claude-skills

Use when working in a project that has a `.agents/` directory or when the user asks about Conway Architecture, the conway-architecture plugin, domain agents, owned_paths enforcement, or how to coordinate a persistent subagent team that shadows the application's architecture

Use when reading or writing OST (Outcome / Strategy / Tactic) graph data in the `agents` TypeDB database - covers the falsifiability rubric, the three-layer discipline, ID conventions, lifecycle transitions, and the structured Task fields that replace generic descriptions

Use when interacting with a TypeDB 3.x database via the typedb MCP tools (typedb-query, typedb-database_schema, etc.) - covers schema-first workflow, transaction types, TypeQL 3.x syntax (which differs materially from 2.x), and the most common failure modes

Use when analyzing another branch's iteration journals to extract findings, decisions, and insights from divergent work

Use when user wants detailed status report for single autonomy branch including iteration timeline and metrics progression

Use when saving current iteration progress mid-conversation, before context compaction, or at interim pause points

Use when user wants to compare two autonomy branches to see different approaches, metrics, and outcomes

Use when setting up a new open-ended goal for autonomy tracking, before starting the first iteration

Use when concluding work on an open-ended goal to write iteration journal entry documenting work performed, decisions made, and state changes

Use when user wants to create new autonomy branch from current commit or specific past iteration

Use when user wants to create new autonomy branch with dedicated worktree for parallel agent workflows

Use when user wants to inventory autonomy branches with custom sorting, grouping, or filtering

Use when user wants to see all autonomy worktrees with their status

Use when user wants to safely remove an autonomy worktree while preserving the branch

Use when user wants to assess progress toward an open-ended goal by reading and summarizing all iteration journals

Use when user wants to set up slime mold exploration strategy with parallel autonomy branches for genetic algorithm approach to problem-solving

Use when beginning a new conversation to work on an open-ended goal, loading context from previous iterations through iteration journals

Component skill for creating compelling data-driven presentations and whitepapers using marp and pandoc with proper citations and reproducibility

Systematic data quality remediation - detect duplicates/outliers/inconsistencies, design cleaning strategy, execute transformations, verify results (component skill for DataPeeker analysis sessions)

Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist

Component skill for creating effective visualizations (terminal-based and image-based) in DataPeeker analysis sessions

Identify foreign key relationships between tables using heuristics, value overlap analysis, and referential integrity checks

Systematic exploratory data analysis process - discover patterns in unfamiliar data, identify meaningful insights, formulate specific questions for deeper investigation

Systematic process for investigating open-ended questions - decompose vague questions into specific sub-questions, map to data, investigate incrementally, synthesize findings

Rigorous hypothesis testing process for data analysis - formulate hypotheses before looking at data, design tests, analyze systematically, interpret with skepticism

Systematic CSV import process - discover structure, design schema, standardize formats, import to database, detect quality issues (component skill for DataPeeker analysis sessions)

Component skill for systematic result interpretation with intellectual honesty in DataPeeker analysis sessions

Systematic marketing experimentation process - discover concepts, generate hypotheses, coordinate multiple experiments, synthesize results, generate next-iteration ideas through rigorous validation cycles

Use when conducting customer discovery interviews, user research, surveys, focus groups, or observational research requiring rigorous analysis - provides systematic 6-phase framework with mandatory bias prevention (reflexivity, intercoder reliability, disconfirming evidence search) and reproducible methodology; peer to hypothesis-testing for qualitative vs quantitative validation

Component skill for systematic data profiling and exploration in DataPeeker analysis sessions

Use when working with SQLite databases in DataPeeker analysis sessions - querying data, importing CSVs, exploring schemas, formatting output, or optimizing performance. Provides task-oriented guidance for effective SQLite CLI usage in data analysis workflows.

Component skill for systematic SQL query development in DataPeeker analysis sessions