Skill: skybinjf/kalshi-politics-random-buyer (version 1.0.2)

--- name: kalshi-politics-random-buyer description: Dry-run Kalshi skill that finds politics-related markets, picks a valid candidate at random, runs Simmer context checks, and proposes a trade plan without placing a real order. metadata: author: "GitHub Copilot" version: "1.0.0" displayName: "Kalshi Politics Random Buyer" difficulty: "intermediate" --- # Kalshi Politics Random Buyer This skill scans Kalshi for politics-related markets, randomizes the candidate pool, checks Simmer context safeguards, and prints a manual-confirmation trade plan for one valid candidate. > **This is a template.** The default signal is intentionally simple: find a politics market at random, then only keep it if context and edge checks still pass. Remix the query set, side logic, price filters, and sizing model with your own thesis. ## What It Does On each run, the skill: 1. Searches Kalshi importable markets using politics-related queries. 2. Falls back to a global Kalshi scan when keyword searches return nothing. 3. Filters for politics candidates with usable tickers and URLs. 4. Ignores markets outside a configurable price band. 5. Randomizes the candidate list. 6. Ensures each candidate is indexed in Simmer using check-then-import. 7. Fetches Kalshi market context and skips risky candidates. 8. Picks YES or NO using a simple fair-probability edge rule. 9. Sizes the hypothetical trade with `simmer_sdk.sizing.size_position()`. 10. Prints a structured execution plan and reasoning for human review. ## Important Limitation This template is intentionally non-executing. - Passing `--live` is rejected. - No real order is sent. - No wallet private key is needed for the default workflow. - The output is a manual-review execution plan, not an order. ## Required Files - `SKILL.md` - `clawhub.json` - `trade_skill.py` ## Environment Variables ### Credentials - `SIMMER_API_KEY` (required): Your Simmer API key. ### Strategy Config - `SEARCH_QUERIES`: Comma-separated politics search terms. Default: `election,president,presidency,senate,house,governor,politics,campaign,ballot,nominee,party` - `MAX_MARKETS_PER_QUERY`: Maximum Kalshi results to inspect per query. Default: `50` - `MIN_PRICE`: Minimum YES price allowed. Default: `0.02` - `MAX_PRICE`: Maximum YES price allowed. Default: `0.98` - `FAIR_PROBABILITY`: Fair YES probability for edge checks. Default: `0.55` - `MIN_EDGE`: Minimum edge required to produce a plan. Default: `0.02` - `MAX_SLIPPAGE_PCT`: Skip candidates with excessive slippage. Default: `0.15` - `RANDOM_SEED`: Optional integer seed for reproducible selection. ## Safety Model - Manual-confirmation only. - Uses Simmer context before proposing a trade. - Skips severe flip-flop, HOLD/SKIP recommendations, resolved markets, and excessive slippage. - Uses bankroll-aware sizing instead of a hard-coded stake. - Avoids publishing wallet identifiers in reasoning. ## Local Usage Default planning run: ```bash export SIMMER_API_KEY="sk_live_..." python trade_skill.py ``` Deterministic planning run: ```bash export RANDOM_SEED="7" python trade_skill.py ``` Custom politics search: ```bash export SEARCH_QUERIES="president,election,governor" export FAIR_PROBABILITY="0.60" python trade_skill.py ``` ## Remix Ideas - Replace random candidate selection with volume or liquidity ranking. - Add event-level filters for US elections only. - Add position-awareness to avoid repeat exposure. - Convert the dry-run plan into a proposal file instead of stdout.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)