pattern-recognition
Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mert
npx skills add majiayu000/claude-skill-registry --skill pattern-recognition --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Pattern Recognition Mathematics advances by the interplay of conjecture and proof. Pattern recognition — the art of observing regularity in data and formulating precise conjectures — is the creative engine that drives discovery. Euler, Gauss, and Ramanujan were supreme pattern-finders whose conjectures shaped centuries of subsequent mathematics. This skill covers systematic methods for detecting patterns, generating conjectures, and calibrating confidence before committing to a proof attempt. **Agent affinity:** ramanujan (intuitive pattern discovery, sequence analysis, identity generation) **Concept IDs:** math-pattern-recognition, math-experimental-probability, math-data-representation, math-functions ## Part I — Sequence Analysis ### The OEIS (On-Line Encyclopedia of Integer Sequences) The OEIS (oeis.org), founded by Neil Sloane in 1964 and now containing over 370,000 sequences, is the single most important resource for pattern recognition in mathematics. Given the first several terms of a sequence, searching the OEIS often identifies the sequence, its name, its generating function, known formulas, and references. **Strategy.** When encountering an unfamiliar sequence: 1. Comp
- Part I — Sequence Analysis
- The OEIS (On-Line Encyclopedia of Integer Sequences)
- Recurrence Relations
- Generating Functions
- Part II — Pattern Detection Methods
- Finite Differences
- Ratio Analysis
- Modular Patterns
- Part III — Conjecture Generation Workflow
- Step 1: Gather Data
- Step 2: Look for Patterns
- Step 3: Formulate a Precise Conjecture
- Step 4: Test the Conjecture
- Step 5: Attempt a Proof
What does the pattern-recognition skill do?
Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mert
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pattern-recognition --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.