Results for “evolution”

Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Large-scale data migrations with validation, rollback, and zero-downtime strategies. Activate on: data migration, database migration, zero-downtime migration, dual-write, backfill, cutover, data validation, schema migration. NOT for: schema evolution in streams (use schema-evolution-manager), API versioning (use api-versioning-backward-compatibility).

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Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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API migration strategies, deprecation workflows, and header/URL/content versioning. Activate on: API versioning, backward compatibility, deprecation, breaking change, API migration, v1 v2, sunset header. NOT for: schema evolution in data (use schema-evolution-manager), gateway routing (use api-gateway-reverse-proxy-expert).

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Evolve prompts/regex/SQL/code with Imbue's evolution loop.

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Plans safe API evolution with versioning strategies, client migration guides, deprecation timelines, and backward compatibility considerations. Use for "API versioning", "deprecation planning", "API evolution", or "breaking changes".

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Improve prompts, regex patterns, SQL queries, or small code snippets through repeated scoring and revision. The skill helps define measurable tests, generate new candidates with an AI model, and inspect results to find better-performing versions.

• Improve a prompt against example answers
• Refine a regex using pass rates
• Improve SQL or code using tests

Inspect evolved prompt configurations, validate their structure, and view the system prompts they produce. You can also classify failures in run transcripts to support reviews before adopting or changing an agent policy.

• Reviewing the system prompt before adopting an evolved policy
• Validating prompt configuration changes in CI
• Classifying failures in an agent run transcript

Create a fixed JSON evaluation suite from a repository's existing tests and check its structure for errors. Use the suite with harness-evolve to compare variants against consistent tasks, independently of the repository's normal test command.

• Create a fixed evaluation suite from existing tests
• Check evaluation suite structure in CI
• Compare variants across commits using consistent tasks

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Design a process for AI agents to learn from execution records. Evaluate outcomes, actions, and quality, choose whether to update knowledge, instructions, code, or model parameters, and define independent validation, release checks, and rollback procedures.

• Turning repeated agent failures into reusable guidance
• Designing evidence-based reviews of execution records
• Converting browser actions into validated workflows

Compare two versions of a project and turn migration patterns into instructions for GitHub Copilot. Document API replacements, new conventions, obsolete patterns, and validation steps so future code changes follow the same approach.

• Documenting framework version upgrades
• Creating rules for replacing a technology
• Preserving conventions after architectural refactoring

Run repeated code improvement experiments with multiple agents. It plans and reviews candidate changes, compares benchmark results, merges candidates that pass rechecking, and records progress until a target, iteration limit, or another stopping condition is reached.

• Comparing several code improvement ideas with benchmarks
• Running repeated experiments toward a measurable target
• Tracking candidate scores and experiment history

Organize scattered concept notes into a map of recurring ideas. Merge duplicates, rank concepts into four tiers, trace how stronger ideas developed across sources, and assess which notes to keep, rewrite, merge, or remove.

• Consolidate duplicate idea notes
• Find recurring themes across notes
• Trace how an idea developed over time

Trace how one idea developed across stored notes and records. Identify its earliest documented mention, clearest expression, changes of stance, contradictions, abandoned branches, and current form, with dated evidence and explicit gaps where sources are missing.

• Trace an idea to its earliest documented mention
• Review changes and contradictions in your thinking
• Clarify your current view on a topic

Set up, debug, and extend PicoClaw using its own commands and configuration structure. Get guidance on model authentication, external tool connections, scheduled tasks, and how conversations are routed and stored.

• Setting up models and authentication in PicoClaw
• Configuring and troubleshooting MCP server connections
• Setting up recurring tasks

Turn an OpenAPI specification into starter code for an MCP server, which connects AI agents to API tools. Support Python or TypeScript implementations, validate tool definitions, and plan changes that preserve compatibility.

• Exposing an existing REST API to an AI agent
• Creating a Python or TypeScript MCP server scaffold
• Checking tool definitions before integration tests

Helps founders and first-time CEOs review delegation, calendar use, energy, and leadership blind spots. Provides frameworks and reflection questions for adapting their role as the company grows, addressing burnout concerns, and preparing others to take over responsibilities.

• Choosing which tasks and decisions to delegate
• Reviewing a CEO's calendar and sources of exhaustion
• Reassessing leadership responsibilities as the company grows

Support startup AI strategy by comparing API use, fine-tuning—additional training of an existing model—and in-house development. Assess three-year costs, regulatory risk, self-hosting breakeven points, and hiring priorities to inform leadership decisions.

• Compare API use, fine-tuning, and in-house development
• Assess regulatory risks for an AI use case
• Calculate when self-hosting becomes cost-effective

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.

• Assessing customer data for AI training
• Choosing data architecture and what to build or buy
• Evaluating data assets before fundraising or M&A

Research a product, company, concept, or person by tracing its history and comparing it with current peers. Combine both perspectives to explain strengths, weaknesses, and possible futures, then deliver a structured PDF report with sources.

• Investigating a product’s history and current competitors
• Understanding the origins and evolution of a technical concept
• Examining a company’s strategic decisions and market position

Extends the core PUA skill with local performance notes, recovery of task state after conversation compression, KPI report cards, and style switching. It tracks established working habits across sessions and keeps feedback in a local file.

• Recording working standards across sessions
• Resuming attempts after conversation compression
• Reviewing work through a KPI report card

Develop varied ideas for names, writing angles, and problem solving by exploring five candidates internally and presenting the strongest. It also expands small example collections into diverse datasets for evaluation, training, or testing.

• Finding a distinctive product name
• Developing a fresh angle for a blog post
• Exploring solutions to a technical problem