Agent skill

Waitingformacguffin

by LeoYeAI in LeoYeAI/openclaw-master-skills

Oscar prediction market intelligence from waitingformacguffin.com.

MITAuto-check passed

Install Waitingformacguffin

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill waitingformacguffin -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills waitingformacguffin --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/waitingformacguffin .claude/skills/waitingformacguffin && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
waitingformacguffin
GitHub stars
2.2k
Token cost
~6.5k tokens
SKILL.md length
1,853 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Oscar prediction market intelligence from waitingformacguffin.com.

  • Works in 6 steps: Category slug or name: "best-picture" or… → Exact name: "Timothee Chalamet"… → Substring: "Chalamet" finds "Timothee… → …
  • User asks about Oscar markets
  • SKILL.md covers Welcome Message, Tool 1: Oscar Brief, Tool 2: Oscar Research and Tool 3: Oscar Precursor…, plus 3 more sections
  • Calls curl; reaches waitingformacguffin.com

What it does

Waitingformacguffin is an agent skill from LeoYeAI/openclaw-master-skills. Oscar prediction market intelligence from waitingformacguffin.com. Get live odds, whale activity, price movements, precursor awards, order book depth, and frontrunner changes across all 19 Oscar categories. Use when user asks about Oscar markets, betting odds, nominees, or wants a market update.

Its SKILL.md is about 6.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • User asks about Oscar markets
  • Wants a market update

Example prompts

  • “/waitingformacguffin”

Requirements

  • Pre-approved tools (allowed-tools): Bash(curl *), Read

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Category slug or name: "best-picture" or "Best Picture" returns category overview
  2. Exact name: "Timothee Chalamet" (case-insensitive)
  3. Substring: "Chalamet" finds "Timothee Chalamet"
  4. Diacritics-normalized: "Timothee" matches "Timothee"
  5. Film title: matches against the film database
  6. Typo correction: "Chalmet" resolves via Levenshtein (edit distance <= 3)

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(curl *)
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • waitingformacguffin.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Waitingformacguffin loads about 6.5k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 1,853 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~6.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,853 words, ~6,533 tokens.

Download SKILL.mdSave it as .claude/skills/waitingformacguffin/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
waitingformacguffin
description
Oscar prediction market intelligence from waitingformacguffin.com. Get live odds, whale activity, price movements, precursor awards, order book depth, and frontrunner changes across all 19 Oscar categories. Use when user asks about Oscar markets, betting odds, nominees, or wants a market update.
allowed-tools
Bash(curl *), Read
homepage
https://github.com/sonderspot/waitingformacguffin-public
metadata.version
1.3.0
metadata.last_updated
2026-03-13

WaitingForMacGuffin -- Oscar Market Intelligence

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
bash
curl -s "https://waitingformacguffin.com/api/oscar/brief?hours=24&sensitivity=medium"
Parameters
ParamTypeDefaultDescription
hoursnumber24Lookback 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
json
{
  "signals": [
    {
      "type": "price_move | whale_trade | frontrunner_change | news_sentiment",
      "category": "best-actor",
      "categoryName": "Best Actor",
      "severity": "major | significant | notable | info",
      "headline": "Chalamet ▼ 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
bash
# 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
bash
curl -s "https://waitingformacguffin.com/api/oscar/research?query=Chalamet"
Parameters
ParamTypeDefaultDescription
querystring(required)Nominee name, film title, or category slug. Supports fuzzy matching.
include_orderbookbooleantrueInclude order book depth and slippage analysis
budget_for_slippagenumber500USD amount for slippage calculation (100-100000)
categorystring(optional)Category slug to narrow disambiguation
Query Resolution

The query is fuzzy-matched automatically:

  1. Category slug or name: "best-picture" or "Best Picture" returns category overview
  2. Exact name: "Timothee Chalamet" (case-insensitive)
  3. Substring: "Chalamet" finds "Timothee Chalamet"
  4. Diacritics-normalized: "Timothee" matches "Timothee"
  5. Film title: matches against the film database
  6. Typo correction: "Chalmet" resolves via Levenshtein (edit distance <= 3)
Three Response Modes

1. Nominee deep-dive (mode: "nominee") -- single match:

json
{
  "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": "🟠",
    "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:

json
{
  "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:

json
{
  "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:

  1. Name, category, and ticker
  2. Odds: current price, implied probability, 7d trend (with arrow), rank
  3. Precursors: list wins with award names
  4. Whale activity: trade count, total volume, directional sentiment
  5. Order book: best ask, depth, slippage at the user's budget
  6. News: relevant headlines with source and sentiment
  7. 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
bash
# 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
bash
curl -s "https://waitingformacguffin.com/api/oscar/simulate?precursor=dga&budget=500"
Parameters
ParamTypeDefaultDescription
precursorstring(required)Which precursor to base the simulation on: dga, sag, pga, critics-choice, golden-globes, bafta, or all
budgetnumber(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 For
Conservative50% 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
json
{
  "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
LabelCriteriaIcon
strong_buyEdge 20%+ AND slippage <= 3%!!!
buyEdge 10%+!!
speculativeEdge > 0%!
skipNo edge or below risk threshold--
How to Present Results
  1. Lead with strategy summary: precursor name, budget, risk tolerance, deployed vs reserve
  2. Show each position as a structured block:
    {recommendation_icon} **{nominee}** -- {categoryName}
    ├─ Price: {currentPrice}c (market says {impliedProb}% / precursor says {precursorProb}%)
    ├─ Edge: {edge}% | Allocated: ${allocatedBudget}
    ├─ Fill: {contracts} contracts @ {avgFillPrice}c avg ({slippagePct}% slippage)
    ├─ Kalshi fee: ${kalshiFee} | Net expected profit: ${netExpectedProfit}
    └─ {reasoning}
  3. Summary table for 2+ positions
  4. Show warnings if any (liquidity issues, missing data)
  5. Always show disclaimer
Examples
bash
# DGA just announced — 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
bash
curl -s "https://waitingformacguffin.com/api/precursors"
Parameters
ParamTypeDefaultDescription
categorystringall big 6Single category slug to filter
precursorstringallSingle 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
bash
# 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

LevelSlippageMeaning
healthy<= 1%Clean fill, safe to size up
moderate1-3%Acceptable for most bets
thin3-7%Consider splitting into smaller orders
dangerous> 7%Order book too thin, risk of bad fill

Edge Indicator Meanings

IndicatorMeaning
strong_valueMultiple bullish signals, price may be undervalued
fair_valueSignals balanced, price reflects available data
overpricedRisk signals outweigh catalysts
uncertainMixed 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

Bet Recommendation Mode

Show full SKILL.md (766 more words)Show less
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
  1. Use the Oscar Brief to identify frontrunners across categories
  2. For each pick candidate, call Oscar Research to get the full risk object
  3. Present each pick using the format below
Per-Pick Presentation Format

For each recommended pick, present as a structured tree:

{tier_emoji} **{Name}** -- {Category}
├─ Price: {current}c ({win_pct}% win / {loss_pct}% loss)
├─ ROI: ${payout_per_100} back on $100 bet (+{roi_pct}%)
├─ Gap: {gap_to_second}pts ahead of {runner_up.name} ({runner_up.price}c)
├─ Precursors: {winCount} wins ({wins list})
├─ Whales: {sentiment} ({totalVolumeUsd} volume)
├─ Volatility: {category_volatility} -- {category_volatility_reason}
└─ Verdict: {1-sentence assessment summary}
Risk Tier Table

Always show this legend when presenting 2+ picks:

TierEmojiWin % RangeMeaning
Near lock🟢85%+Highest confidence, lowest ROI
Strong favorite🟡70-84%Solid pick, moderate ROI
Lean🟠45-69%Has edge but real downside
Toss-up🔴<45%High risk, high reward
Language Rules
  • Never say "sure thing" for any pick priced below 85c
  • "Lock" or "near-lock" only for 85c+ (🟢 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 | 🟢   | 89c   | 89%  | +12%| 72  | 5 wins     |
| Name | 🟡   | 74c   | 74%  | +35%| 45  | 3 wins     |
| Name | 🟠   | 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 🟢 near-lock picks
  • Lower total ROI but highest confidence
  • "If you want to sleep easy"

Balanced (recommended)

  • Mix of 🟢 and 🟡 picks
  • Good ROI with solid confidence
  • "Best risk/reward tradeoff"

Aggressive (highest ROI)

  • Best ROI picks from 🟡 and 🟠 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 | 🟢   | $40       | $45    |
| Name | 🟡   | $35       | $47    |
| Name | 🟠   | $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
  1. Mobile-first: Assume a narrow screen (~40 characters comfortable). Front-load key numbers.
  2. No markdown tables: Telegram does not render | col | col | tables. Use stacked lists instead.
  3. No tree characters: Replace ├─ / └─ structures with compact indented lines using bullet emojis or ▸.
  4. Bold via words, not syntax: Use CAPS or emoji markers for emphasis instead of **bold** — the rendering depends on the bot's parse mode and may not support markdown. If the bot confirms HTML parse mode, use <b> tags.
  5. One data point per line: Each line should convey exactly one fact. No compound sentences.
  6. Separator lines: Use a single blank line between sections, not --- or ────.
  7. Link previews: Append links on their own line at the very end; never inline.
Oscar Brief (Telegram)
📊 Oscar Markets — {overall_sentiment}
🐋 {whale_trade_count_24h} whale trades (24h)

Frontrunners:
▸ Best Picture: {name} {price}c
▸ Best Director: {name} {price}c
▸ Best Actor: {name} {price}c
▸ Best Actress: {name} {price}c
▸ Supporting Actor: {name} {price}c
▸ Supporting Actress: {name} {price}c

{if signals exist}
Signals:
🔴 {major signal headline}
🟡 {significant signal headline}
⚪ {notable signal headline}

{if no signals}
No significant moves — markets are quiet.
Nominee Deep-Dive (Telegram)
{name} — {categoryName}
Ticker: {ticker}

💰 {current}c ({win_pct}% win / {loss_pct}% loss)
📈 7d trend: {trend7d > 0 ? "▲" : "▼"}{abs(trend7d)}pts — rank #{rank}/{categorySize}
🏆 Precursors: {winCount} wins ({wins list})
🐋 Whales: {sentiment} — {tradeCount} trades, ${totalVolumeUsd}
📖 Book: best ask {bestAsk}c, {slippage assessment}
📰 {news headline} ({source}, {sentiment})

Assessment: {edgeIndicator}
{summary}

Risks: {risks as comma-separated}
Catalysts: {catalysts as comma-separated}
Category Overview (Telegram)
{categoryName}

1. {name} — {price}c {trend > 0 ? "▲" : "▼"}{abs(trend)}
2. {name} — {price}c {trend > 0 ? "▲" : "▼"}{abs(trend)}
3. {name} — {price}c {trend > 0 ? "▲" : "▼"}{abs(trend)}
...

Keep to top 5–6 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} — {Category}
💰 {current}c ({win_pct}% W / {loss_pct}% L)
💵 $100 → ${payout_per_100} (+{roi_pct}%)
📊 Gap: {gap_to_second}pts over {runner_up.name}
🏆 {winCount} precursors | 🐋 {sentiment}
⚡ {category_volatility} volatility
→ {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}🏆
2. {tier_emoji} {Name} {price}c | +{roi_pct}% | {winCount}🏆
3. {tier_emoji} {Name} {price}c | +{roi_pct}% | {winCount}🏆
Portfolio (Telegram)

Replace table with stacked allocations:

{Portfolio Type} — ${budget} budget

▸ {tier_emoji} {Name}: ${allocation} → ${if_win} if win
▸ {tier_emoji} {Name}: ${allocation} → ${if_win} if win
▸ {tier_emoji} {Name}: ${allocation} → ${if_win} if win

Total: ${budget} → ${total_if_win} (+{roi}%)
Precursor Simulation (Telegram)
{strategy name}
Budget: ${totalBudget} | Deployed: ${deployedBudget} | Reserve: ${reserveBudget}

{recommendation_icon} {nominee} — {categoryName}
💰 {currentPrice}c (mkt {impliedProb}% / precursor {precursorProb}%)
📊 Edge: {edge}% | ${allocatedBudget} allocated
📦 {contracts} contracts @ {avgFillPrice}c ({slippagePct}% slip)
💸 Fee: ${kalshiFee} | Net profit: ${netExpectedProfit}
→ {reasoning}

{repeat for each position}

{if 2+ positions}
Summary:
▸ {nominee}: ${allocatedBudget} → ${netExpectedProfit} net
▸ {nominee}: ${allocatedBudget} → ${netExpectedProfit} net
Expected return: ${expectedReturn} (+{expectedROI}%)

{warnings if any}

⚠️ Simulation only — not financial advice.
Risk Tier Legend (Telegram)

When presenting 2+ picks, include this compact legend instead of the markdown table:

🟢 Near lock (85%+) · 🟡 Favorite (70-84%)
🟠 Lean (45-69%) · 🔴 Toss-up (<45%)
Welcome Message (Telegram)

Use a shorter version that fits one screen:

🎬 Oscar Market Intelligence

▸ "What's happening?" — market pulse
▸ "Tell me about Chalamet" — deep dive
▸ "Best Oscar bets" — risk-tiered picks
▸ "DGA just announced, $500" — precursor sim

What are you curious about?

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/waitingformacguffin of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Waitingformacguffin next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Genomic IntelligenceK-Dense-AI/scientific-agent-skills48k1 repos~6.2kAutomated safety check: NotesMIT
Autopilot Predictruvnet/ruflo74k—~337Automated safety check: PassMIT
Lead Intelligenceaffaan-m/ECC276k—~1.5kAutomated safety check: PassMIT
Threat Intelligencesickn33/agentic-awesome-skills47k1 repos~1.1kAutomated safety check: PassMIT

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Questions about Waitingformacguffin

What does Waitingformacguffin do?

Oscar prediction market intelligence from waitingformacguffin.com. Waitingformacguffin is an agent skill from LeoYeAI/openclaw-master-skills.com.

When should I use Waitingformacguffin?

Waitingformacguffin fits situations like: user asks about Oscar markets; wants a market update.

How do I install Waitingformacguffin in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill waitingformacguffin -a claude-code`. Or copy the skill folder (skills/waitingformacguffin in LeoYeAI/openclaw-master-skills) into .claude/skills/waitingformacguffin in your project. Claude Code loads it when a task matches its description.

How do I install Waitingformacguffin in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill waitingformacguffin -a codex`. Or copy the skill folder (skills/waitingformacguffin in LeoYeAI/openclaw-master-skills) into .agents/skills/waitingformacguffin in your project. Codex loads it when a task matches its description.

Can I use Waitingformacguffin in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill waitingformacguffin -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/waitingformacguffin, .gemini/skills/waitingformacguffin, .github/skills/waitingformacguffin and .opencode/skills/waitingformacguffin in your project.

What does Waitingformacguffin need to run?

Going by SKILL.md and its folder, Waitingformacguffin needs the command-line tools its instructions call (curl). Its frontmatter pre-approves these tools: Bash(curl *), Read.

Does Waitingformacguffin access the network?

SKILL.md names 1 domain. In commands or code: waitingformacguffin.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Waitingformacguffin safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Waitingformacguffin use?

Waitingformacguffin is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Waitingformacguffin use?

About 6.5k tokens (SKILL.md is roughly 26k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Waitingformacguffin?

Skills that share tags, products or a category with Waitingformacguffin: Prediction Market Oracle Research (affaan-m/ECC, 276k stars), Genomic Intelligence (K-Dense-AI/scientific-agent-skills, 48k stars), Autopilot Predict (ruvnet/ruflo, 74k stars) and Lead Intelligence (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Waitingformacguffin?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.