「confidence」の検索結果

Use when ranking competing initiatives for a roadmap, breaking a tie between two features, or auditing a backlog for hidden low-value work via Reach × Impact × Confidence ÷ Effort.

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Use when constructing the finance-side forecast — top-down vs bottom-up shape, confidence bands, retro-loop. Triggers on 'build the forecast model', 'reconcile top-down with bottom-up'.

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[Research] Use when you need to review knowledge artifacts for completeness, citation quality, confidence accuracy, and template compliance.

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Identifies likely duplicate profiles or companies using available record evidence. Use when the user provides a list and wants duplicate groups, match confidence, and merge recommendations. Describe records as likely duplicates unless there is an exact deterministic match.

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Classifies professional profiles into buyer, influencer, user, decision-maker, founder, recruiter, or other personas. Use when the user provides profiles and wants persona labels with evidence and confidence. Must not infer sensitive personal attributes.

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Groups companies by industry, business model, size, maturity, market, or strategic fit. Use when the user provides company records and wants consistent segment assignments with definitions and confidence.

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Normalizes company names, industries, domains, locations, and descriptions. Use when the user provides company records and wants consistent canonical fields with confidence and unresolved items flagged.

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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.

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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.

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Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, confidence scoring, and command validation.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

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Generic helpdesk first-response skill for creating KB-based response drafts. Use when handling support tickets, creating response templates, or building a structured helpdesk workflow. Supports error code detection, keyword matching, confidence scoring, multi-language templates, and escalation workflows. Customize by providing your own KB articles and configuration.

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Use when simulating a technical interview to build confidence, expose reasoning gaps, and train structured communication under pressure.

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Concrete remediation proposal with Safe Harbour confidence — version bump, inline as first-party code, patch, workaround, or advisory. Use when applying a fix for a triaged CVE, evaluating fix-type trade-offs across registry / source / distro patches, producing rollback-safe manifest edits with dry-run verification, or generating a CycloneDX SBOM after the fix lands.

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Generate, evaluate, and improve Rosetta Stone attribute mappings for a Narrative dataset. Use when: "map this dataset to Rosetta Stone", "suggest normalized attributes for dataset N", "evaluate the mappings on dataset N", "why is this mapping low confidence", "fix this expression", "improve this NQL mapping expression". (narrative-common)

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Analyze a local codebase and infer the architecture it is actually using, including repo kind, best-fit dominant architecture, directly evidenced coexisting patterns, major subsystem exceptions, confidence, docs-vs-code drift, and repo-fit hints for downstream agents. Use when prompts ask what architecture a repo uses, whether it is really hexagonal or just layered, what subtle patterns shape a repo or slice, how a target slice fits a hybrid monorepo, whether documented architecture matches implementation, or when `/tw-tk` needs a repo-dialect preflight before making a minimal change.

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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

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Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2). Use for program/project schedule management of aerospace and large engineering efforts: the five sub-functions (Planning, Development, Assessment & Analysis, Maintenance & Control, Documentation & Communication); the Integrated Master Schedule (IMS) and the WBS/OBS/CBS breakdown structures; the Schedule Management Plan (SMP) and Basis of Estimate (BoE); Critical Path Method, logic, durations, float and margin; tiered schedule assessment (Requirements/Health/Basis checks, the Shock Test) and the Dimensions of Schedule Reliability; Schedule Risk Analysis (SRA), Integrated Cost-Schedule Risk Analysis (ICSRA), Monte Carlo, correlation, merge bias, and the Joint Confidence Level (JCL); baselining, replan vs. rebaseline, EVM schedule metrics (SPI, BEI, CEI, HMI, Earned Schedule, CPLI), and status/progress/forecast reporting. This is NASA's scheduling doctrine grounded in NPR 7120.5 and the GAO Schedule Assessment Guide. Thin on the underlying mechanics of CPM, statistics, and EVM (it points to companion handbooks); aerospace/space-flight-centric; anchored to the 2024 second edition.

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Knowledge base from the NASA Cost Estimating Handbook (CEH) v4.0. Use for NASA's 12-step, three-part cost estimating process (Project Definition, Cost Methodology, Cost Estimate); the WBS, CADRe, and ONCE data backbone; estimating methodologies (analogy, parametric/CER, engineering build-up, EVM, Delphi); ground rules and assumptions, data normalization and inflation (BY/CY/RY); the point-estimate-to-S-curve cost-risk chain and Unallocated Future Expense (UFE) reserves; Joint Cost and Schedule Confidence Level (JCL) analysis via probabilistic cost-loaded schedules; decision-support analyses (sensitivity, trade studies, make/lease-vs-buy, affordability, CAIV); and economic analysis (discounting, NPV, OMB Circular A-94). The cost-estimating complement to NASA's SE and risk packs. Does NOT reproduce the handbook's tool-specific procedures, worked examples, or appendix-depth methods (it names the appendices), nor cover full DoD/GAO cost guides, schedule construction, or formal risk management beyond the cost interface.

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Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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Proposes redactions of personal identifiers in pasted text or an attached document (names, contact details, identification and account numbers, dates that identify, quasi-identifiers) and returns a DRAFT redacted copy with consistent placeholder tokens plus a redaction table stating what was replaced, where and why, with a confidence level and a blank reviewer decision per row. Use when the user asks to "redact the names in this document", "de-identify this transcript before we share it", "strip personal data from these notes", "anonymise this report for publication" or "what would need redacting here". Do not use for assessing a planned processing activity, use dpia-draft-pack instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Never declares a document anonymised, safe to release or compliant. Drafts for human review; never approves, authorises or signs off.

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Turn any hypothesis, however fringe, into an argument map rather than a verdict: five independent lenses (consensus, skeptic, frontier, historian, experimental design), then a synthesis of for and against, evidence quality, a confidence tier and the cheapest decisive experiment. Triggers: "/research-swarm <hypothesis>", "map the arguments on X".

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- Build an entity-relationship link-analysis graph of an investigation — nodes, typed edges carrying source and confidence, aliases, and temporal validity — to expose shared infrastructure, bridging nodes and the real principal behind a [человек]. Use when an investigation has outgrown a list and needs a graph, or when asked how a set of people, companies and domains connect

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