「concrete」の検索結果

QA workflow for testing a feature or task implementation. Analyzes changes, produces test plans, and describes concrete test scenarios. Use when user says "test this", "write test plan", "what should I test", or "QA this branch".

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Etsy SEO optimization for existing and new listings. Auto-detects REWRITE (when user pastes a current title + tags + description) or CREATE (when user gives a short product context). Triggers on "rewrite this listing", "optimize my Etsy listing", "fix my title and tags", "create new listing", "make a new Etsy listing". Uses live Etsy autocomplete + competitor SERP scraping for evidence-driven keyword research. Enforces 2026 Etsy rules — tag ≤20 chars, primary keyword in first 40 chars, indexing spread across title/tags/attributes/description/hero alt, NLP-aware writing, trademark stoplist scan. When the user's problem isn't SEO (CTR, conversion, photos, price, saturated niche), it says so directly and routes to concrete next steps. Silently maintains a markdown listing database at ~/etsy-listings/ for cross-session memory on Claude/Cowork. Catches duplicate primary keywords across the user's own listings.

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Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

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Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

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。

QA workflow for testing a feature or task implementation. Analyzes changes, produces test plans, and describes concrete test scenarios. Use when user says "test this", "write test plan", "what should I test", or "QA this branch".

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。

Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.

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Use this skill when evaluating VCSDD grading dimensions during adversarial review. Provides concrete evaluation checklists for each of the 5 dimensions and convergence signal detection.

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。

Indicator pivoting methodology — how to use one known indicator to discover related infrastructure across the IOC graph. Decision tree by indicator type with concrete `/lookup-*` commands per pivot, a worked multi-hop example, pivot-quality scoring, and routing into the rigor pipeline. Use when the user asks "what else is connected to this IP / domain / hash / cert / actor?", needs to expand a single seed IOC into a campaign cluster, or wants the canonical reference for graph-walking IOCs.

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Use when the founder needs a single orchestrator to route work across multiple departments, synthesize specialist output, or translate an ambiguous business request into a concrete execution path.

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Founder Operating Context. Use to create or update the canonical founder/startup context document so every fundraising, growth, product, and recruiting workflow runs on concrete company facts instead of generic assumptions.

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Socratic code review and refactoring session — whether it's your own code, a teammate's PR, or something you inherited. Leads you to see the issues through questions, names smells and moves precisely, then closes with a concrete plan.

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On-demand situational awareness — fresh git/PR snapshot plus continuation, distilled into one concrete "next action" suggestion. Use mid-session when you've lost track, after a compaction, or starting a new work block. Trigger on "where was I", "what's next", "progress", "what should I do now", "/progress".

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Describe what this skill does and the concrete situations in which an agent should use it.

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Turn a rough feature, product interaction, or architecture idea into a concrete design. Use when meaningful decisions remain open; avoid for already-clear local edits.

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One-line-prompt → cinematic, gorgeous, functional, well-tested, deployed website. Pre-hydrated SPA + full PWA kit + JSON-LD rich snippets + third-party integrations. React 19+Vite default (Angular optional). 100 concrete improvements grouped into 10 categories that EVERY single-prompt site build must satisfy before being marked done. Trigger whenever the user says 'make a website for X', 'build a site for X', 'rebuild X.com', or any equivalent one-liner.

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Operationalizes a threat intelligence report into hunts: extracting IOCs and TTPs, mapping behaviors to ATT&CK and available telemetry, prioritizing durable behavioral indicators over volatile atomic ones, and producing concrete hunt queries. Activates for requests to hunt from a threat report, operationalize CTI, or turn an intel report into detections.

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The investigation harness for "why is this slow?" over a Pulp Perfetto trace (.pftrace). Runs the hypothesis→query→drill-down chain-of-evidence loop autonomously and returns a plain-English root cause + evidence + a concrete fix. TRIGGER on "why is my plugin slow to open", "find the slowest frames", "why is the UI stuttering", "why is my plugin using so much CPU", "which DSP node is expensive", "the load meter looks calm but CPU is pinned", or a named `pulp trace gpu-startup|gpu-health|gpu-probe` analysis. Ships Pulp-specific hints for dsp, frame, js, gpu, and cross-platform symptoms.

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Use when a task needs the judgment of a Molder, Shaper, or Caster (ceramics, concrete, and similar non-metal, non-plastic materials) — calculating mold/pattern dimensions from a material's actual characterized shrinkage rate, deciding whether a demolding decision should follow a fixed time or a material readiness signal, controlling drying/curing rate to avoid cracking on uneven-thickness parts, or verifying mold dimensional accuracy after extended use.

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Standalone E-E-A-T scoring rubric for a URL or content draft. Scores Experience, Expertise, Authoritativeness, and Trust 0-5 each using concrete detectable signals, framed per Google's creating-helpful-content doc. Use when user says "E-E-A-T", "EEAT", "experience expertise", "trust signals", or "author authority".

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Dedicated Core Web Vitals deep-dive: pull field data (CrUX / PageSpeed Insights), fall back to Lighthouse lab data, score LCP, INP, and CLS against Google's thresholds at the 75th percentile, and run per-metric diagnosis playbooks with concrete fixes. Use when user says "core web vitals", "CWV", "LCP", "INP", "CLS", "page speed metrics", or "field data".

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Design a concrete technical proposal with evidence and tradeoffs when a decision needs design. No implementation or automatic reviewer.

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Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.

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Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.

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FALLBACK ONLY — do not invoke unless you have already ruled out (1) a native API (Gmail API, GitHub API, Slack API …), (2) a CLI (git, gh, aws, npm, curl …), (3) direct file editing, and (4) existing browser automation (Playwright, Puppeteer). Only when all four are unavailable or have already failed should you use this skill. It gives AI agents a cursor and a keyboard on a real desktop — the last mile when the only remaining surface is a GUI. Concretely: use it when an earlier attempt via API, CLI, or direct file edit has failed and the user says things like "open X", "click Send", "type this in Word", "read what is on my screen", "do this in Outlook", "drive the Figma UI", "control my desktop", "automate this workflow", "fill out this form", or "copy text between apps". Works on Windows, macOS, and Linux with any LLM that can call functions (Claude, GPT, Gemini, Llama, Kimi, Ollama) over MCP — stdio for editor hosts (Claude Code, Cursor, Windsurf, Zed) or HTTP for daemons and dashboards.

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。