Analytical thinking
Structured thinking skills for AI agents: brainstorming, decision support, systems thinking, and strategy. Built for Claude Code & Cursor.
npx -y skills add ysskrishna/ai-agent-skills --skill analytical-thinkingAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Use this skill when the user asks for analytical thinking (including naming it or directing use/apply/run with obvious misspellings; decisive) or wants a structured breakdown—decomposing the problem, defining metrics and hypotheses, organizing evidence, and synthesizing findings with explicit uncertainty. Use for quant-style reasoning framing, root-cause trees, decision tables, or comparable structure, including informal or incomplete data asks. Skip when they want open-ended idea spray with no measurement or hypothesis angle, or a short verdict with no decomposition requested.
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SKILL.md
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Analytical Thinking
Clarity beats cleverness. End with answers tied to structure and stated confidence.
How to run it with this skill: one clearly headed section per step in this order: Frame → Decompose → Hypotheses → Evidence → Synthesis. Insert Options matrix only when Setup calls for it (after Evidence, before Synthesis).
Setup (run before starting)
In one short block:
- Analytical question — precise, ideally falsifiable
- Default pass — Frame → Decompose → Hypotheses → Evidence → Synthesis (state this line)
If data availability or definitions are missing, ask at most 3 questions in one message, then proceed. Note any remaining gaps or working guesses in plain language (no bracket tags in Setup).
If the user is choosing among concrete alternatives, after Evidence insert Options matrix: rows = options, columns = criteria (state weights if any), qualitative scores (− / 0 / +) with one-line justification per cell — then finish with Synthesis.
The Steps
Frame
Question type (estimate, compare, explain, predict, optimize). Unit of analysis and baseline (even if hypothetical).
Decompose
Tree or table: factors, drivers, or workstreams. Each child node should be MECE-ish (mutually exclusive where it matters; collectively exhaustive enough for the decision).
Hypotheses
Ranked H1, H2, H3 — what would we expect to observe if each were true? What would falsify each?
Evidence
For each hypothesis: Observation: … — Strength note: one short sentence on how much this observation supports or undermines the hypothesis and the main limit (no Strong/Moderate/Weak labels). Caveat: …
If no real data, run a thought experiment section instead — label bullets [THEORETICAL].
Synthesis
- Answer — direct response to the analytical question
- Key uncertainty — what single unknown swings the answer most
- Next data / step — what to collect or run next
Execution Rules
- Do not conflate Hypotheses and Evidence in the same bullet list.
- Numbers: if inputs are guessed, show ranges and label
[ESTIMATED]. - Prefer structure over long prose.
Checklist (verify before responding)
- Setup: analytical question + default pass (note if Options matrix used)
- Frame states question type and baseline
- Decompose is scannable (tree or table)
- Hypotheses have falsifiers
- Evidence (or
[THEORETICAL]) mapped to hypotheses - Synthesis: answer, uncertainty, next step