alternative-analysis
Alternative-analysis generates competing interpretations and scenarios from the same evidence to counteract cognitive bias and single-explanation anchoring. Use this skill when evaluating threats, analyzing complex systems, or challenging dominant narratives by systematically testing multiple hypotheses against available evidence through what-if analysis, four-lens examination, and discriminating diagnostic tests.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/alternative-analysis && cp -r /tmp/alternative-analysis/skills/alternative-analysis ~/.claude/skills/alternative-analysisSKILL.md
# Alternative Analysis Strategy
CIA SAT alternative analysis: generate multiple competing interpretations to prevent anchoring on a single explanation.
## Method
1. **alternative-futures** builds 2-4 divergent scenarios from the same evidence base
2. What-If Analysis: systematically vary key variables to explore outcome sensitivity
3. Four Ways of Seeing: examine artifact through lenses of opportunity, risk, structure, and agency
4. **probe-execution** tests each alternative against available evidence
5. Diagnostic indicators identified that would discriminate between alternatives
6. **finding-aggregation** compares explanatory power across alternatives
## Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Attack vectors | 5 | 12 | 20 |
| Probing rounds | 3 | 6 | 10 |
| Personas | 2 | 4 | 6 |
| Assumption checks | 5 | 10 | 20 |
## Orchestration
```
threat-surface-mapping → [identify variable dimensions]
→ alternative-futures (generate 2-4 scenarios)
→ [for each alternative]:
attack-vector-generation (find discriminating tests)
→ probe-execution (test alternative)
→ finding-aggregation → attack-resilience-scoring
```
## Subagents
- threat-surface-mapping (dimension identification)
- alternative-futures (scenario generation)
- attack-vector-generation (discriminating test design)
- probe-execution (alternative testing)
- finding-aggregation (comparative synthesis)
<!-- BEGIN available-tables (generated) -->
## Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
| --- | --- |
| adversarial-roleplay | Tactic: Construct detailed hostile persona, attack artifact from that persona's perspective, record successful attack paths for aggregation. |
| structured-attack-campaign | Tactic: Full attack lifecycle — threat surface enumeration, attack vector generation, systematic probing, and finding aggregation across all surfaces. |
## Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
| --- | --- |
| alternative-futures | Generate 2-4 divergent scenarios from the same evidence base, each representing a plausible alternative to the artifact's conclusions. |
| attack-vector-generation | Generate specific attack strategies for a given threat surface, producing concrete probes that can be executed. |
| finding-aggregation | Aggregate, deduplicate, and classify findings from multiple probes into a coherent vulnerability report. |
| probe-execution | Execute a single attack probe against an artifact, record the result with evidence and severity classification. |
| threat-surface-mapping | Enumerate all attackable surfaces of an artifact — logical, empirical, methodological, social, and practical dimensions. |
<!-- END available-tables (generated) -->Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems