Results for “refinement”

Generate systematic refinement steps from high-level specifications to concrete implementations in Isabelle/HOL or Coq, preserving correctness obligations at each step. Use when working with formal verification, program refinement, proof development, or when translating abstract specifications into executable code while maintaining formal guarantees. Supports data refinement (abstract types → concrete structures), algorithmic refinement (specifications → algorithms), and stepwise refinement with proof obligations.

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Improve existing code through repeated cycles of testing, implementation, and refactoring. The skill covers performance bottlenecks, error recovery, code coverage, and documentation, helping developers refine behavior and structure while keeping tests passing.

• Refactoring existing code while keeping tests passing
• Adding tests before implementing expected behavior
• Investigating slow database access and cache usage

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Review agent work histories to identify recurring failures, repeated discoveries, and stalled tasks. Improve the prompts, specialist agents, skills, tools, and memory that guide future work, then submit those changes separately for review.

• Addressing recurring command or tool failures
• Recording findings that agents repeatedly rediscover
• Turning repeated procedures into skills or tools

Design guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.

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Organize new features, complex implementations, and architecture changes into the SPARC stages: specification, pseudocode, architecture, refinement, and completion. Define requirements and acceptance criteria, consult existing patterns, and record lessons for future work.

• Defining requirements for a new feature
• Organizing a complex implementation
• Planning an architecture change

Simulate a uniform gas mixture in a closed, insulated reactor at constant volume or pressure. Calculate ignition delay from the peak temperature rise rate, export temperature and species histories, and check conservation and numerical stability.

• Calculate ignition delay at constant volume or pressure
• Inspect temperature and species histories
• Compare ignition delays across reaction mechanisms

Convert agreed goals, constraints, and success criteria into a Seed specification an agent can execute and verify. A quality review checks consistency and testable outcomes, with any suggested refinements left for the user to approve.

• Formalize requirements agreed in conversation
• Create a specification after a requirements interview
• Check acceptance criteria for measurable outcomes

Run repeated rounds of code fixes, performance improvements, or other refinements with defined goals and progress tracking. Evaluate each round, stop when appropriate, and optionally use numeric measurements to keep improvements or roll back regressions.

• Repeat fixes and tests until all tests pass
• Improve response time toward a target
• Refine error messages against clarity criteria

Turn written instructions into infographics for statistics, comparisons, timelines, and more. Choose layouts and color palettes, optionally research supporting facts, and receive versioned images with automated quality reviews and refinement when scores fall below the chosen threshold.

• Turning survey results into a statistical infographic
• Creating a side-by-side product comparison
• Visualizing company history or project milestones

Develop and review machine learning approaches through at least three rounds of critique and refinement. Compare models with simple reference predictions, question data assumptions, and investigate overfitting, feature choices, and evaluation methods before accepting a solution.

• Comparing model choices with simple reference predictions
• Investigating gaps between training and test performance
• Reviewing feature choices and data leakage risks

Create synthetic defect images for detector training using clean images and masks that mark defect regions. Guide mask placement, Cosmos fine-tuning, image generation, evaluation, quality refinement, and automatic assignment of provisional labels.

• Generating synthetic PCB defect images
• Placing defect masks on clean images
• Fine-tuning Cosmos for a defect dataset

Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.

• Calibrating cameras from synchronized MP4 recordings
• Uploading local settings and alignment files
• Evaluating calibration against ground-truth data

Run the measurement and ceremony layer of a delivery team: the metric set, estimation and capacity, the five sprint ceremonies, and the sprint report and quarterly rollup. Use this skill whenever the user is acting as PMO or scrum lead, planning a sprint, running standup, refinement, review, or retro, measuring velocity, cycle time, or throughput, checking capacity, or reporting on how a team is doing. Triggers on PMO, sprint planning, plan the sprint, standup, refinement, backlog grooming, sprint review, retro, velocity, capacity, cycle time, lead time, throughput, WIP, burndown, carryover, sprint report, story points to hours, how is the team doing, delivery metrics, agent lanes. Also triggers when the user asks how much work fits in a sprint or whether a team is on track, even if they never say sprint or PMO.

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Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

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Run a self-refinement loop when a Blender result is subpar, user expectations are not met, validation fails, or repeated issues reveal missing skill depth. The loop diagnoses the gap, decides whether to repair the artifact or acquire/refine a generic skill, sanitizes lessons for reusable publication, updates docs/versioning, validates, and prepares stage/commit/push when explicitly requested.

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Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.

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Premium UI/UX design audit and refinement skill. Conducts systematic visual audits of existing apps and produces phased, implementation-ready design plans. Use this skill whenever the user asks to audit a UI, improve an app's visual design, make an interface feel more polished or premium, review design consistency, fix visual hierarchy, or refine spacing/typography/color. Also trigger when the user says "design review", "make it look better", "UI polish", "visual refinement", "design pass", "audit the design", or references making an app feel more professional. This skill is purely visual — it does not touch functionality, logic, or features. It elevates what exists.

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Normative sufficiency conditions for Initial Boundary Conditions (IBCs) and the SOP for the cheap-tier boundary refinement loop (/boundary). Trigger when: - Crafting, auditing, or refining a prompt/IBC destined for an expensive (architect-class) model or an autonomous worker dispatch. - Evaluating whether a task frame is sufficient to bound an agent walk. - Prompt contains: /boundary, IBC, initial boundary condition, boundary contract, sufficiency conditions, worker prompt, prompt refinement.

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SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.

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Review plans for APIs and developer tools through the eyes of their users. Explore developer needs, compare competitors, and identify onboarding friction, with modes for competitive differentiation, detailed refinement, or critical gaps.

• Reviewing an API or SDK plan
• Finding friction in developer onboarding
• Comparing developer experience with competitors

Guide an iterative review of a built interface against its design brief, using critique and adjustment to improve visual alignment. This catalogue entry points agents to the upstream workflow, which requires a separate installation.

• Review a built interface against its design brief
• Refine visual differences through repeated adjustments
• Plan a design-to-code review cycle

Organize software development from requirements and architecture through testing, review, and deployment. Use specialized agent roles, shared memory, and test-driven development, where tests are written before code, to coordinate work across the project.

• Plan and implement a new feature
• Refactor legacy code with supporting tests
• Review code security and performance

Review implemented features against their requirements, add missing tests, and check performance constraints. Produce documentation, a table linking requirements to code and tests, and evidence showing whether the feature is ready for deployment.

• Review an implementation against its specification
• Add tests for uncovered requirements
• Link requirements to code and passing tests