Skill v1.0.0
currentLLM-judged scan90/100version: "1.0.0" name: forecasting description: Superforecasting with calibrated reasoning, Brier score tracking, and prediction ledger management consumer: cc_background_research phase: 7 skill_type: uplift
Forecasting
Purpose
Make specific, falsifiable predictions with calibrated confidence levels. Track accuracy over time using Brier scores. Apply superforecasting methodology (Tetlock/Good Judgment Project) to any domain — technology trends, project outcomes, market shifts, competitive moves, risk assessment.
When to Use
- User asks for a prediction or forecast on any topic.
- Strategic reflection identifies a decision that depends on uncertain futures.
- Surplus compute is available and a prediction review is due.
- A previously made prediction is approaching its resolution date.
- Deep reflection surfaces a trend worth formally tracking.
Superforecasting Principles
- Triage — Focus on questions where effort improves accuracy. Ignore
questions that are either trivially knowable or fundamentally unknowable.
- Fermi decomposition — Break big questions into smaller, estimable
components. "Will X happen?" → "What's the base rate? What's different this time? What signals would I expect to see?"
- Balance inside and outside views — Start with the reference class
(base rate from historical analogues), then adjust with specific evidence. Never skip the outside view.
- Update incrementally — Bayesian updating. New evidence shifts
confidence by small amounts, not dramatic swings. Avoid overreaction.
- Calibration over precision — A well-calibrated 60% is better than
an overconfident 90%. Your 70% predictions should come true ~70% of the time.
- Distinguish noise from signal — Most new information is noise.
Ask: does this actually change the probability, or does it just feel important because it's recent?
- Consider contrarian views — Actively seek evidence against your
current position. What must be true for the opposite outcome?
- Post-mortem every resolution — When a prediction resolves, analyze
WHY you were right or wrong, not just whether. Update process, not just beliefs.
- Express uncertainty numerically — "Likely" is ambiguous. 70% is not.
Use the probability scale below.
- Separate confidence from conviction — High confidence (90%) means
high probability. Strong conviction means you've thought deeply. You can have low confidence with strong conviction (you've analyzed it thoroughly and it's genuinely uncertain).
Signal Taxonomy
| Signal Type | Weight | Description | |
|---|---|---|---|
| Leading indicator | High | Predicts before the event (e.g., job postings predict growth) | |
| Lagging indicator | Medium | Confirms after the event (e.g., quarterly earnings) | |
| Base rate | High | Historical frequency of similar events | |
| Expert opinion | Medium | Domain expert assessment (weight by track record) | |
| Data point | High | Quantitative measurement directly relevant | |
| Anomaly | High | Deviation from expected pattern — investigate | |
| Structural change | Very High | Rules of the game changing (regulation, technology shift) | |
| Sentiment shift | Medium | Public/market mood change (often noise, sometimes signal) |
Signal strength:
- Strong — Multiple independent sources, quantitative, leading, from sources with track record
- Moderate — Single authoritative source, specialist opinion, qualitative
- Weak — Social buzz, anecdote, rumor, single unverified claim
Confidence Scale
| Probability | Meaning | Betting Odds | |
|---|---|---|---|
| 5% | Almost certainly not | 19:1 against | |
| 15% | Very unlikely | ~6:1 against | |
| 25% | Unlikely but plausible | 3:1 against | |
| 35% | Somewhat unlikely | ~2:1 against | |
| 45% | Toss-up, leaning no | ~1.2:1 against | |
| 55% | Toss-up, leaning yes | ~1.2:1 for | |
| 65% | Somewhat likely | ~2:1 for | |
| 75% | Likely | 3:1 for | |
| 85% | Very likely | ~6:1 for | |
| 95% | Almost certain | 19:1 for |
Adjustment rules: +/-5-15% per strong signal, +/-2-5% per moderate signal. If gut says 80% but analysis says 55%, trust the analysis.
Cognitive Bias Checklist
Before finalizing ANY prediction, check against these 8 biases:
| Bias | Check | Fix | |
|---|---|---|---|
| Anchoring | Am I stuck on the first number I thought of? | Re-derive from base rates | |
| Availability | Am I overweighting recent/vivid examples? | Search for boring counterexamples | |
| Confirmation | Am I only finding evidence that agrees? | Explicitly search for disconfirming evidence | |
| Narrative | Am I constructing a compelling story that feels true? | Check: does the data support this without the story? | |
| Overconfidence | Am I more certain than my evidence warrants? | Would I bet real money at these odds? | |
| Scope insensitivity | Am I treating "some" and "a lot" as the same? | Quantify: how much exactly? | |
| Recency | Am I overweighting what happened last? | Check 5-year and 10-year base rates | |
| Status quo | Am I assuming things will stay the same? | What would need to change, and how likely is each change? |
Reasoning Chain Template
For each prediction, construct:
1. Reference Class (Outside View)
- What is the base rate for this type of event?
- 3-5 historical analogues with outcomes
- Starting probability from base rate alone
2. Specific Evidence (Inside View)
- List each signal with type, strength, and direction
- For each signal: percentage adjustment from base rate
- Net adjustment
3. Synthesis
- Start at base rate
- Apply net adjustment
- State final probability with explicit reasoning
4. Key Assumptions
- What must remain true for this prediction to hold?
- For each assumption: conditional probability shift if violated
5. Resolution Criteria
- Exact date or trigger for resolution
- Specific, observable criteria (not subjective)
- Data source for verification
Brier Score
Brier = (predicted_probability - actual_outcome)^2
Where actual_outcome is 0 (didn't happen) or 1 (happened).
| Score | Quality | |
|---|---|---|
| < 0.10 | Excellent | |
| 0.10 - 0.15 | Good | |
| 0.15 - 0.25 | Average | |
| 0.25 | Coin flip (no skill) | |
| > 0.30 | Worse than guessing |
Track cumulative Brier score across all resolved predictions. Review monthly. If cumulative Brier > 0.25, recalibrate methodology.
Contrarian Mode
When explicitly requested or when consensus confidence exceeds 85%:
- Identify the consensus view and its evidence
- Search specifically for counter-consensus evidence
- Ask: "What must be true for the opposite to happen?"
- If contrarian case is credible (>15% probability), include it
- Always label contrarian predictions as such alongside consensus
Domain Source Guides
| Domain | Priority Sources | |
|---|---|---|
| Technology | GitHub trending, HN, arXiv, Crunchbase, job postings, patent filings | |
| Finance | FRED, SEC filings, central bank statements, VIX, yield curves | |
| Geopolitics | UN resolutions, RAND, think tank reports, diplomatic cables | |
| Climate/Energy | IPCC, IEA, CDP, BloombergNEF, utility filings | |
| AI/ML | arXiv, model benchmarks, API pricing trends, conference papers |
Output Format
prediction_id: <PRED-YYYY-MM-DD-NNN>created: <YYYY-MM-DD>domain: <technology | finance | geopolitics | climate | ai_ml | general>time_horizon: <1_week | 1_month | 3_months | 1_year>prediction: <specific, falsifiable statement>confidence: <probability 0.05-0.95>reasoning_chain:reference_class:base_rate: <probability>analogues:- <historical analogue and outcome>specific_evidence:- signal: <description>type: <leading | lagging | base_rate | expert | data | anomaly | structural | sentiment>strength: <strong | moderate | weak>adjustment: <+/- percentage>synthesis: <narrative combining outside and inside views>key_assumptions:- assumption: <what must hold>if_violated: <probability shift>resolution:date: <YYYY-MM-DD>criteria: <exact observable condition>data_source: <where to verify>bias_check: <which biases were checked and adjustments made>status: active | resolved | expiredupdates:- date: <YYYY-MM-DD>old_confidence: <previous>new_confidence: <updated>reason: <what changed>resolution_result:date: <YYYY-MM-DD>outcome: true | falseevidence: <what happened>brier_score: <calculated score>lesson: <what to learn from this>
Prediction Review Schedule
- Weekly: Review all active predictions. Update confidence if new evidence.
- Monthly: Calculate cumulative Brier score. Identify calibration drift.
- On resolution: Score immediately. Post-mortem. Update procedures.
References
- Tetlock, P. (2015). Superforecasting: The Art and Science of Prediction
src/genesis/learning/— Outcome tracking for Brier score integrationsrc/genesis/identity/REFLECTION_STRATEGIC.md— Strategic reflection context