/assumption-excavation
Systematic extraction, challenge, and sensitivity analysis of assumptions
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill assumption-excavation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/assumption-excavation
Context preview
The summary Claude sees to decide when to auto-load this skill.
Systematic extraction, challenge, and sensitivity analysis of assumptions
SKILL.md
assumption-excavation.SKILL.mdname: assumption-excavation
description: Systematic extraction, challenge, and sensitivity analysis of assumptions
underlying a decision to identify load-bearing beliefs.
execution: tactic
dependencies:
sops:
- conclusion-sensitivity
- convergence-assumption-challenge
- convergence-assumption-extraction
Assumption Excavation
A three-phase tactic that surfaces hidden assumptions, challenges each one adversarially, and maps which assumptions are load-bearing for the conclusion. Decisions often rest on unstated beliefs — this tactic makes them explicit and tests their strength.
Stages
1. **Assumption Extraction** — Systematically surface all assumptions underlying the decision, with confidence levels 2. **Assumption Challenge** — For each assumption, construct the strongest counter-argument and identify alternatives 3. **Conclusion Sensitivity** — Map which assumptions, if wrong, would change the conclusion
Available SOPs
| SOP | Phase | Purpose | |-----|-------|---------| | assumption-extraction | Extract | Surface hidden assumptions with confidence | | assumption-challenge | Challenge | Attack each assumption adversarially | | conclusion-sensitivity | Sensitivity | Map load-bearing assumptions |
Execution Guidance
- Extract minimum 5 assumptions per decision
- Challenge ALL assumptions, not just obvious ones
- Confidence levels: HIGH (>80%), MEDIUM (50-80%), LOW (<50%)
- Critical assumption = conclusion changes if assumption is wrong
- Focus mitigation efforts on critical + low-confidence assumptions
Minimum Yield
- >= 5 assumptions extracted with confidence levels
- Challenge argument for each assumption
- Alternative assumption for each (what if the opposite is true?)
- Sensitivity map showing which assumptions are critical
- List of critical assumptions requiring mitigation
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | conclusion-sensitivity | Map which assumptions are load-bearing by assessing how the conclusion changes if each assumption fails. | | convergence-assumption-challenge | Construct the strongest counter-argument against a specific assumption and propose alternatives. | | convergence-assumption-extraction | Systematically surface hidden assumptions underlying a decision with confidence levels. |
<!-- END available-tables (generated) -->
Read more
name: assumption-excavation description: Systematic extraction, challenge, and sensitivity analysis of assumptions underlying a decision to identify load-bearing beliefs. execution: tactic dependencies: sops: - conclusion-sensitivity - convergence-assumption-challenge - convergence-assumption-extraction
Assumption Excavation
A three-phase tactic that surfaces hidden assumptions, challenges each one adversarially, and maps which assumptions are load-bearing for the conclusion. Decisions often rest on unstated beliefs — this tactic makes them explicit and tests their strength.
Stages
1. **Assumption Extraction** — Systematically surface all assumptions underlying the decision, with confidence levels 2. **Assumption Challenge** — For each assumption, construct the strongest counter-argument and identify alternatives 3. **Conclusion Sensitivity** — Map which assumptions, if wrong, would change the conclusion
Available SOPs
| SOP | Phase | Purpose | |-----|-------|---------| | assumption-extraction | Extract | Surface hidden assumptions with confidence | | assumption-challenge | Challenge | Attack each assumption adversarially | | conclusion-sensitivity | Sensitivity | Map load-bearing assumptions |
Execution Guidance
- Extract minimum 5 assumptions per decision
- Challenge ALL assumptions, not just obvious ones
- Confidence levels: HIGH (>80%), MEDIUM (50-80%), LOW (<50%)
- Critical assumption = conclusion changes if assumption is wrong
- Focus mitigation efforts on critical + low-confidence assumptions
Minimum Yield
- >= 5 assumptions extracted with confidence levels
- Challenge argument for each assumption
- Alternative assumption for each (what if the opposite is true?)
- Sensitivity map showing which assumptions are critical
- List of critical assumptions requiring mitigation
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | conclusion-sensitivity | Map which assumptions are load-bearing by assessing how the conclusion changes if each assumption fails. | | convergence-assumption-challenge | Construct the strongest counter-argument against a specific assumption and propose alternatives. | | convergence-assumption-extraction | Systematically surface hidden assumptions underlying a decision with confidence levels. |
<!-- END available-tables (generated) -->
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

