518 件 ・ 関連度順
概要と使いどころ
Find opportunities to add metrics and estimate numbers when exact data is unavailable. Use when the user wants to quantify achievements, add numbers, percentages, or impact metrics to a resume.
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。
Use when putting data into cognee memory with remember() — choosing inputs (text, files, folders, URLs, repos, databases), datasets and node_sets, loaders, ontologies, the graph extractor (LLM or GLiNER), chunking, dry-run cost estimates, or when remember() raises on a keyword argument.
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。
Plan a sprint — scope work, estimate capacity, set goals, and draft a sprint plan. Use when kicking off a new sprint, sizing a backlog against team availability (accounting for PTO and meetings), deciding what's P0 vs. stretch, or handling carryover from the last sprint.
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。
Use when designing a prospective clinical study before submission — selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging), estimating sample size and power for two-arm designs (means / proportions / survival), or scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named human owner (clinician / biostatistician / regulatory owner) — never clinical fact, never a finished protocol. Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study 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。
Use when an RFP, RFI, RFQ, security questionnaire, vendor questionnaire, or proposal request arrives and the team needs a structured response — parsing multi-section buyer-dictated requirements (MANDATORY vs WEIGHTED vs NICE-TO-HAVE), building a Shipley-method proof-point matrix mapping each requirement to a verifiable proof point, articulating 3-5 win-themes that ladder up across requirements, and producing a Shipley-derived winrate estimate that informs a bid / no-bid / partner-bid recommendation. For Bid Managers, Proposal Leads, Directors of Sales, and Sales Engineers at the response-strategy moment. Surfaces GAP requirements explicitly — never invents claims. NOT free-form proposal narrative authoring, NOT contract redline, NOT marketing collateral.
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。
Estimate market size using TAM, SAM, and SOM with top-down and bottom-up approaches. Use when sizing a market opportunity, estimating addressable market, preparing for investor pitches, or evaluating market entry.
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。
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
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。
Triages a repository's open GitHub issues and pull requests via the gh CLI. Optionally reviews and merges ready PRs — incrementally merging passing automated/bot PRs and maintainer-approved ones, and spawning review subagents for never-reviewed ones — then closes already-resolved issues with comments citing the resolving PR or commit, cross-links issues with their pending fix PRs, and assigns local-only priority and change-size estimates for everything outstanding. Use when triaging, grooming, or reviewing a repository's open issues and PRs.
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。
Burn timed subtitles onto a finished video or configure Whisper-timed caption burning during faceless-video assembly. Takes video/audio generation jobs or a local finished video, optionally with authored narration text, and returns a captioned video or the exact backend subtitle configuration. Timings always come from Whisper on the video's own audio — never estimated. THREE looks: `paper` (torn cream paper label, handwritten ink), `bold` (UGC ALL-CAPS, white with black stroke, platform safe zones), `clean` (slim white CAPS, no plate, tiny at the bottom — no Pillow needed). Use when: adding/burning subtitles or captions to an existing video, restyling captions, or when a production workflow needs its finished cut subtitled. NOT for: generating the video, translating speech, or live/soft subtitle tracks (this burns them into the picture).
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。
Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.
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。
Break down epics into user stories with Humanizing Work split patterns. Use when a backlog item is too large to estimate, sequence, or deliver safely.
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 a realistic Linear cycle plan from current priorities, dependencies, estimates, carryover, and team capacity without silently rescheduling work.
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。
Searches Meta Ad Library (Facebook/Instagram/WhatsApp ads) by keyword or Facebook page ID and extracts ad details including creatives, copy, CTA, publisher platforms, spend, impressions, reach estimates, and page transparency info. Use when user mentions Meta Ad Library, Facebook ads scraper, Instagram ads data, FB ad library, search Facebook ads, get ads from a Facebook page, scrape Meta ads, Facebook advertising data, ad creative extraction, competitor ads analysis, brand ads monitoring, Meta advertising transparency, political ads Facebook, housing ads Facebook, view all ads from a page, facebook ads search, fb ads library api, facebook ad archive, instagram ad data, get ad creatives.
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 is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.
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。
Software project effort estimation assistant. Outputs three-point estimates (optimistic/most-likely/pessimistic values with confidence intervals), T-shirt sizes, or Function Point Analysis (FPA) counts. Triggered when users ask 'how long will this feature take,' need to assess project workload, perform PERT estimation, T-shirt sizing, FPA, sprint planning, or quote-based effort breakdowns.
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。
End-to-end R data analysis for the sewage project. Writes analysis scripts following project conventions (here::here, arrow/parquet, fixest, modelsummary, native pipe), runs code review, and produces publication-ready tables and figures. This skill should be used when asked to "run an analysis", "estimate the model", "add a specification", or "write an R script".
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。
Writes expert academic paper summaries for social science research, particularly political science and applied statistics. Use when asked to summarize, review, or create a reading summary of an academic paper, PDF, or research article. Accepts a file (PDF or text format) or a directory of papers. Produces a structured markdown summary — approximately 400–600 words — covering primary contributions, major questions and answers with point estimates, methods and data, and limitations and robustness. Includes BibTeX citation retrieved from Google Scholar and keyword metadata.
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 structural estimation pipeline — routes to /workflows:work with estimation context from empirical-playbook
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。
Thoroughly verify all code, tables, figures, modeling decisions, and quantitative claims in an academic paper against its source R scripts and output files. Use this skill whenever you need to audit, replicate, or verify an academic research paper - including cross-checking LaTeX tables against R output, validating econometric modeling choices, ensuring sample sizes are consistent, building a verification manifest, and running automated replication tests. Trigger this skill for any mention of: paper verification, replication check, table audit, code-paper consistency, reproducing results, verifying estimates, checking coefficients, or any variant of "does the paper match the 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。
Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.
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。
Analyzes your search term reports across all campaigns and surfaces high-intent terms you're not bidding on yet. Groups them by theme, estimates their potential volume and CPA, and recommends match types and starting bids for each. Platform: Google.
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。
Analyzes performance by day of week and hour of day across your campaigns. Identifies when your ads perform best and worst, recommends ad schedule adjustments with estimated savings, and tells you exactly what you'd give up by restricting hours. Platform: Google and Meta.
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。
The art-route layer of the Forge skills. route_media.py is the one command that generates an image or an image-to-video clip - through the paid API of any provider whose key is configured (OpenAI, Google Gemini, xAI, BytePlus ModelArk, fal.ai; the configured key is the owner's consent), else through the user's own signed-in Codex or Grok CLI - and prints one JSON line with the route, artifact, sha256 and cost estimate; with no route it answers no-route (codeart2d is then the last resort). Also the capability check (forge_doctor), API key configuration, the spend ledger, resume and Codex image adoption. Use when a Forge skill sends a generation here, for the capability check, or when the user asks about keys, routes or spend. Not for planning, processing or QA of sprites, video frames or maps (generate2dsprite, video2dsprite, generate2dmap) or for code-drawn art (codeart2d).
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。
Use this skill whenever the user asks you to load, clean, aggregate, join, or otherwise analyze tabular data (csv, xlsx, json, or a database export) — 'what's the total revenue by region', 'clean up this dataset', 'join these two files on customer id'. Establishes a verification discipline so reported numbers are always numbers the code actually printed, never numbers you inferred or estimated.
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。