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lightweight-calculation

Use this skill for small deterministic calculations during research when pandas/table analysis is unnecessary. Triggers: "calculate", "arithmetic", "unit conversion", "percentage point", "expected value", "weighted average", "range", "ratio", "sanity check", "implied value", "probability conversion". Outputs: concise calculation notes, JSON snippets, or Markdown bullets returned in your ResearchNotes for later synthesis.

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Dépôt
NVIDIA-AI-Blueprints/aiq
Dernière activité de la source
25 juin 2026 à 18:18
Langue détectée de SKILL.md
anglais
Étoiles
873
Forks
265

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SKILL.md
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name
lightweight-calculation
description
Use this skill for small deterministic calculations during research when pandas/table analysis is unnecessary. Triggers: "calculate", "arithmetic", "unit conversion", "percentage point", "expected value", "weighted average", "range", "ratio", "sanity check", "implied value", "probability conversion". Outputs: concise calculation notes, JSON snippets, or Markdown bullets returned in your ResearchNotes for later synthesis.
# Lightweight Calculation Skill Use this skill when the research task needs a small reproducible calculation but does not need full table normalization. Keep the calculation narrow and source-grounded. ## Required Execution Standard 1. Identify the exact input values and their source references. 2. Use `execute` with a short Python script for arithmetic, ratios, probability conversion, expected value, weighted averages, confidence/range arithmetic, or unit conversion. 3. Do not hand-compute values in prose when the arithmetic affects a finding. 4. Use `/workspace` for sandbox-local files. Sandbox code cannot read or write `/shared/` directly. 5. Return the final result in your `ResearchNotes` after the successful `execute` call (not via `write_file`); `run_research_batch` persists your returned notes to `/shared/`. 6. State assumptions, rounding rules, missing inputs, and source references. ## Execution Pattern 1. Gather the relevant figures from source-tool output or research notes. 2. Run a compact Python calculation with explicit variables. 3. Inspect output and fix any code issue before using the result. 4. Include a short calculation summary (Markdown or JSON) in your `ResearchNotes` for synthesis. 5. Cite the original source IDs in the eventual `ResearchFinding`; the calculation artifact is supporting work, not a substitute for sources. ## Python Template ```python from decimal import Decimal, ROUND_HALF_UP inputs = { "market_probability": Decimal("0.62"), "payout_if_yes": Decimal("1.00"), "price": Decimal("0.62"), } expected_value = inputs["market_probability"] * inputs["payout_if_yes"] - inputs["price"] percentage = (inputs["market_probability"] * Decimal("100")).quantize( Decimal("0.1"), rounding=ROUND_HALF_UP, ) print(f"Implied probability: {percentage}%") print(f"Expected value per $1 payout contract: {expected_value:.3f}") print("Assumptions: probability and price are current source values.") ``` ## Output Guidance Keep the saved artifact short: ```markdown # Calculation Check: [topic] - Inputs: ... - Formula: ... - Result: ... - Rounding: ... - Caveats: ... ```
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