NSA - Not Sure About

Input: $ARGUMENTS

Core Principles

Uncertainty has types. Epistemic uncertainty (don’t know the answer), aleatory uncertainty (inherently random), model uncertainty (using the wrong framework), and question uncertainty (not sure what the question is). Each type needs different handling.
Not all uncertainty needs reducing. Reducing uncertainty costs time and effort. If the decision is the same regardless of the uncertain factor, investigating is waste. Always check: does this uncertainty MATTER?
Confidence is a range, not a point. “Not sure” can mean 10% or 90%. Forcing an explicit range (“somewhere between 30-60%”) makes the uncertainty tractable and comparable.
The cheapest evidence first. Don’t commission a study when a 5-minute search would resolve the uncertainty. Order evidence-gathering by cost, not by rigor.
Acting under uncertainty is sometimes optimal. If the cost of delay exceeds the cost of being wrong, act now and correct later. The skill must identify when investigation is the right move AND when it isn’t.

Phase 1: Uncertainty Extraction

[U1] RAW_STATEMENT: [what the user is uncertain about, quoted]
[U2] SUBJECT: [what is uncertain — the specific thing]
[U3] STAKES: [what depends on this — what changes if the answer is X vs Y?]

Multiple Uncertainties

If the statement contains multiple uncertainties, decompose:

[U4] UNCERTAINTY_1: [subject] — STAKES: [what depends on it]
[U5] UNCERTAINTY_2: [subject] — STAKES: [what depends on it]
...

Phase 2: Uncertainty Classification

For each uncertainty:

[U-N] TYPE: [epistemic | aleatory | model | question]
[U-N] EVIDENCE: [why this type]

Type	What It Means	Example	Resolution Strategy
Epistemic	Answer exists but you don’t know it	”Not sure what the API rate limit is”	Find the answer — research, ask, test
Aleatory	Inherently random, no definitive answer	”Not sure if it’ll rain tomorrow”	Estimate probabilities; plan for variance
Model	Using wrong framework to think about it	”Not sure if this is a marketing or product problem”	Reframe — try different models
Question	Not sure what you’re actually uncertain about	”Something feels off but I don’t know what”	Clarify the question first

Phase 3: Confidence Range

[U-N] CONFIDENCE_RANGE: [lower bound]% to [upper bound]%
[U-N] RANGE_REASONING: [what drives the bounds]
[U-N] WHAT_WOULD_MOVE_IT:
  UPWARD: [what evidence would increase confidence]
  DOWNWARD: [what evidence would decrease confidence]

Calibration Checks

Bias	Signal	Correction
Narrow range	”I’m 70-75% sure”	Real uncertainty is wider. Are you genuinely that precise?
Symmetric range	”50% — could go either way”	Do you really have zero evidence? Usually there’s asymmetry
Round numbers	”About 50%” / “About 80%“	These are anchors, not calibration. What specific evidence drives the number?
Stated vs revealed	Says “not sure” but acts with full confidence	Behavior reveals true confidence. Which is it?

Phase 4: Cost-of-Uncertainty Analysis

[U-N] DECISION_SENSITIVITY: [does this uncertainty change the decision? yes/no/partially]
[U-N] COST_OF_BEING_WRONG: [what happens if you act on wrong assumption? severity: low/medium/high/catastrophic]
[U-N] COST_OF_DELAY: [what happens if you investigate instead of acting? severity: low/medium/high/catastrophic]
[U-N] REVERSIBILITY: [if you act and are wrong, can you undo it? easily/with effort/no]

Action Decision Matrix

Decision Sensitivity	Cost of Wrong	Cost of Delay	Action
No (same decision either way)	Any	Any	Stop investigating — uncertainty doesn’t matter
Yes	Low	High	Act now — correct if wrong later
Yes	High	Low	Investigate — the risk warrants evidence
Yes	High	High	Partial action — act on what’s known, investigate the rest
Yes	Catastrophic	Any	Investigate — must reduce uncertainty before acting

[U-N] RECOMMENDED_POSTURE: [investigate | act now | partial action | stop — uncertainty doesn't matter]

Phase 5: Evidence-Gathering Plan

If RECOMMENDED_POSTURE is “investigate”:

[U-N] EVIDENCE PLAN (ordered by cost, cheapest first):
  1. [action] — COST: [low/medium/high] — EXPECTED_RESOLUTION: [how much this reduces uncertainty]
  2. [action] — COST: [level] — EXPECTED_RESOLUTION: [amount]
  3. [action] — COST: [level] — EXPECTED_RESOLUTION: [amount]

[U-N] STOP_INVESTIGATING_WHEN: [condition — when is enough evidence enough?]

Phase 6: Output

UNCERTAINTY ANALYSIS
====================

ORIGINAL: [quoted statement]

UNCERTAINTIES:
1. [subject]
   TYPE: [epistemic | aleatory | model | question]
   CONFIDENCE: [X%] to [Y%]
   STAKES: [what depends on this]
   DECISION_SENSITIVITY: [yes/no]
   COST_OF_WRONG: [level]
   COST_OF_DELAY: [level]

   RECOMMENDATION: [investigate | act now | partial action | doesn't matter]

   IF INVESTIGATE:
     1. [cheapest action first]
     2. [next cheapest]
     STOP WHEN: [condition]

   IF ACT NOW:
     ACT: [what to do]
     CORRECTION_PLAN: [what to do if wrong]

READY FOR:
- /ht [claim] — to formalize a testable hypothesis
- /ver [claim] — to verify against evidence
- /decide [decision] — if uncertainty feeds a decision
- /ar [assumption] — to explore what follows if the uncertain thing is true

Failure Modes

Failure	Signal	Fix
Type confusion	Epistemic treated as aleatory (or vice versa)	Apply type definitions strictly — does an answer exist?
Investigate everything	Every uncertainty gets an evidence plan	Check decision sensitivity first — some don’t matter
Act on everything	Uncertainty dismissed, action taken without checking	Check cost of being wrong — some uncertainties are catastrophic
Vague confidence	”Not very sure” without a range	Force a numeric range — even a wide one is better than nothing
Missing cost analysis	Evidence plan without cost ordering	Cheapest evidence first — always
Infinite investigation	No stop condition on evidence gathering	Always specify when enough is enough
Question uncertainty ignored	Treating “something feels off” as epistemic	If the question itself is unclear, clarify the question before seeking answers

Depth Scaling

Depth	Min Uncertainties Analyzed	Calibration Checks	Cost Analysis	Evidence Actions
1x	1	1	Decision sensitivity only	2
2x	3	3	Full matrix	3
4x	5	All biases checked	Full matrix + reversibility	5
8x	All identified	All + cross-uncertainty interactions	Full + scenario analysis	8

Default: 2x. These are floors.

Pre-Completion Checklist

Each uncertainty identified and separated
Type classified (epistemic/aleatory/model/question)
Confidence range specified (not vague)
Calibration biases checked
Decision sensitivity assessed (does this uncertainty matter?)
Cost of wrong and cost of delay compared
Recommended posture derived from cost analysis (not assumed)
If investigating: evidence actions ordered by cost
Stop condition specified for investigation

Integration

Use from: /it (when confidence is very low), natural language uncertainty detection
Routes to: /ht, /ver, /decide, /ar depending on uncertainty type and posture
Complementary: /it (handles “I think”), /but (handles objections)
Differs from /it: it handles all “I think” claims; nsa specifically handles uncertainty
Differs from /ht: ht formulates hypotheses; nsa analyzes the uncertainty itself

nsa - Not Sure About

NSA - Not Sure About

Core Principles

Phase 1: Uncertainty Extraction

Multiple Uncertainties

Phase 2: Uncertainty Classification

Phase 3: Confidence Range

Calibration Checks

Phase 4: Cost-of-Uncertainty Analysis

Action Decision Matrix

Phase 5: Evidence-Gathering Plan

Phase 6: Output

Failure Modes

Depth Scaling

Pre-Completion Checklist

Integration

`nsa - Not Sure About`