Claude Skills by fabioc-aloha

Comprehensive brain file review — external freshness, internal consistency, semantic accuracy — stamp only after full assessment

Operate the Plugin Mall's canonical vendor, contribution, scan, score, render, and validation workflows. Use when importing or refreshing curated plugins, reviewing contributor PRs, running first-party maintenance, debugging the weekly catalog refresh, or onboarding to Mall internals.

Consolidate session learning into permanent architecture — extract patterns into skills, instructions, prompts, or memory

Maintain the source registry in sources/supported-stores.json — add a new third-party plugin store, retire one, refresh metadata, validate the schema. Use when proposing a registry change, after the weekly cron flags a source as unhealthy, or when a candidate store needs evaluation before adding.

Detect, classify, and prune stale source stores in the Mall — define what stale means and how to remove gracefully without breaking downstream consumers.

Evaluate a proposed store for inclusion in Alex_ACT_Plugin_Mall using a quality scorecard

Use when adding interactive 3D scenes from Spline.design to web projects, including React embedding and runtime control API.

Three.js animation - keyframe animation, skeletal animation, morph targets, animation mixing. Use when animating objects, playing GLTF animations, creating procedural motion, or blending animations.

Context-dependent editorial style rules for academic writing — APA7, Chicago, MLA judgment calls

End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measur...

Research project scaffolding, thesis/dissertation writing, literature reviews, publication workflows, and the AI assistant-assisted academic workflows

APA 7th formatting, citation integration, reference validation, and bibliography generation

Claude-native deep research using DAG-based query planning, parallel subagent execution, and gap-driven iteration. No external API needed.

Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.

Deterministic search across arXiv, PubMed/PMC, and US policy corpora with daily freshness cutoffs.

This skill should be used when the user asks to convert an academic paper in LaTeX from one format (e.g., Springer, IPOL) to another format (e.g., MDPI, IEEE, Nature). It automates extraction, injection, fixing formatting, and compiling.

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

Systematic literature search, synthesis, gap identification, and narrative construction for academic research

Search 10 academic paper databases via REST APIs for research papers, preprints, and scholarly articles. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID lookups, abstracts, full text, open access, preprints, citation graphs, author search, or any scholarly literature query. Triggers on mentions of any supported database or requests like \"find papers on X\" or \"look up this DOI\".

Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.

Build knowledge bases that build software — research before code, teach before execute

Structured research summarization agent skill for non-dev users. Handles academic papers, web articles, reports, and documentation. Extracts key findings, generates comparative analyses, and produces properly formatted citations. Use when: user wants to summarize a research paper, compare multiple sources, extract citations from documents, or create structured research briefs. Plugin for Claude Code, Codex, Gemini CLI, and OpenClaw.

Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

Manuscripts evolve terminology that may not match what was actually administered:

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks

Patterns for adding safety, trust, and policy enforcement to AI agent systems -- control which tools agents can call, what content they process, and maintain accountability through audit trails.

This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.\n

Generate and verify integrity manifests for AI agent plugins and tools -- detect tampering, enforce version pinning, and establish supply chain provenance (the SLSA/Sigstore gap for agent ecosystems).

This skill should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development;...

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.\n

Patterns for agent self-improvement through iterative evaluation and refinement -- generate, evaluate, critique, refine loops that move beyond single-shot generation.

Design autonomous AI agents that reason, plan, and execute tasks

Plan AI model work across Microsoft Foundry, Hugging Face, and ElevenLabs using live provider evidence. Use when choosing a model or provider, comparing cross-provider options, decomposing multimodal work, estimating constraints, or preparing an executable plan before any paid service call.

Execute an approved model task plan through Microsoft Foundry, Hugging Face, or ElevenLabs and record provider evidence. Use after model-router emits a valid plan and the user wants to run it, monitor jobs, cancel work, download outputs, or apply an approved fallback.

Configure optional Microsoft Foundry, Hugging Face, and ElevenLabs provider access for AI Operations. Use after installing the plugin, when a provider is unavailable, when auditing authentication, or when previewing and provisioning the exact ElevenLabs private runtime.

This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.

VS Code Chat API patterns.

Azure Content Safety API integration, multi-layer defense pipeline, output validation, and operational safety controls

This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.

This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.

Build applications powered by GitHub Copilot using the Copilot SDK — session management, custom tools, streaming, hooks, MCP servers, BYOK, deployment patterns

Evaluate plugin quality. Use when user says \"evaluate plugin\", \"review plugin quality\", \"score my plugin\", \"check plugin\", \"rate plugin\".

Microsoft Foundry agent deployment, orchestration, and cloud-native AI service patterns

This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.

Choosing the right model for the task — power vs. cost vs. speed.

Create new Agent Skills for GitHub Copilot from prompts or by duplicating this template. Use when asked to \"create a skill\", \"make a new skill\", \"scaffold a skill\", or when building specialized AI capabilities with bundled resources. Generates SKILL.md files with proper frontmatter, directory structure, and optional scripts/references/assets folders.

Build MCP servers for LLM tool integration — Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK)

Domain: AI Infrastructure