「outputs」の検索結果

Chanjing AI creation Open API client: submit image/video tasks, poll task status, list and get tasks, and optionally download outputs when the user runs download_result.py. Reads and writes local credentials.json; calls open-api.chanjing.cc.

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pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.

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Use when a task needs the judgment of a biomedical/bioengineering engineer — running a design control phase (inputs, outputs, verification, validation) for a medical device, scoring a Design FMEA and deciding whether a failure mode needs mitigation, planning a human factors/usability validation study, building a design traceability matrix, or writing a design verification or risk-mitigation report. Distinct from a regulatory-affairs-specialist (owns the submission/clearance process, not the engineering itself) and from a clinical-data-manager/biostatistician (trial data and analysis, not device design).

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Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.

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Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.

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Audit a roadmap or backlog to distinguish outcomes from outputs. Use when the user says "outcome vs output", "escaping the build trap", "are we building the right things", "roadmap review", "feature factory", "output-driven team", "we keep shipping but nothing changes", or wants to shift focus from shipping features to delivering value - even if they don't explicitly say "outcome vs output".

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Build interactive "living workflow" flowcharts in the visual style of https://statuesque-starship-49757d.netlify.app/revenue-engine - vanilla HTML/CSS/JS files with a horizontal step bar, a vertical trunk that grows per step, tool-to-tool columns with animated payload pills between brand-marked tool nodes, alternating left/right step descriptions, and per-step outputs. Use whenever the user says "build a flowchart", "interactive flowchart", "living workflow", "revenue engine flowchart", "demand gen flowchart", "GTM workflow diagram", "process diagram with animations", "Nebor-style flowchart", or asks to recreate the workflow visual from the Nebor site. Always asks which flowchart to build (Revenue Engine / Demand Gen / CRM & RevOps / Custom) and waits for explicit approval of the step + tool + edge table before generating any files.

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Generate diagrams of any kind from a description, ticket, spec, or file. Outputs Mermaid syntax (renders natively in GitHub, Notion, Confluence, Obsidian, Slack with apps) plus an optional single-file HTML preview that renders the diagram in the browser. Use this skill when the user says: - "draw a flowchart", "create a diagram", "visualize this flow" - "sequence diagram", "state diagram", "ER diagram", "class diagram" - "user journey", "mindmap", "timeline", "gantt", "org chart" - "sankey", "quadrant chart", "git graph" - or pastes a ticket / spec / description and asks for a visual Picks the right Mermaid diagram type for the input, asks if ambiguous, then emits clean syntax + an optional preview file.

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Set a repo up to prove engineering-task work actually works before it ships. Investigates the repo, ensures a one-command dev stack (`dev-local`) exists, asks whether verification runs locally or in a sandbox (crabbox), confirms/installs the driver (the `playwright-cli` skill for web by default). Outputs three artifacts: a committed `/verify` skill (per-task verification SOP — spawn a verifier sub-agent → drive the app → screenshot/video proof → open a PR with the proof embedded), the `/dev-local` skill + script, and the installed driver skill. Use when someone says "set up verification", "make this repo verifiable", "scaffold a verify skill", "set up the verifier".

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**MEASUREMENT SKILL** — Benchmark framework for measuring skill effectiveness. A/B tests agent outputs with and without a target skill, scoring correctness, completeness, and pattern adherence. USE FOR: validating skill quality, comparing before/after skill changes, proving skill ROI with data. DO NOT USE FOR: general testing, unit tests, CI pipelines.

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Generate audio narration of blog posts using Google Gemini TTS. Supports summary narration, full article read-aloud, and two-speaker podcast/dialogue mode with 30 voice options. Outputs MP3 with HTML5 audio embed code. Works standalone via /blog audio or internally from blog-write. Falls back gracefully when API key is not configured. Use when user says "blog audio", "narrate blog", "audio version", "text to speech", "tts", "podcast mode", "read aloud", "audio narration", "voice", "narration", "generate audio".

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Detect keyword cannibalization across blog posts by extracting primary keywords from titles and headings, clustering semantically similar targets, and flagging posts competing for the same search intent. Supports local-only mode (grep-based) and DataForSEO API mode (Page Intersection endpoint at ~$0.01/call). Outputs severity-scored report with merge or differentiate recommendations. Use when user says "cannibalization", "keyword overlap", "competing pages", "duplicate keywords", "cannibalize".

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Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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Phase 7 of the SRS workflow. Runs srs_validator.py --dir on the generated SRS files, reports errors and warnings, auto-fixes ERRORs, and outputs a COMPLIANT / PARTIALLY COMPLIANT / NON-COMPLIANT verdict.

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Phase 7 of the SRS workflow. Runs srs_validator.py --dir on the generated SRS files, reports errors and warnings, auto-fixes ERRORs, and outputs a COMPLIANT / PARTIALLY COMPLIANT / NON-COMPLIANT verdict.

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Use when overriding the default UI of an Agentforce agent action's Apex input or output with your own Lightning web components via a LightningTypeBundle (schema.json + editor.json/renderer.json, lightning__AgentforceInput / lightning__AgentforceOutput targets, the @apexClassType binding). NOT for standard agent actions with primitive inputs/outputs (only Apex-class input/output can be overridden), NOT for Flow screen custom property editors (use flow/flow-custom-property-editors), and NOT for general LWC-in-Lightning-page work (use lwc/* skills).

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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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Firmware development for the LGT8F328P-LQFP32 MiniEVB — the Nano-style 30-pin "purple nano" board whose silkscreen reads LGTBF32BP (also sold as BTE21-15A, WAVGAT, LGT8F328P Nano V3.0), built on the Logic Green / Prodesign LGT8F328P, an AVR-instruction-compatible LGT8XM part with a 32 MHz internal RC and no crystal, a 12-bit ADC, an 8-bit DAC on D4, four timers, 80 mA drive pins, SWD instead of ICSP, and EEPROM emulated in program flash. Use when working on this board, any LGT8F328P/LGT8F328D/LGT8FX8P board, or a sketch being ported from an Arduino Nano or ATmega328P: PlatformIO setup with the lgt8fx core, platformio.ini, f_cpu/f_osc/clock_source and why timings come out 2x wrong, pin mapping and the LQFP32 bonded pads, analogRead resolution and reference errors, analogWrite hitting the DAC, EEPROM vanishing after upload, 80 mA outputs, upload failures over CH340/CH9340/HT42B534, SWD recovery, or debugging why something on the board does not work.

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Run a self-retrospective on a SKMTC-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive SKMTC work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or SKMTC itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.

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Extracts timed subtitles from local video/audio files using Whisper. Outputs SRT format with accurate timestamps. Handles complex filenames automatically. Use when user asks to "extract subtitles", "get subtitles", "transcribe video", "识别字幕", "提取字幕", or "获取字幕".

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Design and build cohesive web pages with custom visual identity — landing pages and portfolios get rich anatomy + section archetypes; any other page type (blog, about, pricing, contact, coming-soon, dashboard, admin, e-commerce, custom) is supported through generic anatomy + skeleton. Use this skill whenever the user mentions: landing page, portfolio, personal site, hero section, marketing page, product page, sales page, splash page, blog page, about page, pricing page, contact page, coming-soon, waitlist, dashboard, admin panel, e-commerce store, redesign my site, design my portfolio, build a portfolio, work showcase, hire-me page, perfect-ui, perfect landing, or asks to create any web page surface. Two tiers: special (landing | portfolio — rich treatment) and generic (everything else — universal craft toolkit). Self-contained 8-phase pipeline: vibe discovery → visual direction (color/typography/mood) → custom icon set (NO emoji, NO icon library) → AI-generated visuals → optional shader effects layer → inline plan + implement on Next.js + Tailwind + shadcn. Always proposes effect layer as optional. No refusals; outputs distinctive sites, not AI slop.

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Show where this machine's Claude Code tokens went - cost by token type, context tax per tool, biggest tool outputs.

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Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.

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Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

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。