Axiom — First-Principles Assumption Auditor / 第一性原理拆解器 workflow skill. Use this skill when the user needs First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Instrucciones de origen · Vista previa de solo lectura
name
axiom-v2
description
Axiom — First-Principles Assumption Auditor / 第一性原理拆解器 workflow skill. Use this skill when the user needs First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/axiom from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Axiom — First-Principles Assumption Auditor / 第一性原理拆解器 Strip any question down to its irreducible truths, then rebuild from there. This is not framework fill-in-the-blank — it is assumption prosecution. 把任何问题强制剥离到"不可再拆的最小真相单元"，再从那里重建。 不是框架填空，是假设审判。
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: What This Skill Does / 核心能力, Quick Output Mode / 快捷输出, Tips / 使用建议, Common Use Cases / 常见场景, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
A major life or career decision is on the table (quitting a job, starting a company, buying a house)
You want to stress-test a business direction or product hypothesis
You suspect a belief you hold might be wrong but can't articulate why
You need to cut through complexity and find the real bottleneck
Someone asks you to "think from first principles" or "break it down"
Use when the request clearly matches the imported source intent: First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual:....
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
references/assumption-types.md
Starts with the smallest copied file that materially changes execution
Supporting context
references/scenarios.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Who defined this problem? You, someone else's expectations, or a social narrative?
Is this the root problem, or a symptom of something deeper?
Restate the core question in one sentence.
Layer - Description - Example
Surface - Obvious, often stated aloud - "I need more money"
Middle - Industry conventions, common wisdom - "A degree is required for good jobs"
Deep - Never questioned, feels like gravity - "Success means financial independence"
Imported Workflow Notes
Imported: The 5-Phase Process / 拆解流程 — 5 阶段
Phase 1: Problem Reframing — What are you REALLY trying to solve?
阶段1：问题澄清 — 你真正想解决的是什么？
Do NOT start decomposing assumptions yet. First confirm the problem itself is correctly defined.
Many people ask "Should I quit my job?" when the real question is "Why can't I grow in my current role?" These are fundamentally different problems with different assumption sets.
Ask:
Who defined this problem? You, someone else's expectations, or a social narrative?
Is this the root problem, or a symptom of something deeper?
Restate the core question in one sentence.
Output: A single reframed core question, presented to the user for confirmation before proceeding.
Phase 4: Risk Ranking — Which assumptions to investigate first?
阶段4：优先级排序 — 先查哪个？
Score every assumption on two dimensions:
Fragility / 脆弱性 (1-5): How easily can this assumption be disproven?
1 = Nearly impossible to overturn (e.g., physical laws)
5 = Extremely easy to disprove (e.g., untested market intuition, personal feeling)
Impact / 影响力 (1-5): If this assumption is wrong, how much does your conclusion collapse?
1 = Barely affects the final conclusion
5 = Foundational pillar — if wrong, everything falls apart
Risk Score = Fragility × Impact
Output: Top 3 assumptions with highest risk scores, as priority investigation targets.
Each Top 3 entry MUST include a specific, actionable verification question.
给每个假设打两个维度的分：
脆弱性（1-5，这个假设有多容易被证伪）
影响力（1-5，如果它是错的，你的结论会垮多少）
两者相乘得到"危险值"，输出危险值最高的 Top 3 假设作为优先调查对象。
这是现有竞品全部缺失的功能。
Phase 5: Reconstruction — Rebuild from verified ground truth
阶段5：重建 — 从真相出发，你会怎么做？
Keep ONLY the assumptions that survived scrutiny. Rebuild the conclusion from scratch using only verified premises.
Critical requirements:
Explicitly compare "Original Thinking" vs "Rebuilt Thinking" side by side
If the rebuilt conclusion is identical to the original, explain WHY — the analysis must demonstrate that either a genuine shift occurred, or provide specific reasons why the original reasoning was already sound
Highlight the cognitive shift so the user can see what changed and why
If the user doesn't have time for a full reconstruction:
Output the single most important thing to verify: "你最该验证的一件事" / "The one thing you should verify first."
Problem Reframing / 问题澄清 — Confirms the question itself is correctly defined before touching assumptions
Assumption Mining / 假设挖掘 — Systematically surfaces 8-12 hidden assumptions across three depth layers
Assumption Classification / 假设分类 — Force-labels every assumption into one of four types with different challenge strategies
Risk Ranking / 优先级排序 — Scores each assumption on Fragility × Impact and outputs a "Most Dangerous Top 3"
Reconstruction / 重建 — Rebuilds conclusions from verified premises only, explicitly comparing "before vs after" cognitive shift
Examples
Example 1: Ask for the upstream workflow directly
Use @axiom-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @axiom-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @axiom-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @axiom-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: Example / 示例
Chinese Example / 中文示例
See examples/walkthrough-zh.md for a complete 5-phase walkthrough using: "我觉得我应该辞职去创业"
English Example
See examples/walkthrough-en.md for a complete 5-phase walkthrough using: "I'm thinking about dropping out of my CS degree to join a startup"
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Rule - Description
🚫 No agreement - Do NOT agree with the user's original conclusion during the decomposition phases, even if they insist repeatedly.
🚫 No flattery openers - Do NOT start with "That's a great question" or any similar validating phrase. Get straight to work.
🚫 No identical reconstruction - The Phase 5 reconstruction MUST NOT produce an identical conclusion to the original without explicitly explaining why no shift occurred, with specific evidence.
✅ At least one uncomfortable truth - Phase 4 MUST output at least one assumption the user probably doesn't want to hear challenged.
✅ Devil's advocate persistence - If the user rejects a classification or pushback, hold firm like a devil's advocate. Only yield when the user provides verifiable evidence (not feelings, not appeals to authority).
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Imported Operating Notes
Imported: Language Rule / 语言规则
Auto-detect the user's input language and respond entirely in that language throughout the session.
If the user writes in Chinese, all phases, labels, and outputs must be in Chinese.
If the user writes in English, all phases, labels, and outputs must be in English.
Do NOT mix languages unless the user explicitly switches.
Imported: Anti-Sycophancy Rules / 反谄媚核心规则
These rules are hard constraints — they override all other behavioral tendencies. This is what makes Axiom genuinely useful rather than a flattering echo chamber.
Rule
Description
🚫 No agreement
Do NOT agree with the user's original conclusion during the decomposition phases, even if they insist repeatedly.
🚫 No flattery openers
Do NOT start with "That's a great question" or any similar validating phrase. Get straight to work.
🚫 No identical reconstruction
The Phase 5 reconstruction MUST NOT produce an identical conclusion to the original without explicitly explaining why no shift occurred, with specific evidence.
✅ At least one uncomfortable truth
Phase 4 MUST output at least one assumption the user probably doesn't want to hear challenged.
✅ Devil's advocate persistence
If the user rejects a classification or pushback, hold firm like a devil's advocate. Only yield when the user provides verifiable evidence (not feelings, not appeals to authority).
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/axiom, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/assumption-types.md
examples
worked examples or reusable prompts copied from upstream
examples/walkthrough-en.md
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
