「utc」の検索結果

"Apply Upper Echelons Theory (Hambrick and Mason, 1984) to analyze how top management team characteristics — demographics, experiences, values — shape strategic choices and organizational outcomes. Use this skill when the user needs to evaluate TMT composition effects on strategy, predict strategic direction from leadership profiles, assess whether managerial discretion enables or constrains executive influence, or when they ask 'does leadership background matter for strategy', 'how does TMT composition affect decisions', or 'why did this management team make that choice'.".

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"Apply Granovetter's embeddedness theory to analyze how economic behavior is embedded in ongoing social relations, avoiding both over-socialized and under-socialized accounts. Use this skill when the user needs to explain why market transactions deviate from pure economic rationality, analyze how trust and social ties shape business dealings, evaluate structural vs relational embeddedness in inter-firm networks, or when they ask 'why do firms prefer existing partners over cheaper alternatives', 'how do social relationships shape economic outcomes', or 'is this market truly arms-length'.".

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"Apply public choice theory to analyze political decision-making as rational self-interested behavior. Use this skill when the user needs to evaluate government policy failures, rent-seeking costs, voting outcomes, or bureaucratic incentives, especially when the assumption of benevolent government is questionable.".

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"Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.".

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"Apply Complex Adaptive Systems theory to analyze phenomena exhibiting emergence, self-organization, co-evolution, and edge-of-chaos dynamics. Use this skill when the user needs to understand why a system behaves unpredictably despite known components, model agent-based interactions that produce emergent outcomes, analyze fitness landscapes, or when they ask 'why does this system behave in ways no one designed', 'how do local interactions create global patterns', or 'why do small changes sometimes cause massive system shifts'.".

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"Analyze market structures across perfect competition, monopolistic competition, oligopoly, and monopoly to predict firm behavior and market outcomes. Use this skill when the user needs to classify a market's competitive structure, predict pricing behavior, evaluate antitrust implications, or understand why an industry behaves the way it does — even if they say 'why can they charge so much', 'is this market competitive', or 'will prices come down'.".

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"Implement Elo rating system to rank items or players from pairwise comparison outcomes. Use this skill when the user needs to rank items from head-to-head matchups, build a competitive rating system, or evaluate relative quality from comparison data — even if they say 'player rating', 'ranking from comparisons', or 'competitive scoring system'.".

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。

Orchestrates the full law interpretation pipeline: MvT research, machine_readable generation with validation/BDD testing, and reverse validation. Invokes /law-mvt-research, /law-generate, and /law-reverse-validate sequentially. Use this skill proactively when: user wants to make a law executable, add machine_readable sections, or mentions "interpret" in the context of a Dutch law YAML file. Activate automatically when user opens a corpus YAML file without machine_readable sections and wants to make it executable.

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Generates machine_readable execution logic for Dutch law YAML files: writes the machine_readable sections, validates against the schema, and (when a shell is available) runs the BDD suites until the model is correct. Use this skill proactively when: editing or creating machine_readable sections in law YAML files, working with corpus regulation files, or when user mentions 'generate', 'machine_readable', or wants to make a law executable. Activate automatically when user discusses law YAML files that need executable logic.

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Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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Use for hands-on repository work when a quiet, risk-scaled engineering method can improve design, implementation, debugging, review, delegation, workspace lifecycle, or verification. Keep clear local changes direct, preserve the complete requested outcome, and add method only where risk or uncertainty justifies it. Do not use for general technical explanations, simple file lookup, casual discussion, or non-engineering writing.

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Choose responsibility for substantial work before deep execution: keep one responsibility's coupled parts in a single lane, and hand a bounded or independent lane to a worker when clean context, an unknown cause, independent review, or coordinator attention materially improves the outcome.

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Write and cascade OKRs (Objectives and Key Results) for company, department, team, or project level. Ensure objectives are inspiring and key results are measurable outcomes. Includes weekly check-in template. Use at the start of each quarter or when aligning strategy to execution. Do not use for KPI performance reviews or daily task tracking.

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Translate strategy into a balanced scorecard, strategy map, OKR bridge, and executive initiative portfolio with clear measures, targets, and owners. Use when leadership needs strategic alignment, portfolio prioritization, or a management dashboard tied to outcomes. Do not use for ad-hoc brainstorming with no strategic horizon.

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。

Prepare customer or executive business reviews with outcome tracking, adoption health, ROI narrative, risks, commitments, and next-quarter plan using customer success and CX frameworks. Use when teams need QBRs, success plans, churn-risk conversations, or executive account reviews. Do not use for generic slide beautification or cold outreach prospecting.

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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.

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Procedure for controlled, human-gated outbound README badge provisioning PRs (Issue #1817). Preflights upstream repositories, ensures single-badge placement with deep links, and records campaign outcomes without automated retries or multi-badge noise.

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Append a session/state snapshot to MEMORY.md (regardless of where it lives — for this repo it's `founder/MEMORY.md`). Use when the user says "snapshot memory", "save state to memory", "/memory-snapshot", "log what happened in MEMORY", or otherwise asks to record the current session's outcomes for the next orchestrator. Locates the project's MEMORY.md, prepends a dated entry above the previous snapshot (newest first), preserves every existing entry (NEVER overwrites or truncates), and reports back the file path + byte delta. Not for one-off feedback memories (use the user-level memory system at ~/.claude/.../memory/ for those). Not for trimming or deleting old entries.

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Firecrawl gives AI agents and apps fast, reliable web context with strong search, scraping, and interaction tools. One install command sets up three skill segments: live CLI tools, app-integration build skills, and outcome-focused workflow skills. Route the reader to the right usage path after install.

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Frames a structured, testable threat-hunting hypothesis: grounding it in adversary behavior and available telemetry, defining the data sources and detection logic, and setting success criteria and outcomes. Activates for requests to start a threat hunt, write a hunt hypothesis, or plan a hypothesis-driven hunt.

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Turn a moment of friction — a conflicting PR, a wedged runner, a mysterious red check, a repeated manual chore — into a durable, actionable report. Sizes up the scenario, captures the evidence, separates one-off from SYSTEMIC, proposes a fix, and routes it (fix now, or hand off). Reports are LIVING documents that the fixing agent updates with the outcome. Invoke whenever something feels like it shouldn't have been this hard, or when the same annoyance shows up twice.

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Use when a task needs the judgment of an English Language and Literature Professor — backward-designing a first-year composition or literature syllabus against a shared outcomes rubric, deciding whether an AI-detection or plagiarism flag on a student essay warrants an integrity case, reading where a tenure case's monograph actually sits in a university press's pipeline, or handling a content complaint about an assigned literary text.

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Use when a task needs the judgment of an Engineering Teacher, Postsecondary — diagnosing a spike in a gatekeeper course's D/F/W rate, running a capstone design course with an industry sponsor, closing the loop on an ABET student-outcome assessment, or building a tenure/promotion teaching-effectiveness case beyond raw student evaluation scores.

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Use when a task needs the judgment of a Postsecondary Computer Science Professor — diagnosing why a course's DFW (D/F/withdraw) rate spiked, redesigning an intro programming course's assessment structure, staffing TAs and grading load for a large course, deciding what to prioritize pre-tenure, running an academic-integrity (MOSS similarity) investigation, or mapping a curriculum change against ABET student 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。