403 skills · sorted by relevance
Evaluate Jira ticket complexity + inconsistencies via the jira-evaluator agent. Read-only. Triggers: 'evaluate KEY', 'analyze ticket complexity for KEY', 'check ticket quality for KEY'. NOT for estimation (mk:jira-estimator); NOT for full RCA (mk:jira-analyst).
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Two-phase code quality skill: (1) review codebase for structural smells — file LOC, method LOC, accidental complexity, test readability — producing a structured report, then (2) fix all smells via provable refactorings with ACN micro commits and green-bar-at-all-times discipline. Use this skill when the user says 'sweep', 'code review', 'find code smells', 'check method lengths', 'review for complexity', 'tidy up the codebase', 'refactor smells', or asks about long methods, test readability, or accidental complexity. Also trigger when users ask to 'clean up' or 'improve code quality' across multiple files.
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Use when the operator asks for an authoring-time complexity hint or a complexity guide preview before commit.
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Design modules that offer substantial behavior through a small interface, the rules callers must understand. Use shared design terms to simplify usage, contain complexity, choose where implementations can vary, and make behavior easier to test.
Design an LLM API workflow that controls spending through task-based model selection, budget tracking, selective retries, and caching repeated prompts. Use the examples to route simpler tasks to cheaper models and track cumulative costs.
Guide coding, reviews, and refactoring with explicit assumptions, simple implementations, focused changes, and testable goals. These behavioral guidelines help an AI agent avoid unnecessary complexity and verify that its changes meet the request.
Review code changes and connected code for bugs, security risks, load problems, missing tests, slowdowns, and unnecessary complexity. Get prioritized findings with concrete failure scenarios, small fixes, and the consequences of leaving each issue unresolved.
Make working code easier to read and maintain while preserving its behavior. The skill identifies tangled conditions, unclear names, duplication, and unnecessary abstractions, then guides small refactoring steps checked against existing tests and project conventions.
Audit a codebase for unnecessary complexity, including dead code, duplicated state, redundant interfaces, and obsolete compatibility paths. Receive evidence-backed simplification candidates, or authorize changes that remove justified targets and validate the behavior that must remain.
Have agents with different roles challenge a proposed approach, exposing weak assumptions, missed interactions, and unnecessary complexity. Surviving findings go to a dedicated planning agent, which produces sequenced tasks with explicit success criteria.
Clean up AI-generated code in branch changes or selected files while preserving behavior. Establish passing regression tests first, then remove unnecessary code and complexity, run project quality checks, and report both changes and skipped issues.
Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.
Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.
Analyze pull requests and source code across multiple programming languages to identify complexity, security risks, and structural problems. Produce prioritized findings and structured review reports to guide code review decisions.
Review code changes for unnecessary complexity, including unused flexibility, avoidable dependencies, and abstractions with little purpose. Receive concise findings that name what to remove, what can replace it, and how many lines could be cut.
Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.
Group bug reports by their underlying cause and plan shared fixes. This skill guides reproduction, checks for unnecessary complexity, and requires named test evidence before treating related issues as resolved.
Compare code structure across two commits, tags, or directories to identify security-relevant changes. Produces a report on new attack paths, privilege boundaries, complexity, and how widely a change could affect other functions.
Plan and implement features with a workflow that adapts to their complexity. It connects testable requirements to tasks, tests, and small commits, then checks the finished work against the specification through an independent verification pass.
Interpret .NET coverage reports, which show code exercised by tests, to explain missing coverage and stalled progress. Calculate the gains needed to reach a target and, when requested, combine coverage with complexity to rank risk.
Keep code, tests, documentation, configuration, and other changes as tight, clear, performant, and minimal as possible without weakening behavior. Use during implementation to prevent unnecessary complexity, after behavior is proven to consolidate working changes, before handoff for a full branch audit, and when asked to simplify, tighten, clean up, reduce verbosity, remove complexity, or minimize existing work.
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AI DevKit · Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.
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Simplify code, plans and systems without losing required outcomes; delete machinery that earns nothing; for code, AST-measured complexity with regression gates. Triggers: simplify, refactor, complexity, overengineered, dead code, useless guard.
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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
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