Results for “structure”

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `web-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Search the web using AIsa Scholar Web endpoint. Returns structured web results with titles, URLs, and snippets. Use when: the user needs web search, research, source discovery, or content extraction.

Use when writing or reviewing tests, or when asked how to write a good single test. Encodes the per-test rules behind the "test the contract / responsibility, not the implementation" principle — name and structure one behavior per `it`, drive through the public surface, stub only true external boundaries, control time and config via documented knobs, and keep tests clear, isolated, and refactor-resilient. The same rules drive both authoring (write mode) and auditing existing tests (review mode).

Post-exploitation workflow. Covers privilege escalation (Linux SUID/sudo/kernel, Windows UAC/service/token), persistence assessment, local enumeration, credential harvesting, and pivot preparation. Structured workflows for Linux and Windows targets using impacket, netexec, john, linpeas/winpeas, and standard Kali tools. Includes kernel exploit reference tables, GTFOBins exploitation chains, Potato attack selection, Docker/container escapes, DLL hijacking, SSH key harvesting, credential recovery from memory, and Windows token manipulation. Chains from /pentester or /credential-audit when access is obtained.

Restructure an academic paper's title and section structure to match a target journal's house style. Asks the user to specify the target journal first, then adjusts main.tex accordingly (title wording and case, section skeleton, heading case, numbering scheme, merging/splitting sections). Trigger when user says "paper style" / "paper-style" / "期刊风格" / "按目标期刊调整" / "调整为XX风格" / "JFE风格" / "convert to journal style" / "restructure for [journal]", or wants a paper's title/sections reformatted for a specific journal submission.

Provides a structured workflow for migrating an Android XML View to Jetpack Compose. This skill details the step-by-step process, from planning and dependency setup, to theming and layout migration, validation and XML cleanup. Use this skill when you need to migrate an XML View to Jetpack Compose in an Android project. It solves the problem of converting the UI of a legacy XML View into modern, declarative Compose components while maintaining interoperability.

Use when asked to set up AI agent templates with coordinator mode, implement persistent agent memory, compress context for long-running agents, build multi-agent workflows with skills/agents/workflows structure, or use the .agents/ folder convention for AI-native editors. Triggers on: 'ag-kit', 'antigravity kit', 'coordinator mode agent', 'persistent agent memory', 'context compression agent', 'agent workflow template', 'multi-agent coordinator', '.agents folder', 'agent skills workflows', 'ag kit init', 'template đa agent', 'bộ nhớ agent lâu dài'.

Report-only QA testing. Systematically tests a web application and produces a structured report with health score, screenshots, and repro steps — but never fixes anything. Use when asked to "just report bugs", "qa report only", or "test but don't fix". For the full test-fix-verify loop, use /qa instead. Proactively suggest when the user wants a bug report without any code changes. (gstack) Voice triggers (speech-to-text aliases): "bug report", "just check for bugs".

Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", "anti-corruption layer", "context mapping", "domain events", or "strategic design". Also trigger when splitting a monolith into services, defining microservice boundaries, or aligning code structure with business processes. Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.

Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Use when working on landing pages — creation, copywriting, design, optimization, or conversion rate improvement. Covers hero sections, above-the-fold content, value propositions, CTAs, landing page templates, and high-converting page structure. Applies to sales pages, lead capture pages, product pages, and marketing site pages. Use this skill for landing page copy, landing page design, landing page optimization, CTA writing, conversion rate optimization, hero section content, value proposition work, and any page builder or landing page template tasks.

Query ChEMBL via the chembl_webresource_client Python client for curated bioactive molecules and drug-like compound libraries at scale — search compounds by structure or physicochemical properties, retrieve bioactivity measurements (IC50, Ki, EC50), and find inhibitors of a target. Use when screening chemical libraries, mining curated bioactivity for a protein, running SAR studies, or sourcing medicinal-chemistry data; for measured protein-ligand binding affinities (Ki/Kd/IC50) prefer alterlab-bindingdb instead. Part of the AlterLab Academic Skills suite.

Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a protein target, doing lead optimization, polypharmacology analysis, or structure-activity relationship (SAR) studies; for curated bioactivity mining or drug-like compound library screening at scale prefer alterlab-chembl instead. Part of the AlterLab Academic Skills suite.

Design motivation systems using Autonomy, Mastery, and Purpose (AMP) for products and teams. Use when the user mentions "intrinsic motivation", "gamification isnt working", "team incentives", "autonomy", "mastery", "purpose-driven", "employee engagement", or "reward systems". Also trigger when designing onboarding progression systems, fixing broken gamification, or building team structures that sustain high performance. Covers why carrot-and-stick fails and how to build progress systems. For habit-forming product loops, see hooked-ux. For retention behavior design, see improve-retention.

Expert copy editor for Quarto (.qmd) files. Checks grammar, spelling, punctuation, and academic writing quality. Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to proofread, check grammar, fix typos, or review prose in a .qmd document. For APSA style rules (numbers, citations, capitalization, abbreviations, neutral language), use the apsa-style skill instead. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.

Audit a page or a site for both classic search and answer-engine visibility, then return prioritized fixes so an AI answer engine will actually quote you. Checks entity clarity, structured answers, schema, internal links, and freshness, and ranks every fix by effort against impact. Trigger on "audit my SEO", "why don't I show up in AI answers", "will ChatGPT cite my page", "check my page for search", "AEO audit", or "how do I rank for this".

Query the EMBL-EBI InterPro REST API for protein family, domain, and functional-site annotations integrated from member databases (Pfam, PANTHER, PRINTS, SMART, SUPERFAMILY, CDD, ProSite, NCBIfam, and others). Use when predicting protein function, analyzing or comparing domain architecture, classifying a protein by family or homologous superfamily, resolving a Pfam/InterPro accession, or mapping a protein's signatures to GO terms. Not for raw UniProt entry/FASTA retrieval or AlphaFold 3D structures. Part of the AlterLab Academic Skills suite.

Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.

Build empowered product teams using discovery and delivery dual-track. Use when the user mentions "product discovery", "empowered teams", "feature factory", "product roadmap", "opportunity assessment", "product vision", "product-led growth", or "discovery vs delivery". Also trigger when restructuring product teams away from output-driven models, setting product strategy, or defining what to build next based on outcomes. Covers product discovery techniques, team structure, and continuous value delivery. For customer interviews, see mom-test. For ongoing discovery systems, see continuous-discovery.

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).

Structure software around the Dependency Rule: source code dependencies point inward from frameworks to use cases to entities. Use when the user mentions "architecture layers", "dependency rule", "ports and adapters", "hexagonal architecture", "use case boundary", "onion architecture", "screaming architecture", or "framework independence". Also trigger when decoupling business logic from databases or frameworks, defining module boundaries, or debating where to put business rules. Covers component principles, boundaries, and SOLID. For code quality, see clean-code. For domain modeling, see domain-driven-design.

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", or "decompose conditional". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation to apply to a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

Reviews code using pi CLI with read-only tools. Delegates the review to pi (dev/pi) with a structured review rubric, running in read-only mode to prevent accidental edits. By default reviews uncommitted working tree changes (git diff HEAD) with pi restricted to the read tool; explicit targets (--branch/--diff/@file/PR) or --explore widen pi to read,grep,find,ls. Use when the user asks to "review code with pi", "pi review", "have pi review", "let pi review", or invokes /pi:review.

Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Implement the Entrepreneurial Operating System (EOS) to align vision and execution across a company. Use when the user mentions "EOS", "V/TO", "quarterly rocks", "Level 10 meetings", "accountability chart", "IDS process", "Entrepreneurial Operating System", or "business operating system". Also trigger when a growing company needs meeting structure, goal-setting frameworks, or a systematic approach to solving recurring organizational issues. Covers the six EOS components: Vision, People, Data, Issues, Process, Traction. For team motivation design, see drive-motivation. For lean experimentation, see lean-startup.

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

Structured troubleshooting for Sage/Acorn/Lando projects — diagnoses PHP errors, Blade rendering failures, Livewire mount errors, Eloquent query issues, Acorn boot failures, queue job failures, WP hook conflicts; uses lando logs, Query Monitor, Xdebug, WP_DEBUG. Invoke for: "/debugging", "something is broken and I can't figure out why", "lando logs show an error", "acorn boot error", "blade is rendering wrong", "livewire component not mounting", "500 error", "debug this issue". Skip when: you are building or implementing features — that is /building, not debugging.

Amazon Ads deep analysis covering Sponsored Products, Sponsored Brands (incl. Sponsored Brands Video), Sponsored Display (audiences + contextual), and basic Amazon DSP. Evaluates campaign structure, ACOS/TACOS targets, search-term harvesting, negative keyword discipline, Brand Analytics signals, day-parting, bid management, auto vs manual campaign mix, ASIN targeting, and DSP retargeting. Use when user says Amazon Ads, Amazon advertising, Amazon PPC, Amazon search ads, Sponsored Products, Sponsored Brands, Sponsored Display, Amazon DSP, ACOS, TACOS, retail media audit, Amazon Marketing Services, AMS, or Amazon seller advertising.

Triage a raw, vague issue or bug report into a structured document that names what is known, what is missing, and what to do next. Use when an incoming issue, bug report, or problem description is too vague or incomplete for investigation or planning, and recommend the right next han skill. Does not investigate root causes or trace code paths — use investigate for debugging, diagnosis, and root cause analysis. Does not plan features or build solutions — use plan-a-feature or plan-implementation for that.

Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and citation capsules. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

Run RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to trajectories or studying differentiation dynamics from spliced/unspliced layers (velocyto/STARsolo output); for the general QC, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for .h5ad data-structure I/O and layer wrangling prefer alterlab-anndata instead. Part of the AlterLab Academic Skills suite.

Capture structured feedback on the Han skills and agents used in the current session and optionally post it as a GitHub issue to testdouble/han. Use at the end of any session where one or more han-* skills or agents ran, to rate a run, log what worked and what didn't, or submit observations for maintainers. Does not review code, investigate bugs, or research options; use code-review, investigate, or research for those. Does not provide feedback on skills or agents from non-Han plugins.

Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data through clustering, cell-type annotation, DE, or pseudotime workflows; for building or reading the .h5ad data structure itself (layers, obs/var, concatenation, backed mode) prefer alterlab-anndata instead, and for RNA velocity from spliced/unspliced counts prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.