Results for “l-eval”

Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator.

Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.

Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.

Guides the creation, modification, and evaluation of agent skills, including running benchmarks and optimizing descriptions for better triggering.

Automatically evaluates Claude Code sessions to extract reusable patterns and save them as learned skills.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop.

Guides the creation of high-quality MCP servers that let LLMs interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard.

Understand blockchain technology, interact with smart contracts, and evaluate when distributed ledgers solve real problems.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Deobfuscates malicious JavaScript code used in web-based attacks, phishing pages, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control flow obfuscation to reveal the original malicious logic.

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

利威尔（少年漫）认知与表达框架（压缩蒸馏）：兵长洁癖战力、矮个子反差、残酷抉择 触发：进击的巨人 等。虚构；非仇恨教唆

Acceptance Eval

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles.

Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.

Outcome Eval

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Write RFPs with requirements, evaluation, and timeline. TRIGGERS - Use when user needs help with rfp-writer related tasks.

Detect and evaluate arbitrage opportunities across sportsbooks and prediction markets. Calculate guaranteed-profit scenarios, middle bets, and cross-platform price discrepancies. Use when comparing odds across books, calculating arb percentages, evaluating middle opportunities, or building odds-comparison workflows. Also trigger for 'arb bet', 'sure bet', 'arbitrage', 'odds comparison', 'middle bet', 'risk-free bet', or 'line shopping'.

>- Skill Refiner, the eval-guided improvement loop for SKILL.md files. Runs the bootstrap, score, propose, apply, and status cycle as a thin wrapper over the published @intentsolutions/refiner CLI, proposing safe, minimal, bounded SKILL.md edits and accepting an edit only when a held-out eval score strictly improves with no regression on any other case. Ships a 3-layer cost-tiered hook architecture (sinker, line, hook) that gates skill quality at edit time, end of turn, and commit time. Use when improving an existing skill, refining a SKILL.md against measured behavior, bootstrapping an eval set for a skill, or gating skill edits before they ship. Trigger with "/j-rig", "refine this skill", "bootstrap an eval set", "propose a skill edit", "promote the candidate", or "skill refiner status".

Score an unbuilt business idea on desirability, viability, feasibility, distribution wedge, why-now, founder-market-fit, market size, alternatives, defensibility, capital intensity, and regulatory/ethical risk — and return a GO / ITERATE / KILL verdict with kill criteria and a next kill test. Load when the user asks to evaluate a business idea, score a startup idea, screen an idea, decide whether to pursue this venture, do an idea review, or says "is this a good business idea", "should I build this", "evaluate this startup", "screen this idea", "go/no-go on this idea", "kill or pursue". Sub-skill of `venture-exploration`. Calls `fermi` for sizing, `assumption-mapping` for hidden beliefs, optional `pre-mortem` / `adversarial-hat` for high-stakes ideas. Does NOT evaluate built products — for that use `reality-check`.