Master backend architectures, REST API design, microservices, and server-side development across multiple languages. Use this as a central index to access specialized sub-skills.
日本語の概要は準備中です。原文の説明を表示しています。
[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Core Principle: No claim without proof. Every critical fact needs: source + confidence level. If unsure, keep digging until confident or escalate to user.
When you invoke this skill, you're asking AI to:
| Dimension | 100% Core | +50% Enhancement |
|---|---|---|
| Source | Primary source identified | + Independent confirmation |
| Confidence | Percentage stated | + Reasoning documented |
| Verification | Single source check | + Multi-source cross-validation |
| Gaps | Note uncertainties | + Active Loop150 to fill gaps |
| Level | % Range | Description | Action Required |
|---|---|---|---|
| Verified | 95-100% | Multiple primary sources, no contradictions | Use in critical decisions |
| Strong | 85-94% | Reliable sources, minor uncertainties | Safe for most purposes |
| Moderate | 75-84% | Limited sources, some gaps | Flag for verification |
| Weak | 50-74% | Insufficient evidence, major gaps | Do not use without confirmation |
| Insufficient | <50% | Contradictory or missing | Reject, research further |
State the fact clearly:
🔍 **Claim:** [Precise factual statement]
**Context:** [Why this matters]
**Critical Level:** [High/Medium/Low]
Find the original source:
Find independent corroboration:
Calculate confidence:
**Evidence Evaluation:**
├── Primary Source: [Quality assessment]
├── Secondary Sources: [Count and quality]
├── Contradictions: [Any found?]
└── Gaps: [What's missing?]
**Confidence:** [X]%
**Reasoning:** [Why this percentage]
Confidence ≥85%?
├─ YES → Use fact with stated confidence
└─ NO → Enter Loop150
When confidence <85%:
🔄 **LOOP150 ACTIVATED** (Current: [X]%)
ITERATION 1: EXPAND SOURCES
├── Identify new source types
├── Use alternative research methods
├── Broaden search scope
↓
ITERATION 2: DEEPER ANALYSIS
├── Drill into source details
├── Verify source credibility
├── Check contextual factors
↓
ITERATION 3: CROSS-VALIDATION
├── Compare against known facts
├── Test logical consistency
├── Seek expert corroboration
↓
RECALCULATE: New confidence = [Y]%
Continue loop until:
├─ ≥90% achieved → EXIT, proceed with confidence
└─ Sources exhausted → ESCALATE to user
🔍 RELIABILITY FACTORS:
📊 EVIDENCE STRENGTH:
| Type | Strength | Example |
|---|---|---|
| Primary | High | Original data, first-hand |
| Secondary | Medium | Analysis of primary |
| Tertiary | Low | Summaries, reviews |
| Statistical | High | Large sample, proper method |
| Anecdotal | Variable | Personal experience |
🔍 **PROOF-GRADE 150 VERIFICATION**
**Claim:** [Precise factual statement]
**Primary Source:**
- [File/location/date]
- "[Direct quote or data]"
**Secondary Sources:**
- [Source 2]: [Confirmation]
- [Source 3]: [Confirmation]
**Confidence Level:** [X]%
**Reasoning:** [Why this level]
**Validation Method:** [How verified]
**Outstanding Issues:** [Any uncertainties]
**Status:** ✅ VERIFIED | ⚠️ NEEDS CONFIRMATION | ❌ INSUFFICIENT
| Failure | Detection | Recovery |
|---|---|---|
| Unverified Claims | Facts without sources | Immediate verification, Loop150 |
| Overconfidence | Inflated percentages | Recalculate with scrutiny |
| Source Bias | Only confirming sources | Actively seek contradictions |
| Incomplete Docs | Missing source trail | Document all sources now |
AI: "The API response time is fast enough"
Source: "Feels fast to me"
Result: Performance issues in production
🔍 PROOF-GRADE 150 VERIFICATION
Claim: "API response time is consistently under 200ms"
Primary Source:
- /tests/performance/load_test_results.json
- "p99 latency: 187ms across 10,000 requests"
Secondary Sources:
- Production monitoring (last 7 days): avg 156ms
- APM dashboard: p95 = 178ms
Confidence Level: 95%
Reasoning: Multiple measurement sources, consistent results,
production data confirms test environment findings.
Validation Method: Cross-referenced test data with production metrics
Status: ✅ VERIFIED FOR USE
Remember: Proof-grade isn't about being slow — it's about being trustworthy. A 95% confidence claim is more valuable than an unverified assertion. When stakes are high, proof-grade protects everyone.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master backend architectures, REST API design, microservices, and server-side development across multiple languages. Use this as a central index to access specialized sub-skills.
日本語の概要は準備中です。原文の説明を表示しています。
在每次回复中使用固定前缀并列出本次实际参考的指令/文件,便于校验是否遵循上下文与规则。
日本語の概要は準備中です。原文の説明を表示しています。
Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
日本語の概要は準備中です。原文の説明を表示しています。
[00] META. Orchestrate skills into dynamic chains for complex tasks. Analyzes the task, discovers available skills, builds an optimal chain, explains why each skill is needed, and executes step-by-step with user confirmation. Use for any complex task requiring multiple thinking/research/analysis steps. Triggers on "plan this", "how to approach", "what's the strategy", "build a plan", or any multi-step problem requiring skill orchestration.
日本語の概要は準備中です。原文の説明を表示しています。