npm-packages

Creates polished self-contained HTML explanation reports for complex, multi-step topics using overview tables, evidence cards, adaptive graphs or diagrams, meaningful SVG/media, and accessible tabs for long pages. Use when the user requests a browser-readable diagnostic report, technical guide, decision analysis, implementation plan, investigation, or research synthesis as HTML.

Agents should invoke this skill to create, migrate, inspect, apply, verify, or roll back versioned PATCH.md v2 lifecycle packages with machine-readable manifests, deterministic plans, drift detection, and transactional safety.

Safely inspect, search, render, edit, diff, validate, and commit Microsoft Word DOCX files with the dedicated docx_* tools. Use for DOCX/DOTX/DOCM/DOTM document work; enforces dry-run-first edits, active-content refusal, preservation checks, and save-as by default.

Agents should invoke this skill for high-stakes or complex research needing multi-source evidence, scientific/technical fact-checking, decision traces, or rigorous verification. Runs deterministic two-phase research with schema/policy validation.

Use when inspecting, reading, rendering, editing, formatting, diffing, or validating local .xlsx and .xlsm Excel workbooks. Provides fail-closed workbook tools with dry-run planning, SHA-256 conflict protection, PNG previews, and byte-identity checks for VBA and other protected OOXML parts. Do not use for CSV-only work or for executing/editing VBA.

Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.

Use when drafting new reusable Pi/Agent Skills from repeated successful workflows, troubleshooting trajectories, notes, or PATCH.md files. Enforces reusability checks, writes disabled drafts, adds contract tests when possible, and asks before enablement.

Agents should invoke this skill for Tauri + Django + React desktop apps, especially backend lifecycle, CORS/auth, frontend integration, mandatory light/dark theming, German/English i18n, build packaging, dual desktop/web deployment, Rust commands, and platform-specific gotchas.

Audit and manage the local Pi skill bank. Use when inventorying enabled or installed skills, checking stale or duplicate skill scopes, producing read-only prune/merge/update plans, or validating skill lifecycle hygiene.

Evaluate Pi and Agent Skills packages for frontmatter validity, routing description quality, required workflow/safety/verification sections, referenced scripts, destructive-command safeguards, optional routing fixtures, and runnable tests. Use when reviewing, enabling, publishing, or quality-gating skills.

Use when a Pi skill receives a user correction, fails a test, produces unsafe/low-quality output, or should be improved from runtime feedback. Creates structured per-skill memory plus PATCH.md-style refinement proposals via skill_refinement_plan without mutating production skill behavior.

Agents should invoke this skill before modifying unfamiliar codebases, answering where/how something is implemented, tracing dependencies, mapping repo structure, or planning changes. Explores a repository and returns a strict JSON handoff with key files, symbols, risks, and evidence.

Use when creating or restyling German HTML/CSS/JS presentations for chemical production quality-control laboratory audiences: laboratory technicians, scientists, and/or quality-control teamleaders. Reproduces a modern professional green laboratory theme while regenerating content only from the current source material.

Use automatically when evaluating or applying Dolt, the Git-like version-controlled SQL database, for database branching, merging, diffs, audit history, rollback, data collaboration, DoltHub, or versioned MySQL replica workflows.

Use when evaluating, implementing, reviewing, or debugging Shoo auth (shoo.dev) Google sign-in in browser apps, including @shoojs/react, @shoojs/auth, hosted shoo.js, Convex custom JWT integration, PKCE callbacks, session checks, and server-side id_token verification. Do not use for unrelated auth systems.

Use for repeated-looking troubleshooting, solved-issue recall, adding durable troubleshooting notes, and maintaining the local LEARNINGS archive. Reads LEARNINGS-SUMMARY.md first, follows referenced archive/source files before applying lessons, and verifies against current system evidence.

Use when creating, updating, reviewing, or publishing Arch User Repository (AUR) packages. Runs /release-aur plan first, reviews PKGBUILD/.SRCINFO/build/namcap evidence, and only recommends /release-aur publish after a GO decision.

Always use for Arch Linux and Arch-based distro troubleshooting, especially EndeavourOS and CachyOS, including pacman, systemd/init, NetworkManager/DNS/Wi-Fi, PipeWire/audio, boot/initramfs/mkinitcpio, GPU/Wayland, Bluetooth, filesystems, or package/signature issues. Use local ArchWiki evidence before web sources; the user should not need to explicitly ask for local ArchWiki.

Use automatically for Hyprland troubleshooting, configuration, Wayland compositor issues, monitors, input, keybinds, rules, animations, decoration, portals, plugins, crashes, NVIDIA, or hyprctl/hyprpm questions. Prefer local official Hyprland Wiki evidence via hyprwiki tools before web sources.

Use automatically for NixOS, Nix, nixpkgs, flakes, Nix language, Home Manager-adjacent Nix configuration, nix commands, NixOS modules/options, packages, overlays, derivations, and troubleshooting. Prefer local official NixOS/Nix documentation evidence via nixoswiki tools before web sources.

Use automatically for Raspberry Pi computers, Raspberry Pi OS, Raspberry Pi Imager, raspi-config, config.txt/cmdline.txt, GPIO, HATs/accessories, camera/rpicam/libcamera/Picamera2, SSH/VNC/Connect, bootloader/EEPROM/NVMe/USB boot, Compute Module, Pico/RP2040/RP2350/Pico SDK/MicroPython, and Raspberry Pi documentation questions. Prefer local Raspberry Pi Documentation evidence via raspberrypi_wiki tools before web sources.

Use when creating, updating, validating, evaluating, or maintaining Pi local wiki/documentation extension packages from templates. Provides create_wiki, update_wiki, list_wiki_templates, and validate_wiki workflows, plus quality gates for accuracy, effectiveness, and token output.

Agents should invoke this skill for broad multi-claim research projects needing planning, parallel investigation, source merging, gap closure, citation audit, and final synthesis when narrower research skills are insufficient.

Agents should invoke this skill as the final gate before release, handoff, or claiming completion for substantial changes. Runs acceptance/readiness checks, determines pass/fail, and gives a go/no-go recommendation.

Agents should invoke this skill for architecture reviews, module boundaries, dependency direction, coupling/cohesion, SOLID concerns, system design trade-offs, layering, service boundaries, or design decisions before implementation.

Agents should invoke this skill for backup health checks, restore testing, NAS/Gitea backup integrity, 3-2-1 strategy review, backup script audits, or verifying repositories and archives can be restored safely.

Agents should invoke this skill when defects, regressions, failed tests, unexpected behavior, or spec mismatches are found. Produces structured reproducible bug reports with severity, evidence, environment, and actionable next steps.

Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.

Agents should invoke this skill for code security reviews, leaked secret checks, dependency risk, unsafe shell/Python/TypeScript/Rust patterns, auth/input-validation flaws, SAST-style audits, or supply-chain concerns in repositories.

Agents should invoke this skill when comparing competing products, services, libraries, tools, vendors, or approaches for market/product positioning, feature matrices, strategic trade-offs, pricing, adoption, or differentiation.

Agents should invoke this skill for Docker Compose deployments, container updates, stack health checks, rollbacks, compose-file changes, image upgrades, failed deploys, or service restart planning. Provides safe deployment and rollback workflows.

Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.

Agents should invoke this skill for connectivity, DNS, Pi-hole, port reachability, routing, firewall reachability, TLS/network timeouts, or service access failures. Provides structured network troubleshooting commands and interpretation.

Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.

Agents should invoke this skill for slow code, high CPU/memory, latency, large data processing, algorithmic complexity, profiling plans, benchmarks, or optimization requests. Profiles first and weighs trade-offs before changing code.

Agents should invoke this skill for refactors, code smells, migrations, duplication removal, module splitting, API cleanup, or restructuring plans. Emphasizes small safe steps, behavior preservation, and verification after each change.

Agents should invoke this skill for Linux server security reviews, SSH hardening, firewall/open-port audits, user/permission checks, exposed services, or host hardening requests. Produces severity-rated findings and practical remediation steps.

Agents should invoke this skill when a spec, plan, README, issue, or requirement must be verified against implementation. Traces requirements to code, checks interface contracts, and reports gaps or mismatches.

Agents should invoke this skill when identifying, categorizing, prioritizing, or planning technical debt work, debt sprints, cleanup backlogs, TODO consolidation, or long-term maintainability risks. Tracks debt with severity/effort.

Agents should invoke this skill when choosing or evaluating libraries, frameworks, tools, platforms, models, databases, APIs, or architectures for a use case. Produces criteria scoring, ecosystem assessment, and recommendations.