fia-field-analysis

name	fia - Field Analysis
description	Comprehensive analysis of a field's key tensions, hidden assumptions, and blind spots. Chains assumption extraction, deep ARAW testing, cross-domain perspective, evaluation dimensions, and insight synthesis into a complete field report.
output	{"format":"prose"}

Field Analysis

Input: $ARGUMENTS

Interpretations

Before executing, identify which interpretation matches the user's input:

Interpretation 1 — Analyze a field's hidden assumptions and blind spots: The user wants a comprehensive analysis of an academic, professional, or intellectual field — surfacing orthodoxies, testing assumptions, and producing a full field health report. Interpretation 2 — Compare fields or paradigms: The user is interested in how two or more fields differ in their assumptions, methods, or blind spots, and wants cross-field insight rather than a single-field deep dive. Interpretation 3 — Evaluate a specific claim or practice within a field: The user has a particular belief or methodology from a field they want to stress-test, rather than analyzing the entire field.

If ambiguous, ask: "I can help with a full field analysis, a cross-field comparison, or testing a specific claim within a field — which fits?" If clear from context, proceed with the matching interpretation.

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Purpose

Every field has unquestioned orthodoxies, unresolved tensions, and blind spots that insiders can't see. This skill produces a comprehensive analysis of a field by combining assumption extraction, rigorous testing, outside perspective, and multi-dimensional evaluation.

What this produces: A comprehensive report covering a field's hidden assumptions, key tensions, blind spots, what other fields know that this one doesn't, and actionable recommendations.

This is a compound skill -- it chains 5 skills in sequence. It is the most comprehensive single-field analysis available.

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Depth Scaling

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

Depth	Min Sources Reviewed	Min Frameworks Applied	Min Cross-References	Min Synthesis Passes
1x	5	1	2	1
2x	10	2	4	1
4x	20	3	8	2
8x	35	5	12	3
16x	50	7	18	4

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

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The Chain

Step 1: /aex     -- Surface the field's hidden beliefs
Step 2: /araw 8x                   -- Deep test of each assumption
Step 3: /cda      -- What do other fields see that this one doesn't?
Step 4: /evaluation_dimensions     -- Evaluate the field across multiple dimensions
Step 5: /ins         -- Combine into comprehensive report

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Execution Procedure

Step 1: Extract the Field's Hidden Assumptions

-> INVOKE: /aex $ARGUMENTS

Go beyond surface beliefs. Extract:

• Foundational assumptions: What the field takes as given (often unstated in textbooks)
• Methodological assumptions: How the field thinks knowledge is produced
• Value assumptions: What the field considers important or good
• Boundary assumptions: What the field considers in-scope vs out-of-scope
• Historical assumptions: Beliefs inherited from the field's origins that may no longer hold

Target: 20-30 assumptions across all five categories.

Output: Categorized assumptions with criticality ratings.

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Step 2: Deep ARAW Testing

-> INVOKE: /araw 8x [assumptions from Step 1, grouped by criticality]

For HIGH and MED criticality assumptions, run deep ARAW:

• 6+ levels deep for each assumption
• Each sub-claim gets its own AR/AW branch
• Track which assumptions support each other (cluster analysis)
• Identify keystone assumptions (many others depend on them)

Output: Each assumption rated VALIDATED / CHALLENGED / OVERTURNED, with full reasoning trees. Keystone assumptions identified.

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Step 3: Cross-Domain Perspective

-> INVOKE: /cda [the field + its challenged/overturned assumptions]

Bring in outside perspectives:

• Which other fields face similar problems but answer them differently?
• Where does this field's orthodoxy look strange from outside?
• What would a practitioner from [adjacent field] find surprising about this field's assumptions?

Target: 5-10 cross-domain insights.

Output: Outside perspectives on the field's blind spots.

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Step 4: Multi-Dimensional Evaluation

-> INVOKE: /evd [the field as a whole]

Evaluate the field across universal dimensions:

• Epistemic health: How well does the field update on evidence?
• Internal coherence: Do the field's claims contradict each other?
• External validity: Do the field's claims match observable reality?
• Practical utility: Does the field's knowledge actually help practitioners?
• Innovation capacity: Can the field generate novel insights?
• Self-correction: Does the field fix its own errors?
• Inclusivity of evidence: Does the field consider all relevant evidence sources?
• Transparency: Are the field's methods and assumptions visible?

Output: Scorecard with evidence for each dimension.

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Step 5: Synthesize Complete Analysis

-> INVOKE: /ins [all outputs from Steps 1-4]

Create the comprehensive field analysis:

FIELD ANALYSIS: [Field Name]
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EXECUTIVE SUMMARY (1-2 pages)
- The field's greatest strength
- The field's biggest blind spot
- Top 3 recommendations

PART 1: WHAT THE FIELD ASSUMES
- Foundational assumptions (rated)
- Methodological assumptions (rated)
- Value assumptions (rated)
- Boundary assumptions (rated)
- Historical assumptions (rated)
- Keystone assumptions (others depend on these)

PART 2: WHAT TESTING REVEALED
- Assumptions that held up under scrutiny
- Assumptions that are genuinely uncertain
- Assumptions that appear wrong
- Clusters of related assumptions

PART 3: WHAT OUTSIDERS SEE
- Cross-domain insights
- What looks strange from outside
- What other fields do differently and why

PART 4: FIELD HEALTH SCORECARD
- Epistemic health: [rating + evidence]
- Internal coherence: [rating + evidence]
- External validity: [rating + evidence]
- Practical utility: [rating + evidence]
- Innovation capacity: [rating + evidence]
- Self-correction: [rating + evidence]

PART 5: KEY TENSIONS
- [Tension 1]: The field wants X but this requires assuming Y, which conflicts with Z
- [Tension 2]: ...

PART 6: RECOMMENDATIONS
- For practitioners: [What to do differently based on findings]
- For researchers: [What to investigate based on gaps found]
- For the field as a whole: [Structural changes that would improve field health]

-> COMPLETE

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Output Standards

• Readable by both insiders and outsiders of the field
• Every claim supported by reasoning (not just assertions)
• Balanced: acknowledges what the field gets right, not just criticism
• Constructive: recommendations are actionable
• Honest about limitations: "This analysis was produced without domain expert review"
• Both executive summary and full report included

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Quality Gates

After Step 1: If fewer than 15 assumptions found, the extraction wasn't deep enough. Consider the 5 assumption categories and ensure each has at least 2-3 entries.

After Step 2: If everything is VALIDATED, either the field is unusually well-founded or the testing wasn't adversarial enough. Check that ASSUME WRONG branches were genuinely explored.

After Step 4: If all dimensions score high, consider whether the evaluation was rigorous or just surface-level.

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Example Usage

/fia software engineering
/fia clinical psychology
/fia economics
/fia education
/fia nutrition science
/fia venture capital
/fia machine learning research