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Prediction Market Live Ops

Community
agiprolabs
prediction-market-live-ops

Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign

Overview

Publisheragiprolabs
Repositoryclaude-trading-skills
Skill nameprediction-market-live-ops
Stars
434
Forks
85
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by agiprolabs on GitHub. Read the source before you install it.

Installation

Install the Prediction Market Live Ops AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/prediction-market-live-ops
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/prediction-market-live-ops .claude/skills/prediction-market-live-ops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prediction Market Live Ops in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prediction Market Live Ops on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prediction Market Live Ops is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prediction-Market Trading Doctrine

Hard-won operational knowledge from a live Kalshi campaign (Aug–Sep 2026: crypto/commodity maker ladders, regime tilts, tennis dust vacuum — all falsified honestly, infrastructure open-sourced). Reference implementation: https://github.com/agiprolabs/kalshi-stack. Complements prediction-market-strategy (edge selection/sizing) — this skill covers the OPERATIONAL side: validating, executing, and killing strategies against a live venue without losing more than the lesson costs.

The prime directive

Every strategy decision is evidence-gated: backtest → paper → live micro-probe → ratchet. Each stage has a PRE-DECLARED kill condition with correctly calibrated probability math, written down BEFORE the data arrives. When a threshold turns out miscalibrated, correct it openly and re-declare — never silently move a goalpost in either direction.

Backtest hygiene (each rule was violated once, expensively)

  • Print-replay EV of passive-fill strategies is an UPPER BOUND, never an estimate. The prints a resting order actually captures are an adversely-selected sample of the tape (sweeps deep enough to reach your queue skew toward true collapses). A tennis dust book that measured +125%/$ on prints went 0-for-78 live (P<2%). Only fill-conditioned live outcomes validate a maker strategy.
  • Time-series joins use the last bar ending ≤ the decision instant. A candle containing the instant is look-ahead and will fabricate covariates that vanish out-of-sample.
  • Taker backtests need print-confirmed executability, not quoted-ask edges — stale quotes create phantom fills.
  • Any bucketed-PnL review includes a top-N-share column. Lottery-payoff books concentrate EV; a "pattern" that is two elephant rounds is noise. Same rule when a holdout "wins": check concentration before believing it.
  • Early settlements are selection-biased toward losers (collapses end fast; comebacks are long matches/rounds). Never read a verdict off the fast-settling prefix.
  • Static tilts that flip sign between tape halves are regime bets, not edges. A conditioning scheme must beat baseline on BOTH halves and on each individual day; pre-register the rules, no per-day fitting.
  • Mirror/complement markets are the SAME bet — deduplicate before computing independence-based probabilities.

Live execution rules (the incident ledger)

  • Smoke-test every live order path with a real 1-lot round trip (place, verify resting, cancel, verify gone) before any strategy trades through it. Paper cannot exercise venue routing parameters. Cost: $0.01.
  • Cancels must be truthful: distinguish cancelled / already-gone (filled!) / failed. A 404 is not a cancel — it means wrong routing shard or a fill-race. Swallowed cancel failures + re-anchor loops = runaway position stacking (cost us $84 in one evening).
  • Route the exchange shard (exchange_index) consistently on EVERY call — placement, reads, cancels, collateral. Collateral must be pre-positioned on the shard before orders.
  • Measure rate limits empirically with dust orders (burst ladder + sustained test). Advertised budgets can be 10× off effective per-order cost. Pace bursts to the measured rate with retry-requeue on 429.
  • post_only for any maker strategy — an "aggressive maker" price on a market trading inside your band silently becomes a taker fill of a different (unvalidated) trade. Tag and cohort maker vs taker fills separately in all realized-EV accounting.
  • Rails, always: halt-file checked before every order batch (cancel-all on appearance), independent scheduler-driven drawdown breaker with deposit-jump detection, per-order and per-market size caps with anomaly ledger events, equity floor stand-down. The breaker baseline is set at session start per the operator's stated loss tolerance.
  • Queue priority is the product in penny/maker books. Synchronized-open venues: NTP + fire at boundary+0 with a retry probe (measure the venue's open-transition latency). Event-driven venues: push-based discovery (lifecycle WebSocket channels) beats polling; a fresh market's book is empty, so first-to-rest owns the level for its lifetime.
  • Restart daemons at activity boundaries; mid-round restarts orphan in-memory state (untracked orders, missed settlements).
  • Geofencing is real: order-origin IP matters, categories differ (sports/elections vs financials), and enforcement can change overnight. Datacenter location is part of your compliance posture.

Scaling doctrine

  • House-money ratchet: double size only when realized profit at the current step covers the next step's incremental risk AND round-count and EV-continuity gates pass. Drop back a step on trailing degradation.
  • Measure the capacity curve (EV/$ by per-print cap) before believing any scale projection; then the binding constraint is capture rate × measured pool, and capture rate is only measurable live.
  • Fill quality is the untested link at every new size — the paper/live EV-per-$ ratio at each step gates the next.

Experiment operations

  • Cohort every fill (maker/taker, band, source) at write time — post-hoc attribution is what lets a bug contaminate a verdict.
  • Ship one variable at a time; changing size and placement logic in the same window destroys attribution.
  • Run A/B variants in paper against a frozen baseline (small-footprint variant, shared rounds, EV/premium-$ as the scale-free judge).
  • Log everything to append-only ledgers; reconcile realized PnL to the venue's own fills/settlements, never to internal marks.
  • Post results (wins AND falsifications) to the operator's channel; write a decision record with evidence and reversal conditions for every ship, retire, and kill.

Frequently asked questions

What does the Prediction Market Live Ops AI skill do?

Use when building, backtesting, operating, or scaling automated trading on prediction markets (Kalshi or similar) — evidence-gated methodology, live-execution safety rails, and falsification protocols distilled from a real live-money campaign

Why use Prediction Market Live Ops on TypingMind?

Because you install it once and use it with any model. Prediction Market Live Ops is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prediction Market Live Ops in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/prediction-market-live-ops. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prediction Market Live Ops?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prediction Market Live Ops?

As many as you like. As long as a model supports skills, you can use Prediction Market Live Ops with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prediction Market Live Ops AI skill free?

Yes. It is published on GitHub by agiprolabs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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