Plugins

12 plugins

Advanced development workflow orchestration with BDD support and self-improving skills

8 skills · plugin

Agent and skills for code simplification and refactoring to improve code quality while preserving functionality

3 skills · plugin

Customer Journey Mapping

Map the end-to-end customer journey, identify friction points, and uncover improvement opportunities.

5 skills · plugin

Optimize Qdrant Search Quality

Diagnose and improve Qdrant search relevance by isolating embedding, config, or query issues.

3 skills · plugin

Diagnose and Fix AI Workflow

Diagnoses an AI workflow and applies structured improvements for quality and reliability.

4 skills · plugin

Web Performance Audit and Optimize

Measure performance, identify bottlenecks, and apply fixes to improve Core Web Vitals.

9 skills · plugin

SEO Audit and Fix

Audit a website for SEO issues, fix metadata and structured data, and verify improvements.

10 skills · plugin

Skills for running, generating, analyzing, and improving .NET tests: test execution, filtering, platform detection, coverage, testability, and MSTest workflows.

20 skills · plugin

SEO Audit to Optimization

Audit a website for SEO issues, analyze on-page elements, and implement fixes to improve organic performance.

9 skills · plugin

Utility skills (decision-matrix, navigate, model-route, prompt-improver, grill-research, align-prompt). Useful for specialized one-off tasks like structured decisions, adversarial research grilling, or aligning a draft prompt to an Opus model. NOT for: daily development workflow — these are situational tools, not always-on skills.

4 skills · plugin

Pocock Engineering

Engineering workflow skills from Matt Pocock's skills repo (triage, to-issues, to-prd, tdd, prototype, diagnose, grill-with-docs, improve-codebase-architecture, zoom-out). Covers issue lifecycle, TDD, prototyping, architectural review, domain grilling, and PRD generation. NOT for: daily development workflow — install individual skills as needed.

7 skills · plugin

@alirezarezvani

Engineering Team

32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security,

16 skills · plugin

Results for “improv”

21 skills

self-improvement-loops

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

16.9k · bundle

Evaluates the agent's own work, catches mistakes, and improves permanently through self-reflection, self-criticism, and learning from corrections.

cuopt-skill-evolution

Detects generalizable learnings from problem-solving interactions and proposes skill updates to improve future performance.

Continuously monitors system performance, identifies improvement opportunities, and orchestrates skill discovery and creation to autonomously evolve capabilities.

Builds reliable browser automation skills through iterative experimentation, running an inner agent to browse sites and improving navigation instructions until tasks pass consistently.

Iteratively reviews and fixes Claude Code skill quality issues by running automated fix-review cycles using the skill-reviewer agent until they meet standards.

More results

Builds reliable browser automation skills by iteratively running a browsing task, reading the trace, and improving the navigation strategy until it passes consistently.

agent-platform-eval-flywheel

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology, including dataset creation, metric selection, failure analysis, and iterative improvement.

14.4k · bundle

Guides the creation, iterative improvement, and evaluation of agent skills, including drafting, testing, benchmarking, and optimizing descriptions.

Improves skills by analyzing execution data to identify patterns in successful versus failed runs, staging changes for human approval.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

30.2k · bundle

tao-run-automl-deft-pipeline

Runs a three-phase AOI training pipeline: AutoML HPO baseline, DEFT iterative data improvement, and AutoML refinement on the augmented dataset.

context-engineering

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

agent-self-evaluation

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

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

dual-axis-skill-reviewer

Review AI agent skills using a dual-axis method: deterministic code-based checks and LLM deep review, with weighted scoring and improvement recommendations.

capability-evolver

Analyzes runtime history to identify failures and inefficiencies, then autonomously writes improvements using a protocol-constrained evolution engine. Communicates with EvoMap Hub via a local Proxy mailbox.

Analyzes past agent sessions to identify recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them, tracking improvement over time.

tao-run-deft-aoi

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.