「anp」の検索結果

Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.

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。

Triage a competitive or market question into the right intelligence disciplines, cadence, and executing skill. Use when you know something needs researching but not which channel to run.

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 full discovery cycle from problem hypothesis to validated solution. Use when a team needs a structured path through framing, interviews, synthesis, and experiments.

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 the support FAQ, sales talking points, and objection handling teams need before an EOL announcement. Use when customer-facing teams must be ready before customers hear.

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。

Analyze political, economic, social, technological, environmental, and legal forces. Use when external market shifts could materially affect a product, roadmap, or strategy.

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。

Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.

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。

Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.

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。

Research a competitive landscape with cited snapshots, a comparison matrix, and so-what implications. Use when a product decision needs competitive grounding, not a market report.

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。

Orchestrate a complete competitive analysis across six steps, from landscape to strategic direction. Use when you need the full picture, not a single scan or card.

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。

Design a new PM skill through guided conversation. Use when you have raw content or an idea and want to shape it into a compliant skill.

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 phase-gated EOL checklist sized to the sunset, with a named owner on every item. Use when the decision to retire is made and you need the operational plan.

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 an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.

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 who to talk to about a sunset, in what order, and what each conversation must cover. Use when an EOL decision is made and you want the landmines found before the announcement.

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。

Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.

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 customer journey map across stages, touchpoints, actions, emotions, and metrics. Use when diagnosing a broken experience or aligning a team on the full customer flow.

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 structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.

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。

Evaluate pricing changes using ARPU, conversion, churn risk, NRR, and payback. Use when deciding whether a pricing move should ship.

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。

Identify what to measure, test, or track to de-risk a product or AI idea. Use when stress-testing an idea across internal (DUFV) and external (PESTEL) dimensions.

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 customer discovery interviews with the right goal, segment, constraints, and method. Use when preparing interviews for problem validation, churn research, or new product ideas.

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。

Write a right-sized EOL announcement — brief notice through full phased comms — with rationale, customer impact, and next steps. Use when retiring a product, feature, or plan.

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。

Uncover customer jobs, pains, and gains in a structured JTBD format. Use when clarifying unmet needs, repositioning a product, or improving discovery and messaging.

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 covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".

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。

Single-cell RNA-seq and retained scverse workflow owner. Load .h5ad/10X data, manage AnnData metadata, plan scVI/scANVI batch-correction workflows, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.

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。

Primary retained Python toolkit for molecular biology sequence work. Preferred for sequence manipulation, FASTA/FASTQ/GenBank parsing, Bio.Entrez, BLAST workflows, alignments, structures, and phylogenetics. For biological database evidence lookup, use bio-database-evidence. For single-cell workflows use scanpy. For direct literature REST API, use pubmed-database.

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。