Assumption Audit

Input: $ARGUMENTS

Purpose

Every field, practice, or belief system rests on hidden assumptions that practitioners rarely question. This compound skill systematically surfaces those assumptions and stress-tests them.

What this produces: A ranked report of a field’s hidden assumptions with validation status (validated, challenged, or overturned), cross-domain perspective, and actionable implications.

This is a compound skill — it chains 6 skills in sequence. Each step builds on the previous.

Depth Scaling

Default: 2x. Parse depth from $ARGUMENTS if specified (e.g., “/aa 4x [input]”).

These are floors. Go deeper where insight is dense. Compress where it’s not.

The Chain

Step 1: /aex    -- Surface hidden assumptions
Step 2: /ai     -- Ask "what if the opposite is true?"
Step 3: /lr        -- Check what's already known about these assumptions
Step 4: /araw 8x                  -- Rigorously test each assumption (Assume Right / Assume Wrong)
Step 5: /cda     -- How do other fields handle similar assumptions?
Step 6: /ins        -- Combine all findings into ranked report

Execution Procedure

Step 1: Extract Hidden Assumptions

-> INVOKE: /aex $ARGUMENTS

Extract all hidden assumptions from the field/practice/belief. For each assumption, identify:

• What type it is (causal, existence, stability, access, capability, value, knowledge, resources, permission, timing)
• How critical it is (HIGH / MED / LOW)
• Whether practitioners are aware of it

Target: 15-25 assumptions for a meaningful audit.

Output of this step: Numbered list of assumptions with type and criticality rating.

Step 2: Invert Each Assumption

-> INVOKE: /ai [assumptions from Step 1]

For each assumption, ask: “What if the opposite is true?” This reveals:

• Alternative worldviews that practitioners miss
• Failure modes hiding behind the assumption
• Opportunities that the assumption conceals

Output of this step: Each assumption paired with its inversion and what the inversion would mean.

Step 3: Research Existing Critiques

-> INVOKE: /lr [key assumptions and inversions]

Before running ARAW, check what’s already known:

• Has this assumption been challenged before?
• What evidence exists for and against?
• Are there fields that already operate on the opposite assumption?

This prevents rediscovering known critiques and grounds the audit in existing work.

Output of this step: Evidence summary for/against each major assumption.

Step 4: ARAW Testing

-> INVOKE: /araw 8x [assumptions with evidence from Steps 1-3]

For each HIGH and MED criticality assumption, run full ARAW:

• ASSUME RIGHT: What follows if this assumption holds? What must also be true?
• ASSUME WRONG: What follows if this assumption fails? What’s the alternative?

Recurse to depth 6+. Each sub-claim gets its own AR/AW branch.

Output of this step: Each assumption classified as VALIDATED, CHALLENGED, or OVERTURNED with reasoning.

Step 5: Cross-Domain Perspective

-> INVOKE: /cda [challenged and overturned assumptions]

For assumptions that were CHALLENGED or OVERTURNED:

• What do other fields do differently?
• Are there domains that explicitly reject this assumption and succeed?
• What can this field learn from those domains?

Output of this step: Cross-domain insights for each challenged assumption.

Step 6: Synthesize Findings

-> INVOKE: /ins [all outputs from Steps 1-5]

Combine everything into a final audit report:

ASSUMPTION AUDIT: [Field/Practice/Belief]
==========================================

EXECUTIVE SUMMARY (1 page)
- X assumptions surfaced, Y challenged, Z overturned
- Top 3 findings
- Key implications

FULL FINDINGS (by criticality)

OVERTURNED ASSUMPTIONS:
[For each]: What it is, why it's wrong, what the evidence says,
what other fields do instead, what to do about it

CHALLENGED ASSUMPTIONS:
[For each]: What it is, why it's uncertain, evidence both ways,
what other fields suggest, what to investigate further

VALIDATED ASSUMPTIONS:
[For each]: What it is, why it holds, supporting evidence

CROSS-DOMAIN INSIGHTS:
[Transferable lessons from other fields]

IMPLICATIONS:
[What practitioners should do differently based on findings]

-> COMPLETE

Output Standards

• All outputs readable WITHOUT knowledge of GOSM, ARAW, or UAUA
• The method is invisible; the findings are the product
• Include validation status: “This audit has not been reviewed by domain experts”
• For high-stakes fields (medical, legal, financial): include disclaimer
• Produce both executive summary AND full report

Quality Gates

Between Steps 3 and 4: If literature review reveals the assumptions are already well-studied and consensus exists, note this and focus ARAW on areas of genuine uncertainty.

Between Steps 4 and 5: If all assumptions are VALIDATED, the audit may not be very interesting. Consider whether the extraction (Step 1) went deep enough.

Between Steps 5 and 6: If cross-domain insights are superficial (surface analogy, not structural), re-examine whether the analogy is transferable.

Example Usage

/aa software project estimation
/aa higher education as career preparation
/aa venture capital as startup funding model
/aa agile methodology in large organizations