting-actress,best-cinematography,best-original-screenplay,best-adapted-screenplay,best-international-feature,best-film-editing,best-costume-design,best-original-song,best-original-score,best-production-design,best-sound,best-documentary-feature,best-makeup-hairstyling,best-visual-effects"
Tool 2: Oscar Research
When to use: User asks "Tell me about Chalamet", "Should I bet on X?", "What are the Best Picture odds?", or wants detailed research on a specific nominee or category. What it returns: Deep dive with odds, 7-day trend, precursor wins, whale activity, order book depth + slippage, news, and a data-driven assessment.
API Call
curl -s "https://waitingformacguffin.com/api/oscar/research?query=Chalamet"
Parameters
ParamTypeDefaultDescriptionquerystring(required)Nominee name, film title, or category slug. Supports fuzzy matching.include_orderbookbooleantrueInclude order book depth and slippage analysisbudget_for_slippagenumber500USD amount for slippage calculation (100-100000)categorystring(optional)Category slug to narrow disambiguation
Query Resolution
The query is fuzzy-matched automatically: Category slug or name: "best-picture" or "Best Picture" returns category overview Exact name: "Timothee Chalamet" (case-insensitive) Substring: "Chalamet" finds "Timothee Chalamet" Diacritics-normalized: "Timothee" matches "Timothee" Film title: matches against the film database Typo correction: "Chalmet" resolves via Levenshtein (edit distance <= 3)
Three Response Modes
1. Nominee deep-dive (mode: "nominee") -- single match: { "mode": "nominee", "nominee": { "name": "Timothee Chalamet", "category": "best-actor", "categoryName": "Best Actor", "ticker": "KXOSCARACTO-26-TIM" }, "odds": { "current": 62, "impliedProbability": "62%", "trend7d": -5, "trendDirection": "falling", "rank": 1, "categorySize": 9 }, "risk": { "tier": "lean", "tier_emoji": "π ", "win_pct": 62, "loss_pct": 38, "roi_pct": 61, "payout_per_100": 161, "gap_to_second": 40, "runner_up": { "name": "Sean Penn", "price": 22 } }, "category_volatility": "low", "category_volatility_reason": "Category tends to follow precursors and consensus", "precursors": { "wins": ["globe", "cc"], "winCount": 2, "results": [...] }, "whaleActivity": { "tradeCount": 3, "totalVolumeUsd": 20200, "sentiment": "mixed", "directionRatio": 0.59, "recentTrades": [...] }, "orderBook": { "bestAsk": 62, "depthAtBest": 847, "slippageAnalysis": [{ "budgetUsd": 500, "avgFillPrice": 62.4, "slippagePct": 0.6, "assessment": "healthy" }] }, "news": [{ "title": "...", "source": "THR", "sentiment": "negative" }], "assessment": { "summary": "...", "edgeIndicator": "strong_value | fair_value | overpriced | uncertain", "risks": [...], "catalysts": [...] } } 2. Category overview (mode: "category") -- query is a category: { "mode": "category", "categoryName": "Best Picture", "nominees": [ { "rank": 1, "name": "One Battle After Another", "price": 45, "trend7d": 3, "trendDirection": "rising" }, { "rank": 2, "name": "Sinners", "price": 22, "trend7d": -2, "trendDirection": "falling" } ] } 3. Disambiguation (mode: "disambiguation") -- multiple matches: { "mode": "disambiguation", "query": "Wicked", "matches": [ { "name": "Wicked: For Good", "category": "best-picture", "categoryName": "Best Picture" }, { "name": "Wicked: For Good", "category": "best-adapted-screenplay", "categoryName": "Best Adapted Screenplay" } ], "hint": "Narrow with category param" } When you get disambiguation, ask the user which category they mean, then re-call with &category=best-picture.
How to Present Results
Nominee deep-dive -- present in this order: Name, category, and ticker Odds: current price, implied probability, 7d trend (with arrow), rank Precursors: list wins with award names Whale activity: trade count, total volume, directional sentiment Order book: best ask, depth, slippage at the user's budget News: relevant headlines with source and sentiment Assessment: summary, edge indicator, risks and catalysts Category overview -- present as a ranked table with price and trend. Disambiguation -- list the matches and ask user to pick a category.
Examples
# Nominee deep-dive curl -s "https://waitingformacguffin.com/api/oscar/research?query=Chalamet" # Category overview curl -s "https://waitingformacguffin.com/api/oscar/research?query=best-picture" # With custom slippage budget curl -s "https://waitingformacguffin.com/api/oscar/research?query=Chalamet&budget_for_slippage=2000" # Narrow disambiguation curl -s "https://waitingformacguffin.com/api/oscar/research?query=Wicked&category=best-picture" # Skip order book (faster) curl -s "https://waitingformacguffin.com/api/oscar/research?query=Chalamet&include_orderbook=false"
Tool 3: Oscar Precursor Simulation
When to use: User asks "DGA just announced, what's the play?", "Build me a portfolio based on SAG results", "I have $500, what should I bet after guild week?", or wants a data-driven portfolio based on precursor award results. What it returns: A slippage-aware portfolio simulation with EV calculations, position sizing (Kelly-inspired), order book slippage, and recommendation labels for each position.
API Call
curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=dga&budget=500"
Parameters
ParamTypeDefaultDescriptionprecursorstring(required)Which precursor to base the simulation on: dga, sag, pga, critics-choice, golden-globes, bafta, or allbudgetnumber(required)USD budget for the portfolio (50-100000)risk_tolerancestring"moderate"conservative (safe, 20% reserve), moderate (balanced), aggressive (max deployment)categoriesstringall applicableComma-separated category slugs to limit simulation
Risk Tolerance Guide
LevelMax Single PositionReserveMin EdgeBest ForConservative50% of budget20%10%+ edge"I want to sleep at night"Moderate70% of budget10%5%+ edgeBalanced risk/reward (recommended)Aggressive90% of budget5%0%+ edge"I trust the data, deploy everything"
Response Structure
{ "strategy": { "name": "DGA Awards Portfolio Simulation", "precursor": "dga", "riskTolerance": "moderate", "totalBudget": 500, "deployedBudget": 450, "reserveBudget": 50, "expectedReturn": 520, "expectedROI": 15.6 }, "positions": [ { "nominee": "Paul Thomas Anderson", "category": "best-director", "categoryName": "Best Director", "ticker": "KXOSCARDIR-26-PAU", "currentPrice": 72, "impliedProb": 72.0, "precursorProb": 88.0, "precursorSource": "DGA Awards", "edge": 16.0, "allocatedBudget": 300, "contracts": 416, "avgFillPrice": 72.2, "slippagePct": 0.3, "kalshiFee": 5.92, "netExpectedProfit": 66.08, "recommendation": "strong_buy", "reasoning": "Paul Thomas Anderson won DGA Awards (88% Oscar correlation). 16.0% edge over market price. Strong setup: large edge with healthy liquidity." } ], "warnings": [], "disclaimer": "This is a simulation for educational purposes only...", "meta": { "generated_at": "2026-02-19T...", "data_sources": { "precursor_data": "2026-02-09", "live_odds": true, "order_books": 2 }, "latency_ms": 2100 } }
Recommendation Labels
LabelCriteriaIconstrong_buyEdge 20%+ AND slippage <= 3%!!!buyEdge 10%+!!speculativeEdge > 0%!skipNo edge or below risk threshold--
How to Present Results
Lead with strategy summary: precursor name, budget, risk tolerance, deployed vs reserve Show each position as a structured block: {recommendation_icon} **{nominee}** -- {categoryName} ββ Price: {currentPrice}c (market says {impliedProb}% / precursor says {precursorProb}%) ββ Edge: {edge}% | Allocated: ${allocatedBudget} ββ Fill: {contracts} contracts @ {avgFillPrice}c avg ({slippagePct}% slippage) ββ Kalshi fee: ${kalshiFee} | Net expected profit: ${netExpectedProfit} ββ {reasoning} Summary table for 2+ positions Show warnings if any (liquidity issues, missing data) Always show disclaimer
Examples
# DGA just announced β what's the play with $500? curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=dga&budget=500" # SAG winners, conservative, acting categories only curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=sag&budget=1000&risk_tolerance=conservative&categories=best-actor,best-actress,supporting-actor,supporting-actress" # All precursors combined, aggressive $2000 portfolio curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=all&budget=2000&risk_tolerance=aggressive" # PGA for Best Picture only curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=pga&budget=300&categories=best-picture"
Supporting Endpoint: Precursor Data
Raw precursor data enriched with live odds and correlation scores. Use when you need precursor details without a full portfolio simulation.
API Call
curl -s "https://waitingformacguffin.com/api/precursors"
Parameters
ParamTypeDefaultDescriptioncategorystringall big 6Single category slug to filterprecursorstringallSingle precursor ID to filter winners
What it Returns
For each category: nominees with their precursor wins, correlation rates, precursor scores (0-100), and current odds. Also includes the award calendar with completed/upcoming status.
Examples
# All categories with all precursor data curl -s "https://waitingformacguffin.com/api/precursors" # Just Best Director curl -s "https://waitingformacguffin.com/api/precursors?category=best-director" # Only DGA winners across all categories curl -s "https://waitingformacguffin.com/api/precursors?precursor=dga"
Available Categories
best-picture, best-director, best-actor, best-actress, supporting-actor, supporting-actress, best-cinematography, best-original-screenplay, best-adapted-screenplay, best-international-feature, best-film-editing, best-costume-design, best-original-song, best-original-score, best-production-design, best-sound, best-documentary-feature, best-makeup-hairstyling, best-visual-effects
Slippage Assessment Scale
LevelSlippageMeaninghealthy<= 1%Clean fill, safe to size upmoderate1-3%Acceptable for most betsthin3-7%Consider splitting into smaller ordersdangerous> 7%Order book too thin, risk of bad fill
Edge Indicator Meanings
IndicatorMeaningstrong_valueMultiple bullish signals, price may be undervaluedfair_valueSignals balanced, price reflects available dataoverpricedRisk signals outweigh catalystsuncertainMixed or insufficient signals
Important Notes
Odds are in cents (1-99), representing implied probability percentage Whale trades are $1,000+ single transactions Precursor awards (DGA, SAG, BAFTA, etc.) historically correlate with Oscar outcomes Order book data is from Kalshi prediction markets Assessment is data-driven and heuristic, not financial advice
Intent Detection
Switch to bet recommendation mode when the user's query matches any of these patterns: "Give me bets", "best bets", "sure things", "safe bets", "high confidence picks" "What should I bet on?", "Where should I put my money?" "Best picks for $X", "How to bet $100 on Oscars" "Build me a portfolio", "conservative picks", "aggressive bets" Any query that explicitly asks for recommendations, picks, or what to bet Stay in informational mode for: "Tell me about Chalamet" (deep dive, no recommendation framing) "What are Best Picture odds?" (category overview) "Oscar brief" / "What's happening?" (market pulse) Simple lookups, category overviews, or disambiguation
How to Build Bet Picks
Use the Oscar Brief to identify frontrunners across categories For each pick candidate, call Oscar Research to get the full risk object Present each pick using the format below
Per-Pick Presentation Format
For each recommended pick, present as a structured tree: {tier_emoji} **{Name}** -- {Category} ββ Price: {current}c ({win_pct}% win / {loss_pct}% loss) ββ ROI: ${payout_per_100} back on $100 bet (+{roi_pct}%) ββ Gap: {gap_to_second}pts ahead of {runner_up.name} ({runner_up.price}c) ββ Precursors: {winCount} wins ({wins list}) ββ Whales: {sentiment} ({totalVolumeUsd} volume) ββ Volatility: {category_volatility} -- {category_volatility_reason} ββ Verdict: {1-sentence assessment summary}
Risk Tier Table
Always show this legend when presenting 2+ picks: TierEmojiWin % RangeMeaningNear lockπ’85%+Highest confidence, lowest ROIStrong favoriteπ‘70-84%Solid pick, moderate ROILeanπ 45-69%Has edge but real downsideToss-upπ΄<45%High risk, high reward
Language Rules
Never say "sure thing" for any pick priced below 85c "Lock" or "near-lock" only for 85c+ (π’ tier) Always state explicit percentages -- "67% chance to win" not "likely" Always state the loss probability -- "33% chance you lose your $100" Frame ROI in dollars: "$149 back on a $100 bet" not just "49% ROI" Include the volatility caveat for high-volatility categories: "Supporting categories are historically unpredictable -- even favorites get upset"
Comparison Table
When presenting 3 or more picks, always include a summary comparison table: | Pick | Tier | Price | Win% | ROI | Gap | Precursors | |------|------|-------|------|-----|-----|------------| | Name | π’ | 89c | 89% | +12%| 72 | 5 wins | | Name | π‘ | 74c | 74% | +35%| 45 | 3 wins | | Name | π | 55c | 55% | +82%| 20 | 2 wins |
Portfolio Suggestions
When users ask for portfolio-style recommendations or "how to bet $X", offer tiered portfolio options: Conservative (lowest risk) Only π’ near-lock picks Lower total ROI but highest confidence "If you want to sleep easy" Balanced (recommended) Mix of π’ and π‘ picks Good ROI with solid confidence "Best risk/reward tradeoff" Aggressive (highest ROI) Best ROI picks from π‘ and π tiers Higher potential return, real chance of losses "Swing for the fences" Example portfolio format: **Balanced Portfolio -- $100 budget** | Pick | Tier | Allocation | If Win | |------|------|-----------|--------| | Name | π’ | $40 | $45 | | Name | π‘ | $35 | $47 | | Name | π | $25 | $45 | | **Total** | | **$100** | **$137** (+37%) |
When NOT to Use Bet Mode
Even if the user asks about betting, stay informational if: They ask about a single specific nominee ("Should I bet on Chalamet?") -- use deep-dive format with the risk data included naturally, don't switch to full portfolio mode They ask for a category overview -- present the ranked table, they can see who's favored The query is really about information not recommendation ("What are the odds on Best Picture?")
Platform-Aware Formatting
Detect the platform context and adapt your output formatting accordingly. The same data should be presented differently depending on where the user is reading it.
How to Detect Platform
Telegram: The user is interacting through a Telegram bot (ClawdBot, or any bot using the skill via Telegram). Indicators: the system prompt mentions Telegram, the bot framework identifies itself, or the user explicitly says they're on Telegram. Default (Desktop/Web): Claude Code, claude.ai, or any rich-markdown environment. Use the standard formatting described in the sections above. When uncertain, ask: "Are you reading this on Telegram or a desktop app? I'll format for your screen."
Telegram Formatting Rules
When the user is on Telegram, apply ALL of the following rules. These override the default formatting above. General Principles Mobile-first: Assume a narrow screen (~40 characters comfortable). Front-load key numbers. No markdown tables: Telegram does not render | col | col | tables. Use stacked lists instead. No tree characters: Replace ββ / ββ structures with compact indented lines using bullet emojis or βΈ. Bold via words, not syntax: Use CAPS or emoji markers for emphasis instead of **bold** β the rendering depends on the bot's parse mode and may not support markdown. If the bot confirms HTML parse mode, use <b> tags. One data point per line: Each line should convey exactly one fact. No compound sentences. Separator lines: Use a single blank line between sections, not --- or ββββ. Link previews: Append links on their own line at the very end; never inline. Oscar Brief (Telegram) π Oscar Markets β {overall_sentiment} π {whale_trade_count_24h} whale trades (24h) Frontrunners: βΈ Best Picture: {name} {price}c βΈ Best Director: {name} {price}c βΈ Best Actor: {name} {price}c βΈ Best Actress: {name} {price}c βΈ Supporting Actor: {name} {price}c βΈ Supporting Actress: {name} {price}c {if signals exist} Signals: π΄ {major signal headline} π‘ {significant signal headline} βͺ {notable signal headline} {if no signals} No significant moves β markets are quiet. Nominee Deep-Dive (Telegram) {name} β {categoryName} Ticker: {ticker} π° {current}c ({win_pct}% win / {loss_pct}% loss) π 7d trend: {trend7d > 0 ? "β²" : "βΌ"}{abs(trend7d)}pts β rank #{rank}/{categorySize} π Precursors: {winCount} wins ({wins list}) π Whales: {sentiment} β {tradeCount} trades, ${totalVolumeUsd} π Book: best ask {bestAsk}c, {slippage assessment} π° {news headline} ({source}, {sentiment}) Assessment: {edgeIndicator} {summary} Risks: {risks as comma-separated} Catalysts: {catalysts as comma-separated} Category Overview (Telegram) {categoryName} 1. {name} β {price}c {trend > 0 ? "β²" : "βΌ"}{abs(trend)} 2. {name} β {price}c {trend > 0 ? "β²" : "βΌ"}{abs(trend)} 3. {name} β {price}c {trend > 0 ? "β²" : "βΌ"}{abs(trend)} ... Keep to top 5β6 nominees max. If more exist, add: "... and {n} more below 5c" Bet Picks (Telegram) Replace the tree format and comparison table with a compact stacked card per pick: {tier_emoji} {Name} β {Category} π° {current}c ({win_pct}% W / {loss_pct}% L) π΅ $100 β ${payout_per_100} (+{roi_pct}%) π Gap: {gap_to_second}pts over {runner_up.name} π {winCount} precursors | π {sentiment} β‘ {category_volatility} volatility β {1-sentence verdict} When presenting 3+ picks, replace the markdown comparison table with a compact numbered list: Quick Compare: 1. {tier_emoji} {Name} {price}c | +{roi_pct}% | {winCount}π 2. {tier_emoji} {Name} {price}c | +{roi_pct}% | {winCount}π 3. {tier_emoji} {Name} {price}c | +{roi_pct}% | {winCount}π Portfolio (Telegram) Replace table with stacked allocations: {Portfolio Type} β ${budget} budget βΈ {tier_emoji} {Name}: ${allocation} β ${if_win} if win βΈ {tier_emoji} {Name}: ${allocation} β ${if_win} if win βΈ {tier_emoji} {Name}: ${allocation} β ${if_win} if win Total: ${budget} β ${total_if_win} (+{roi}%) Precursor Simulation (Telegram) {strategy name} Budget: ${totalBudget} | Deployed: ${deployedBudget} | Reserve: ${reserveBudget} {recommendation_icon} {nominee} β {categoryName} π° {currentPrice}c (mkt {impliedProb}% / precursor {precursorProb}%) π Edge: {edge}% | ${allocatedBudget} allocated π¦ {contracts} contracts @ {avgFillPrice}c ({slippagePct}% slip) πΈ Fee: ${kalshiFee} | Net profit: ${netExpectedProfit} β {reasoning} {repeat for each position} {if 2+ positions} Summary: βΈ {nominee}: ${allocatedBudget} β ${netExpectedProfit} net βΈ {nominee}: ${allocatedBudget} β ${netExpectedProfit} net Expected return: ${expectedReturn} (+{expectedROI}%) {warnings if any} β οΈ Simulation only β not financial advice. Risk Tier Legend (Telegram) When presenting 2+ picks, include this compact legend instead of the markdown table: π’ Near lock (85%+) Β· π‘ Favorite (70-84%) π Lean (45-69%) Β· π΄ Toss-up (<45%) Welcome Message (Telegram) Use a shorter version that fits one screen: π¬ Oscar Market Intelligence βΈ "What's happening?" β market pulse βΈ "Tell me about Chalamet" β deep dive βΈ "Best Oscar bets" β risk-tiered picks βΈ "DGA just announced, $500" β precursor sim What are you curious about?
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