「measure」の検索結果

Measure, root-cause, fix, verify and record performance contention on a developer machine: cloud-sync daemons (iCloud Drive, OneDrive, Dropbox, Google Drive), file indexers, AI-tool MCP server fleets, and automation browsers left behind by tests and browser skills. Trigger when the machine is slow, apps freeze, browser or MCP tool calls time out, load exceeds the core count, or before and after any UI/UX/performance test run. Includes the L0–L4 optimisation scale and the test-hygiene contract.

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When writing a commit message, branch name or squash title — measure the repo's own convention first, Conventional Commits as fallback — even on a bare 'commit this'.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Triggers: create a skill, new skill, build a skill, skill creation, write a SKILL.md, evaluate skill, test skill, skill eval, benchmark skill, optimize skill description.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Set up authorized C2 simulation workflows and measure defensive detection outcomes.

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Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.

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How to MEASURE this repo instead of eyeballing it — interpret the loop-station context-budget trend (history.jsonl, thresholds, the tokenizer-switch gotcha), read a session trace, use the eval ledger (cost-per-success), and pick the right evidence tool (app-shots, smoke-deployed, db-counts, --check modes). Use when asked "is the harness bloating", "how big is CLAUDE.md really", "did this agent run actually succeed", "prove this works", "what evidence do we have", or when tempted to declare a capability (browser, tool) unavailable. For RUNNING the loop-station scripts, defer to the loop-station skill — this skill is interpretation + the wider evidence toolkit.

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Observe the harness itself — (WS1) measure context budget as a deterministic, trendable inventory (tokens per CLAUDE.md / skill / agent, lexical redundancy, per-CI cold-write totals; committed history.jsonl), (WS2) reconstruct the real invocation trace (which skills/agents/tools fired and how they nested) from session transcripts, and (WS-A) roll the per-run CI traces up into a committed weekly usage record (per-skill invocation counts; usage.jsonl) — the usage side of the dead-weight join. Use for "context budget", "how big is CLAUDE.md / our skills", "is the harness bloating", "how do our agent loops actually run", "which skills actually get used", "trace this session", "loop station".

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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do...

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。

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do ...

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Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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Design and execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.

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Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability.

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Ultra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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Use when evaluating, designing, drafting, revising, stress-testing, or preparing academic articles, empirical research reports, literature reviews, systematic reviews, meta-analyses, journal submissions, reviewer responses, research methods, sampling, measurement, analysis, publication readiness, or citation/source audits.

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Knowledge base from NIST AI Risk Management Framework 1.0 (NIST AI 100-1). Use for AI risk management, trustworthiness characteristics, GOVERN/MAP/MEASURE/MANAGE functions, bias management, AI actor roles, TEVV, AI RMF profiles, and socio-technical risk framing. Does not cover sector-specific regulations, EU AI Act, ISO 42001 in depth, or the AI RMF Playbook tactical detail.

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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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Knowledge base from the GAO Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).

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Knowledge base from NASA NPR 7123.1D — Systems Engineering Processes and Requirements. Use for the 17 common SE processes (SE Engine), life-cycle reviews and entrance/success criteria, SEMP structure and content, ETA roles and tailoring/customizing rules, Technology Readiness Levels (TRL 1–9), SE work product maturity terminology, contracted project oversight, and MOE/MOP/TPM measurement frameworks. Does not cover NASA programme financial management, human spaceflight medical standards, or software engineering requirements in depth (see NPR 7150.2 for the latter).

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Knowledge base from OMB Circular A-94 (revised 2023-11-09) on guidelines and discount rates for benefit-cost and cost-effectiveness analysis of federal programs. Use for BCA principles, benefit/cost measurement, discount-rate policy, uncertainty/sensitivity, distributional incidence, and decision reporting. Rescoped at build to A-94 only (Army CBA Guide unreachable). Does not replace A-11 budget preparation detail, agency-specific statutory BCA rules, or Army CEAC process templates.

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Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).

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Knowledge base from NASA NPR 7150.2D — NASA Software Engineering Requirements. Use for NASA's Agency-wide software-engineering requirements: the software classification scheme (Classes A–F), the Requirements Mapping Matrix and class-driven applicability, tailoring and Technical Authority governance, the SWE-### 'shall' requirements across software management (Ch 3) and the engineering life cycle (Ch 4), the supporting disciplines (configuration management, risk, peer reviews/inspections, measurement, non-conformance — Ch 5), safety-critical and cybersecurity requirements, IV&V applicability, traceability, and the Appendix A definitions. Does not cover detailed software-assurance/safety procedures (see NASA-STD-8739.8), implementation how-to and document content (see NASA-HDBK-2203), the systems-engineering process framework (see NPR 7123.1), or program/project management (see NPR 7120.5).

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