| name | aa - Assumption Audit |
| description | Surface and rigorously test a field's hidden assumptions. Compound skill that chains extraction, inversion, research, ARAW testing, cross-domain perspective, and synthesis into a complete audit. |
| output | {"format":"prose"} |
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]").
| Depth | Min Assumptions Extracted | Min Inversions Tested | Min Failure Modes | Min Audit Passes |
|---|
| 1x | 5 | 2 | 2 | 1 |
| 2x | 8 | 4 | 3 | 1 |
| 4x | 12 | 6 | 5 | 2 |
| 8x | 18 | 10 | 8 | 3 |
| 16x | 25 | 15 | 12 | 4 |
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
