paiml

Generates comprehensive, LLM-optimized codebase context using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Starting work on unfamiliar codebases - Onboarding to new projects or repositories - Need quick understanding of project architecture - Preparing for refactoring or feature implementation - Creating documentation or technical specifications Outputs highly compressed markdown (60-80% reduction) optimized for LLM consumption. Supports 25+ languages with architecture visualization, complexity heatmaps, and dependency graphs.

Analyzes polyglot codebases with multiple programming languages using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Working with projects containing multiple programming languages - Assessing cross-language integration patterns and quality - Understanding language distribution and architectural boundaries - Comparing quality metrics across language ecosystems - Identifying language-specific best practices violations Supports 25+ languages including Rust, Python, TypeScript, JavaScript, Go, C++, Java, Ruby, PHP, Swift, Kotlin, C, C#, Scala, Haskell, Elixir, Clojure, Dart, Lua, R, and more. Provides unified quality assessment across heterogeneous codebases.

Analyzes code quality, complexity, and technical debt using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User mentions "code quality", "complexity", "technical debt", or "maintainability" - Reviewing code or conducting code review - Modifying or refactoring existing code files - Creating pull requests or preparing commits - Investigating performance or quality issues Supports 25+ languages including Rust, Python, TypeScript, JavaScript, Go, C++, Java, Ruby, PHP, Swift, and more. Provides cyclomatic complexity, cognitive complexity, maintainability index, dead code detection, and technical debt annotations (SATD: TODO, FIXME, HACK comments).

Provides automated refactoring suggestions and complexity reduction strategies using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User requests code refactoring, optimization, or improvement - Complexity analysis reveals high-complexity functions (cyclomatic > 10) - Code review identifies maintainability issues - Technical debt needs to be addressed systematically - Preparing legacy code for modernization Supports 25+ languages with data-driven refactoring recommendations based on complexity metrics, mutation testing results, and industry best practices (Fowler's refactoring catalog).

Tracks and manages technical debt using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User asks about technical debt, TODO comments, or code quality issues - Planning sprint work and need to prioritize debt repayment - Conducting code audits or technical debt assessments - Tracking debt accumulation trends over time - Creating technical debt reports for stakeholders Detects SATD (Self-Admitted Technical Debt) annotations: TODO, FIXME, HACK, XXX, NOTE comments. Provides debt quantification in hours, prioritization by severity, and repayment tracking.

Implementation blueprint from spike experiments — SEP-2640 Skills support AND the schema-server toolkit lift (config-driven MCP servers for SQL / GraphQL / OpenAPI backends). Requirements, proven patterns, multi-dialect SQL connector trait, two-axis Skills (Type 1 build-time vs Type 2 runtime SEP-2640), and the dual-surface invariant. Auto-loaded during implementation work.

Help a developer design a config.toml for a PMCP schema-server toolkit deployment, applying the Pareto principle to curated tools and code-mode policy.

Generate validated GraphQL queries against this server's schema

Demonstrates the simplest possible MCP skill

Process customer refund requests per company policy

Systematic coverage gap analysis and test writing for the APR Model QA Playbook. Uses pmat query --coverage-gaps for highest-ROI targets, provides test patterns for each crate (proptest for gen, Evidence assertions for runner, MQS scoring for report), handles CommandRunner trait extensions, clippy pedantic/nursery landmines, and ExecutionConfig construction. Target: 95% library coverage.

Diagnoses and fixes G0-G4 gateway failures in model qualification runs. Interprets MQS zero-scores, evidence JSON, and stderr output. Covers LAYOUT-002 violations, config.json mismatches, garbage detection, crash analysis, and all 5 contract invariants (I-1 through I-5). Includes the GarbageOracle detection patterns and conversion diagnostics.

Guides end-to-end HuggingFace model certification: playbook creation from templates, running qualification at any tier (Smoke/MVP/Quick/Standard/Deep), collecting evidence, computing MQS scores with G0-G4 gateways, updating models.csv, and syncing the README certification table. Covers SafeTensors ground truth, LAYOUT-002 compliance, and Popperian falsification methodology.

Find which provable-contracts YAML contracts a Hugging Face model needs, create missing contracts, generate Rust artifacts, and implement all stubs. Triggers on: "find contracts", "contract coverage", "what contracts does X need", "which contracts for", HF model analysis, model contract gap analysis.

QA the installed apr CLI binary — pull a model, test every subcommand, hunt for bugs, file GitHub issues. Use when asked to QA apr, test apr, or check apr-cli.

Dogfood forjar — rebuild, install, exercise every command, prove the C1–C10 claims and contracts against a sandboxed stack, check quality, file bugs