# Thinking Probabilistic > Use when stating a forecast, estimate, or risk. Anchor on the base rate, give a confidence range instead of a point, and update the number when new evidence arrives. - **Type:** Skill - **Install:** `agentstack add skill-tjboudreaux-cc-thinking-skills-thinking-probabilistic` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [tjboudreaux](https://agentstack.voostack.com/s/tjboudreaux) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [tjboudreaux](https://github.com/tjboudreaux) - **Source:** https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-probabilistic ## Install ```sh agentstack add skill-tjboudreaux-cc-thinking-skills-thinking-probabilistic ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Probabilistic Thinking ## Overview Probabilistic thinking, informed by Philip Tetlock's "Superforecasting," treats a forecast as a probability and a range rather than a single confident number. Three moves do almost all the work: **anchor on the base rate**, **express the estimate as a range** (not a point), and **update the number** when new evidence arrives. **Core Principle:** Start from how often similar things happen, state your estimate as a range with a confidence level, and move the number — explicitly — when the evidence moves. > **Stateless-agent note.** Across a single task you have no persistent prediction log, so there is no "track my calibration over months" step here. The leverage is in the *act* of estimating: base rate, range, update. Apply the calibration *attitude* (assume you're overconfident; widen the range) without pretending to keep a cross-session scorecard you don't have. ## When to Use - Stating a timeline or effort estimate - Assessing the risk of an action (migration, deploy, change) - Predicting an outcome (will this fix work? will this launch hit the target?) - Evaluating an uncertain technical choice - Any time you're about to give a confident single number you can't actually be sure of Decision flow: ``` About to state a forecast/estimate/risk? → Outcome genuinely uncertain? → yes → BASE RATE, then a RANGE (not a point) → New evidence since last estimate? → yes → UPDATE THE NUMBER → Can you just look it up / measure it? → yes → DO THAT INSTEAD ``` ## When NOT to Use - **The quantity is knowable.** If you can measure it, query it, or look it up, do that — don't dress a checkable fact as a probability. - **A single piece of evidence updates a single prior.** That's the narrower mechanics of `thinking-bayesian`; use it for the explicit prior × likelihood-ratio update. - **The decision doesn't depend on the number.** If you'd act the same across the plausible range, skip the estimate and act. - **You'd be inventing the base rate.** If there's no real reference class, say the estimate is a guess rather than manufacturing false precision. ## Core Concepts ### Probability as Confidence Convert vague language to numbers: | Vague Statement | Probability Range | |-----------------|-------------------| | "Certain" | 99%+ | | "Almost certain" | 90-99% | | "Very likely" | 80-90% | | "Likely" / "Probable" | 65-80% | | "Better than even" | 55-65% | | "Toss-up" | 45-55% | | "Unlikely" | 20-35% | | "Very unlikely" | 10-20% | | "Almost impossible" | 1-10% | | "Impossible" | These are sanity checks you apply *now*, within the task — not a longitudinal tracking exercise. ### The Equivalent Bet Test "Would I bet at these odds?" ``` Prediction: 80% confident project finishes on time Equivalent: Would I bet $4 to win $1? If that feels wrong, adjust the probability. ``` ### The Outside View Always check base rates: ``` Inside view: "Our team is great, we'll definitely finish on time" Outside view: "What % of similar projects finished on time?" Inside tends toward overconfidence Outside provides calibration anchor ``` ### The Pre-Mortem Adjustment Imagine failure, then adjust: ``` Initial estimate: 85% success After pre-mortem: Identified 5 failure modes I hadn't considered Adjusted estimate: 70% ``` ### The Confidence Interval Check Are your intervals too narrow? ``` Test: Of your 90% confidence intervals, do 90% contain the actual? Common finding: Only 60-70% do Fix: Widen intervals by 50% ``` ## Application Examples ### Project Estimation ```markdown ## Project: Payment System Rewrite Timeline estimate: - 50% confidence: 8-12 weeks - 80% confidence: 6-16 weeks - 95% confidence: 4-24 weeks Key variables: - API complexity: High uncertainty (+/- 3 weeks) - Team availability: Medium uncertainty (+/- 2 weeks) - Integration testing: High uncertainty (+/- 4 weeks) Commitment: "We're 80% confident we'll deliver in Q2" ``` ### Risk Assessment ```markdown ## Risk: Database migration causes extended downtime Probability assessment: - Base rate for similar migrations: 20% have issues - Our preparation level: Above average (-5%) - Complexity of our schema: Above average (+5%) - Rollback plan quality: Strong (-5%) Estimate: 15% probability of extended downtime Mitigation value: - If issue occurs: 4 hours downtime × $10K/hour = $40K - Expected loss: 15% × $40K = $6K - Mitigation cost: $3K for additional testing - Decision: Mitigation worth it (ROI positive) ``` ### Technical Decision ```markdown ## Decision: Adopt new framework Success probability factors: | Factor | Probability | Weight | |--------|-------------|--------| | Team learns quickly | 70% | 0.3 | | Framework matures | 80% | 0.2 | | Performance meets needs | 60% | 0.3 | | Integration works | 75% | 0.2 | Combined probability (simplified): 0.70 × 0.80 × 0.60 × 0.75 = 25% (if all must succeed) OR weighted average: 70% (if partial success acceptable) Decision: High uncertainty suggests pilot first ``` ## Probabilistic Thinking Template ```markdown # Probabilistic Assessment: [Prediction] ## Prediction [Clear, falsifiable statement with timeframe] ## Initial Probability Estimate: [X]% Base rate: [Similar events: Y%] Adjustment rationale: [Why different from base rate] ## Confidence Interval - 50% CI: [Range] - 80% CI: [Range] - 95% CI: [Range] ## Key Uncertainties | Uncertainty | If positive | If negative | |-------------|-------------|-------------| | [Factor 1] | +X% | -Y% | | [Factor 2] | +X% | -Y% | ## Updates (within this task) | New information | Old P | New P | |-----------------|-------|-------| | | | | ## Checkable Outcome [The specific observation that will prove this forecast right or wrong] ``` ## Verification Checklist - [ ] Expressed prediction as specific probability - [ ] Checked base rate for similar events - [ ] Created appropriate confidence intervals - [ ] Identified key uncertainties and their impacts - [ ] Stated the prediction so it's checkable (claim + timeframe + range) - [ ] Applied equivalent bet test for sanity check - [ ] Willing to update the number when new information arrives ## Key Questions - "What probability would I assign to this?" - "What's the base rate for similar things?" - "What would change my estimate up or down?" - "Am I being overconfident? (Usually yes — widen the range)" - "Have I given a range, or am I hiding uncertainty behind a single number?" - "Would I bet at these odds?" ## Tetlock's Superforecaster Traits 1. **Update often:** Change the number when evidence changes 2. **Granular probabilities:** Use 65% not "likely" 3. **Outside view:** Start with base rates 4. **Seek disconfirming evidence:** Look for reasons you're wrong 5. **Ranges, not points:** Express confidence as an interval, and widen it 6. **Intellectual humility:** Assume you're often wrong ## Tetlock's Wisdom "The fox knows many things, but the hedgehog knows one big thing." Superforecasters are foxes—they integrate many perspectives, update frequently, and avoid ideological certainty. They're not smarter; they're more calibrated. "Beliefs are hypotheses to be tested, not treasures to be protected." Your predictions should change as evidence changes. Holding steady when you should update is a calibration failure. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [tjboudreaux](https://github.com/tjboudreaux) - **Source:** [tjboudreaux/cc-thinking-skills](https://github.com/tjboudreaux/cc-thinking-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-tjboudreaux-cc-thinking-skills-thinking-probabilistic - Seller: https://agentstack.voostack.com/s/tjboudreaux - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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