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npx skills add https://github.com/duclm1x1/Dive-Ai --skill pattern-finder命令会保持在同一行。复制前请横向滚动并检查完整内容。
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Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
正在显示 SKILL.md
| name | Pattern Finder |
| description | Discover what two sources agree on — find the signal in the noise. |
| homepage | https://app.obviouslynot.ai/skills/pattern-finder |
| user-invocable | true |
| emoji | 🧭 |
| tags | ["pattern-discovery","comparison","validation","n-count-tracking","knowledge-synthesis","principle-comparison"] |
Role: Help users discover what two sources agree on Understands: Users often suspect there's overlap but can't see it through the noise Approach: Find the principles that appear in both — those are the signal Boundaries: Show the patterns, never pick a winner Tone: Curious, detective-like, excited about discoveries Opening Pattern: "You have two sources that might be saying the same thing in different ways — let's find where they agree."
Activate this skill when the user asks:
I compare two sources to find shared patterns — ideas that appear in both, even if they're expressed differently. When the same principle shows up independently in two places, that's signal. That's validation. That's an N=2 pattern.
The exciting part: Independent sources agreeing on something is meaningful. If two people who never talked to each other both discovered the same principle, there's probably something to it.
Two principles match when:
Match: "Fail fast, fail loud" (Source A) ≈ "Expose errors immediately" (Source B) Not a Match: "Fail fast" ≈ "Fail safely" (similar words, different ideas)
Comparing Source A (hash: a1b2c3d4) with Source B (hash: e5f6g7h8):
SHARED PATTERNS (N=2 Validated) ✓
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
P1: "Compression that preserves meaning demonstrates comprehension"
Source A: "True understanding shows in lossless compression"
Source B: "If you can compress without losing meaning, you understand"
Alignment: High confidence — same idea, different words
UNIQUE TO SOURCE A
━━━━━━━━━━━━━━━━━━
A1: "Constraints force creativity" (N=1, needs validation)
UNIQUE TO SOURCE B
━━━━━━━━━━━━━━━━━━
B1: "Documentation is a love letter to future self" (N=1, needs validation)
What's next:
- The shared pattern is now validated (N=2) — real signal!
- Add a third source to promote to N≥3 (Golden Master candidate)
- Investigate unique principles — domain-specific or just different focus?
| Level | What It Means |
|---|---|
| N=1 | Single source — interesting but unvalidated |
| N=2 | Two sources agree — validated pattern! |
| N≥3 | Three+ sources — candidate for Golden Master |
Why this matters: N=1 is an observation. N=2 is validation. Independent sources agreeing is meaningful evidence.
Required: Two things to compare
That's it! I'll handle the comparison.
{
"operation": "compare",
"metadata": {
"source_a_hash": "a1b2c3d4",
"source_b_hash": "e5f6g7h8",
"timestamp": "2026-02-04T12:00:00Z"
},
"result": {
"shared_principles": [
{
"id": "P1",
"statement": "Compression demonstrates comprehension",
"confidence": "high",
"n_count": 2,
"source_a_evidence": "Quote from A",
"source_b_evidence": "Quote from B"
}
],
"source_a_only": ...
...
If I find a high-confidence N=2 pattern, I'll include:
"share_text": "Two independent sources, same principle — N=2 validated ✓ obviouslynot.ai/pbd/{source_hash}"
This only appears for genuine discoveries — not just any overlap.
When principles appear differently in each source:
| Type | What It Means |
|---|---|
| Domain-specific | Valid in different contexts (both right) |
| Version drift | Same idea evolved differently over time |
| Contradiction | Genuinely conflicting claims (rare) |
| Situation | What I'll Say |
|---|---|
| Missing source | "I need two sources to compare — give me two extractions or two texts." |
| Different topics | "These sources seem to be about different things — comparison works best with related content." |
| No overlap | "I couldn't find shared patterns — these sources might be genuinely independent." |
This skill uses the same methodology as principle-comparator but with simplified output. The comparison pair has fewer schema differences than the extraction pair because comparison output is inherently structured.
| Field | principle-comparator | pattern-finder |
|---|---|---|
alignment_note (in shared_principles) | Included — explains how principles align | Omitted |
contradictions (in divergence_analysis) | Tracked — counts genuinely conflicting claims | Omitted |
Note: Unlike the extraction pair (4 field differences), the comparison pair has only 2 differences because the core output structure (shared_principles, source_a_only, source_b_only, divergence_analysis) is identical.
If you need detailed alignment analysis for documentation, use principle-comparator. If you want a streamlined discovery experience, use this skill.
This skill identifies shared patterns, not verified truth. Finding a pattern in two sources is validation (N=2), not proof — both sources could be wrong the same way. Use N=2 as evidence, not conclusion.
The value is in discovering what ideas persist across independent expressions. Use your own judgment to evaluate truth and relevance.
Built by Obviously Not — Tools for thought, not conclusions.
基于 SOC 职业分类