Results for “reproducible-scoring”

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Create new skills, modify existing ones, and measure their performance through iterative evaluation and benchmarking.

PRISMA Scoping Review Pipeline

Screens resumes with skill matching, experience evaluation, and bias-free candidate ranking

Turn a pile of accounts into a stack-ranked priority list with a reason on every row. A layered score (gates first, then an evidence-weighted base rank over the signals you actually have, then bounded boosts for product usage and buyer intent) that stays fair across channels and never scores a blank field as a zero. Built for B2B GTM teams, customizable to your signals and your ICP. Trigger on "score these accounts", "rank by fit", "composite ICP score", "stack-rank my list", "who should I work first", "prioritize these leads", or any multi-signal account qualification.

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

Create irresistible offers using the Value Equation, bonus stacking, risk-reversing guarantees, and ethical scarcity.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Build a scalable outbound B2B sales machine with specialized SDR, AE, and CSM roles, using Cold Calling 2.0, referral emails, ANUM qualification, and pipeline math to generate predictable revenue.

Lightweight ContentShake AI self-check the /draft stage can call before saving. Returns just SEO + Quality scores (no full optimization) so the writer knows whether the draft is in winning territory before /quality-check runs. Fails soft when SEMRUSH_API_KEY is unset.

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples. Use when the user runs /si:extract or asks to package a recurring solution from memory into a skill.

Create new skills, modify and improve existing skills, and measure skill performance through iterative evaluation and benchmarking.

Defines ideal customer profile filters, scores inbound and outbound leads, and builds a lightweight qualification rubric to sharpen pipeline focus for founder-led sales.

Update or correct an existing skill file based on judge feedback or improved understanding.

Generates four improved variations of any installed skill, scores them against a weighted rubric, and applies the winning version while preserving the original.

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

A git SHA pins the transformation.

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

Builds Retrieval-Augmented Generation pipelines with vector stores, chunking strategies, and reranking

Score any piece of writing for AI-generation tells and produce a weighted 0-100 scorecard with flagged evidence and ranked fixes. Use this skill whenever the user asks "does this sound AI-written", "run this through the AI detector", "score this writing", "check this for AI tells", "would this pass as human", "humanize check", or wants any article, blog post, email, or copy audited for AI patterns before publishing. Also use it when the user pastes or points to text and asks how it reads, whether it's too "ChatGPT-ish", or wants a QA pass on generated content. Works on pasted text, files, and URLs.

Scores genetic compatibility between all male-female pairings in a Genomebook generation, ranking optimal mating pairs based on heterozygosity, trait complementarity, and disease risk.

Deterministic pixel art upscaling using EPX/Scale2x, hq2x/hq4x, and xBR algorithms that add valid sub-pixels through pattern recognition. Activate on 'pixel art scaling', 'EPX', 'Scale2x', 'hq2x', 'hq4x', 'xBR', 'retro game upscaling'. NOT for AI/ML upscaling, photo enlargement, or simple nearest-neighbor.

SUPERSEDED by persistent-cache-gap-filling (v2.8.0). Cache data during symbol selection for instant repeat runs.

Parse RFP/RFI/RFQ documents, score fit per requirement with verifiable proof points, generate win-themes, estimate winrate, and produce a bid/no-bid recommendation using the Shipley method.

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

当需要在众多机会中筛选出投入产出比最高的选项，以最小化风险并最大化潜在回报时

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

Use when making a forward-looking claim with a checkable outcome (reply within 24h, error rate will drop, this skill will see more use) — record to state/predictions.jsonl with a review horizon so reflection can grade you later. Closes the in-the-moment double-loop.

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.

Clone a known-good report + retarget bindings (preview)

Write, debug, and test NumPyro probabilistic programs on JAX with correct shapes, PRNG keys, and inference choice.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.