「outcome」の検索結果

Run outcome-focused Firecrawl workflows that produce deliverables such as research reports, literature reviews over published papers, SEO audits, QA reports, lead lists, knowledge bases, website design systems, and other structured web-data artifacts. Use when the user wants Firecrawl to complete a business, marketing, product, or creative workflow rather than merely scrape a page or integrate API calls into 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。

Create structured records of decisions with expected outcomes, probabilities, and quit conditions. Use for important decisions.

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。

Generate influence strategies using Cialdini's seven principles. Use when you need to influence toward an outcome.

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。

Transform research into Opportunity Solution Tree (Teresa Torres method). Map outcomes, opportunities, solutions, 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。

Use when the stakeholder asks about an analysis outcome or its implementation tasks - understand the human approval gate and review the tasks the architect created once the human approved

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 the stakeholder asks for release notes or a changelog - group done/released work into Added, Changed, Deprecated, Removed, Fixed and Security, in outcome language, with task references

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 updating the stakeholder - report in outcome language (what changed for users, what's next, what decision is needed), translate technical detail, and surface risks early

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。

Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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 (OST) to structure product discovery — map a desired outcome to customer opportunities, possible solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when the team is unclear what to build next, when multiple opportunities compete, or before writing a PRD for a complex feature space.

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 Trade Reviewer's procedure for journaling desk activity and reviewing trades from the exchange record - process graded separately from outcome, execution costs measured, one repeatable finding per review, plus the weekly desk review. Use after any send, when a trade closes, on the weekly routine, or when the user asks "how did that go".

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。

Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.

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。

Store a memory in AgentDB — an episode (task + outcome + critique), a pattern, or a skill. Use when the user says "remember this", "save this for later", "add to memory", or when the agent has just succeeded/failed at a task and the lesson is worth keeping.

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。

Generate a session report with token usage estimates, work summary, and outcomes

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 when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.

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。

Map evidence-based connections between a wellbeing initiative and specific academic learning outcomes. Use when justifying wellbeing programmes, integrating SEL, or linking pastoral and academic 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。

Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.

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 metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through 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。

Recommends team composition based on outcomes and mode. Invoked by launch commands during team setup.

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。

General mode operational spec for the team lead. Returns lead identity, facilitator identity, suggest-members guidance, and phase arc for general-purpose teams. The silent fallback when outcomes fit no specific mode, and a wrapper base for custom workflows — it has no shortcut command.

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。

Independent review loop run before delivery in code work. An independent reviewer (Codex, or fresh Codex-style subagents) reads the whole PR against the approved outcome; the lead fixes in-scope functional findings and re-reviews until none remain. Its differentiator is independence and exhaustiveness — a reviewer that fails differently from the authors, run to clean — distinct from the recursive-refinement ladder. Invoked by the team lead from the unified pre-ship gate (code-mode and /swarm:refine) after the ship steps.

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。

Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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。

Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.

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。

Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.

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。

Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive 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。