Clean/hexagonal architecture: dependency rules, ports and adapters, layering, and testable boundaries. Use when structuring applications for maintainability and testability.
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概要と使いどころ
Clean/hexagonal architecture: dependency rules, ports and adapters, layering, and testable boundaries. Use when structuring applications for maintainability and testability.
日本語の概要は準備中です。原文の説明を表示しています。
Maintain changelogs users actually read: Keep-a-Changelog format, automation, and release-note craft. Use when writing changelogs or automating release notes.
日本語の概要は準備中です。原文の説明を表示しています。
Configure Babel: presets, plugins, env-specific builds, polyfills, and performance. Use when maintaining Babel-based toolchains or writing custom transforms.
日本語の概要は準備中です。原文の説明を表示しています。
Professional C: memory ownership, defensive programming, build hygiene, and systems-level correctness. Use when writing, reviewing, or maintaining C code.
日本語の概要は準備中です。原文の説明を表示しています。
Bash scripting guidance — safe scripting patterns, quoting, error handling, pipes, and maintainable shell code.
日本語の概要は準備中です。原文の説明を表示しています。
Build workflow automations — triggers, actions, conditional logic, error handling, and maintainable automation design.
日本語の概要は準備中です。原文の説明を表示しています。
Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.
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Keep paperclip-docs in lockstep with the parent paperclipai/paperclip codebase. Detects code-surface changes (CLI, env vars, API routes, adapters, plugin SDK, schemas) and produces friendly-tutorial-voice doc updates. Two modes — `nightly` (tracks parent's default branch (currently `master`), targets the `nightly` branch, preview deploy) and `release` (on parent tag, merges nightly → main, tags, ships live). Trigger phrases: "sync docs", "update docs from paperclip", "/sync-docs", "check for paperclip changes", "is docs current".
日本語の概要は準備中です。原文の説明を表示しています。
This skill provides guidance for maintaining documentation consistency with implementation in the vscode-sidebar-terminal project. Use this skill when creating or updating documentation, verifying documentation accuracy, preparing releases, or when the codebase has changed and documentation may need updates. This skill should be used proactively after implementing features or bug fixes.
日本語の概要は準備中です。原文の説明を表示しています。
Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Focuses on recently modified code unless instructed otherwise. This skill should be used when code-quality checks pass but the code would benefit from structural cleanup — deduplication, branching simplification, naming improvements, or dead-code removal. Invoked as a subagent from /code-quality or directly via the Task tool.
日本語の概要は準備中です。原文の説明を表示しています。
Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. This skill should be used after implementing features or bug fixes to clean up code, when refactoring for readability, or when reducing unnecessary complexity. It launches the code-simplifier subagent provided by the code-simplifier@claude-plugins-official plugin.
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Pull latest origin/main into the current local branch and resolve merge conflicts (aka update-branch). Use when Codex needs to sync a feature branch with origin, perform a merge-based update (not rebase), and guide conflict resolution best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Perform structured code reviews focusing on correctness, readability, security, and maintainability. Use this skill when reviewing pull requests, evaluating code changes, or establishing review standards for a team.
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Extracts indicators of compromise from raw analysis artifacts: parsing strings dumps, sandbox reports, PCAP summaries, and logs for URLs, domains, IPs, hashes, mutexes, and file paths, then deduplicating and typing them. Activates for requests to extract IOCs from analysis output, pull indicators from a report, or harvest atomic indicators.
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Prepares indicators of compromise for safe sharing: defanging URLs, domains, IPs, and emails so they cannot be accidentally clicked or resolved, and formatting them into STIX/CSV/MISP-friendly output. Activates for requests to defang IOCs, sanitize indicators for a report, or export indicators for sharing.
日本語の概要は準備中です。原文の説明を表示しています。
Run a structured discovery session to build an Allium specification through conversation. Use when the user wants to create a new spec from scratch, elicit or gather requirements, capture domain behaviour, specify a feature or system, define what a system should do, or is describing functionality and needs help shaping it into a specification.
日本語の概要は準備中です。原文の説明を表示しています。
Implement event-driven side effects that react to domain events
日本語の概要は準備中です。原文の説明を表示しています。
Create a complete Grain domain service with schemas, read models, commands, queries, and tests
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Generate and maintain browser demos of Pulp audio plugins (both web ABIs — WAM and WCLAP) from one declarative config, so every demo mounts the SAME shared player and the two ABIs stay in lockstep. Invoke it whenever you publish/update a browser demo of a Pulp plugin, add a plugin to a demo gallery, stand up a new gallery, or touch demo pages/hosting. It runs a deterministic generator (config → stamped WAM+WCLAP site + owned-files manifest) and a blocking validator (config schema, cross-origin-isolation coverage, OG metadata, regeneration ownership). Customization is declarative config in <repo>/.pulp-web-demo/config.json (schema bundled) plus at most two typed module seams; the shared player (pinned @danielraffel/web-player) holds all UX behavior and is never vendored. Works in Claude and Codex.
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Coordinate a cross-session or cross-machine handoff — snapshot the open work, write a status doc to the pulp-planning repo on main, and emit a ready-to-paste goal prompt that links it, so a fresh session (often on another machine) can pick up and finish. Use when the user says "coordinate a handoff", "hand this off", "move this work to a new machine", "we're retiring this session", or asks for a status doc plus a goal for someone else to continue.
日本語の概要は準備中です。原文の説明を表示しています。
Maintain Pulp's installed design-time agent capability manifest and public-surface ledger. Use when adding, removing, renaming, or materially changing public audio, MIDI, signal, timebase, or sequence APIs; registering a new algorithm for generators; changing capability support or deprecation state; or repairing agent-capabilities freshness, schema, fingerprint, tombstone, or installed-SDK tests.
日本語の概要は準備中です。原文の説明を表示しています。
Maintainer side of an outside contribution — find what has arrived (fork PR, issue, or patches sent out-of-band), adopt it into an in-repo branch with authorship intact, review it against the repo's bar, and ship it through the normal merge path. Use when landing work from someone without write access or without the CI fleet.
日本語の概要は準備中です。原文の説明を表示しています。
Maintain Pulp's macOS Intel (x86_64) portability lint and CI tiering — the PULP_INTEL_CANARY configure gate, intel_canary_lint.py + its allowlist, and the Tier 0-3 workflows (build.yml canary step, intel-portability.yml, nightly-intel.yml, release-cli.yml universal gate). Use when touching cross-arch build wiring, arch-gated SIMD, or Intel CI.
日本語の概要は準備中です。原文の説明を表示しています。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
日本語の概要は準備中です。原文の説明を表示しています。