Reasoning

Route to cognitive mode. Execute structured analysis. Produce formatted output.

Mode Selection

Mode Question Output Trigger

Causal How do we execute? Plan with actions Known process, operational workflow

Abductive Why did this happen? Diagnosis with hypotheses Single anomaly, diagnosis needed

Inductive What pattern exists? Rules or assessment Multiple observations, evaluation

Analogical How is this like that? Adaptation plan Novel situation, transfer needed

Dialectical How do we resolve this? Synthesis or decision Conflicting positions, choosing options

Counterfactual What if we had/do X? Comparison with verdict Decision evaluation, scenarios

For simple cases without deep reasoning: Use templates directly.

Decision Tree

Is this operational execution with known steps? YES → Causal NO ↓ Is there a single anomaly requiring explanation? YES → Abductive NO ↓ Are there multiple instances suggesting a pattern? YES → Inductive NO ↓ Is this a novel situation with a similar past case? YES → Analogical NO ↓ Are there conflicting positions or trade-offs? YES → Dialectical NO ↓ Evaluating past decisions or future scenarios? YES → Counterfactual NO → Ask clarifying question

Mental Models

Apply these models to sharpen reasoning across all modes.

Model Core Insight Apply When

Telescope, Not Brain AI reveals data structure, doesn't create it Diagnosing AI/model failures

Geometry Under Constraints Dense patterns → reasoning; thin patterns → hallucination Evaluating AI confidence

Compression = Generalization Models compress structure into reproducible patterns Explaining model behavior

Four-Layer Stack Representation → Generalization → Reasoning → Agency Localizing AI failures

Prediction vs Behavior Prediction is cheap; behavior has consequences Designing agent constraints

Labels ≠ Truth Labels are opinions frozen in data Evaluating training data

Full reference: references/mental-models.md

Challenge Techniques

Every conclusion must survive challenge. Use these techniques:

Devil's Advocate

Attack your own position. What's the strongest argument against this conclusion?

Pre-Mortem

Assume the plan failed in 6 months. Why did it fail?

Stakeholder Lens

How does [engineering/sales/user/finance] see this differently?

Steel-Man + Attack

State the opposing view at its strongest, then find the flaw.

Layer Check

Which layer is actually failing? (Representation → Generalization → Reasoning → Agency)

Mode Summaries

Causal

Purpose: Execute systematic cause-effect reasoning.

Flow: Input → Hypothesis → Implication → Decision → Actions → Learning

Output: Execution analysis or phased plan (for larger initiatives)

Key rules:

• All claims require evidence with source

• Hypothesis must be falsifiable

• Implications need specific numbers (not "significant")

• Decision must be explicit: PROCEED / DEFER / DECLINE

• Actions need owner + deadline + success criteria

• Learning compares expected vs actual

Challenge: "What would prove this hypothesis wrong?"

→ references/causal.md

Abductive

Purpose: Generate best explanation from observation.

Flow: Observation → Hypotheses (≥5) → Evidence Debate → Best Explanation

Output: Diagnosis with ranked hypotheses and minority report

Key rules:

• Quantify the anomaly (%, deviation, timeline)

• Generate hypotheses across ≥3 categories

• For AI systems: check by layer (Representation/Generalization/Reasoning/Agency)

• Include minority report if second hypothesis ≥40% confidence

• State what was ruled out and why

Challenge: "What else could explain this? What doesn't this hypothesis explain?"

→ references/abductive.md

Inductive

Purpose: Extract patterns from multiple observations.

Flow: Collection (≥5 instances) → Pattern Detection → Generalization → Confidence Bounds

Output: Pattern analysis with rules, or assessment against criteria

Pattern types: Frequency, Correlation, Sequence, Cluster, Trend, Threshold

Key rules:

• Minimum 5 instances before generalizing

• Correlation ≠ causation (test mechanism separately)

• State applicability bounds for every rule

• Document exceptions (≥30% exception rate = unreliable rule)

Challenge: "Is this pattern or coincidence? What's the exception that breaks this?"

→ references/inductive.md

Analogical

Purpose: Transfer knowledge from source to target situation.

Flow: Source Retrieval → Structural Mapping → Target Application → Adaptation

Output: Adaptation plan with what transfers, what adapts, what's new

Key rules:

• Source must have documented outcome

• Map structure (objects, relations, mechanisms), not surface features

• Identify at least one "broken" relation (perfect analogies don't exist)

• Specify what's genuinely new (not just adapted)

Challenge: "Where does this analogy break down? What's different about the new context?"

→ references/analogical.md

Dialectical

Purpose: Synthesize opposing positions.

Flow: Thesis (steel-man) → Antithesis (steel-man) → Synthesis

Output: Synthesis resolving conflict, or decision selecting between options

Key rules:

• State underlying concern, not just position

• Steel-man both sides (strongest version)

• Synthesis ≠ compromise (must address root concerns)

• Explicit trade-offs with who accepts the cost

Resolution types: Integration, Sequencing, Segmentation, Reframing, Transcendence

Challenge: "Am I straw-manning either side? Does synthesis actually resolve the tension?"

→ references/dialectical.md

Counterfactual

Purpose: Evaluate alternatives through "what if" simulation.

Flow: Actual World → Intervention → Projection → Comparison

Output: Comparison with verdict and learning

Key rules:

• Document what was knowable at decision time (avoid hindsight bias)

• Intervention must have been actually available

• Model three scenarios: Expected (55-60%), Optimistic (20-25%), Pessimistic (15-20%)

• Verdict requires confidence bounds

Challenge: "Am I using hindsight? Was this actually an option then?"

→ references/counterfactual.md

Output Format

Prose, not YAML. Every reasoning output includes:

[Mode] Analysis: [Topic]

Conclusion: [Primary finding in 1-2 sentences]

Confidence: [X%] — [Why this confidence level]

Supporting evidence:

• [Evidence 1]
• [Evidence 2]

Challenges addressed:

• [Challenge]: [How resolved]

Uncertainty: [What's still unknown]

Next steps:

1. [Action with owner if applicable]

Mode Transitions

From To Trigger

Abductive Causal Diagnosis complete → ready to act

Inductive Causal Pattern validated → ready to apply

Analogical Causal Adaptation ready → ready to execute

Dialectical Causal Synthesis agreed → ready to implement

Counterfactual Inductive Multiple counterfactuals suggest pattern

Any Abductive Unexpected outcome during execution

Anti-Patterns

Avoid Do Instead

Skipping challenge step Every conclusion must survive attack

"It's obvious" Require evidence for conclusion

Vague confidence ("pretty sure") Numeric confidence with rationale

Single hypothesis Generate ≥5 before evaluating

Perfect analogy assumption Always find where mapping breaks

Compromise as synthesis Address underlying concerns

Hindsight in counterfactuals Document what was knowable then

Templates

For simple structural needs without full reasoning, use templates directly.

Template Use Case Trigger

SOP/Runbook Document known process "create runbook", "write SOP"

Checklist Quick verification "checklist for", "pre-flight"

Success Criteria Define "done" "how do we know", "success metrics"

Recommendation Actionable guidance "what should I do", "recommend"

→ references/templates.md

References

File Content

mental-models.md Conceptual models for reasoning

causal.md Execution flow + plan output

abductive.md Hypothesis testing + diagnosis output

inductive.md Pattern extraction + assessment output

analogical.md Knowledge transfer + adaptation output

dialectical.md Position synthesis + decision output

counterfactual.md Alternative evaluation + comparison output

templates.md SOPs, checklists, success criteria, recommendations