「identification」の検索結果

Econometrics skill for instrumental variables and treatment effect estimation. Activates when the user asks about: "instrumental variables", "IV estimation", "2SLS", "two-stage least squares", "endogeneity", "weak instruments", "first stage", "Sargan test", "overidentification", "propensity score matching", "PSM", "average treatment effect", "ATT", "LATE", "local average treatment effect", "endogenous regressor", "instrument validity", "工具变量", "两阶段最小二乘", "内生性", "弱工具变量", "倾向得分匹配", "平均处理效应", "处理效应", "局部平均处理效应"

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Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

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Knowledge base from MIL-HDBK-61B, the DoD Configuration Management (CM) guidance handbook (7 April 2020, cataloged with Change 1, 2025). Use for defense CM across the acquisition and sustainment life cycle: the five CM functions (planning, identification, control/change management, status accounting, verification & audit), the three configuration baselines (FBL/ABL/PBL) and FCD→ACD→PCD documentation, configuration items (CIs/HWCI/CSCI), change instruments (ECP/NOR/RFV), the CCB and Class I/II classification, CDCA/AA authority, FCA/PCA audits, data management and data rights (CDRL/DID, DFARS rights ladder, master/authoritative source), and tailoring CM by phase (EIA-649-1 R/T/NR matrix, Appendix C templates), plus digital-era CM (digital twin, viewpoints, MOSA). NOTE: 61B is advisory guidance that adopts and points to the SAE EIA-649 / EIA-649-1 / GEIA-HB-649 suite for the authoritative CM requirements — this pack names and describes that suite but does not reproduce its copyrighted requirement text. Thin on: the EIA-649 requirement clauses themselves, form-level detail (DD-form field instructions, full DID content), and non-DoD/commercial CM frameworks.

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Knowledge base from IS-GPS-200 Rev N (NAVSTAR GPS Space Segment / Navigation User Segment Interfaces). Use as a worked ICD/IS exemplar: what an interface specification is, DIST-A and IRN/CCB change control, interface definition vs identification, composite-signal criteria patterns, NAV data as payload families, and time/definition hygiene. Covers Rev N synthesized notes only; does not transcribe Apps II–IV bit fields or PRN/Gold-code tables, and does not replace faa-std-025 preparation rules.

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Knowledge base from NASA Risk Management Handbook (NASA/SP-2011-3422). Use for risk-informed decision making, continuous risk management, performance commitments, risk scenario diagrams, risk disposition types, risk burn-down schedules, objectives hierarchies, deliberation and alternative selection, risk driver identification, and RM planning for space and safety-critical programmes. Does not cover domain-specific PRA methods (fault trees, event trees) in procedural depth — see NASA PRA Procedures Guide for those.

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Knowledge pack from 'Towards Digital Engineering: The Advent of Digital Systems Engineering' (Huang et al., Old Dominion University, arXiv:2002.11672, 2020). Use when reasoning about digital engineering, the DoD Digital Engineering Strategy and its five goals, the Authoritative Source of Truth, digitalization vs. digitization, digital augmentation, digitalized models, provenance, unique identification, and the four-level (vision/strategy/action/foundation) DSE research framework. Covers the conceptual/vision layer of the field. SCOPE LIMITS: this is a single ~28-page vision paper, not a methods handbook — it is thin on step-by-step procedures, tooling, case studies, and quantitative evaluation, and it predates 2020 so it does not cover later digital-engineering standards or recent AI/LLM advances. Use the sebok pack for established SE canon and MBSE depth.

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Structured 6-step procedure for improving, renovating, or rebuilding existing pipelines, individual project folders, documentation structures, or software stacks. Addressable as "pipeline optimizer" (for whole topic pipelines, e.g. a software, research, or game-dev pipeline) or "project-folder optimizer" (for individual project folders within a pipeline, e.g. a single software tool or paper project). Triggers on tasks like "improve pipeline X", "optimize the stack", "rebuild Y", "renovation", "pipeline refactoring", "clean up project folder", "improve folder structure", "unify conventions", "documentation consolidation", "integrate into existing system", or any substantial intervention in established structures. Delivers building-stock analysis, purpose clarification, ideal sketch, gap plan, empirical pain-point identification, and retests with fresh subagents. Prevents parallel standards, duplication, and pipeline breaks.

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Synthesises exit interview notes or leaver survey responses into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim evidence, breakdowns by broad group only for groups that meet a minimum group size, referred items and open questions for the people team, without naming or identifying any individual. Use when the user asks to analyse, summarise, code, theme or report on exit interviews, leaver feedback, attrition interviews or departure surveys, or says "what are our leavers telling us", "theme these exit interviews" or "summarise the leaver survey". Do not use for customer or product feedback, use customer-feedback-theme-synthesis instead; for redacting one document with no theming, use document-deidentification-pass. Drafts for human review; never approves, authorises or signs off.

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Drafts one anonymised DRAFT case study (situation, approach, outcome as evidenced, optional lessons) from engagement notes, status reports, closing reports, emails and metrics extracts the user supplies, with a fact trace to the notes, an evidence grade on every result, quotes only where the user supplied them with speaker role and permission, an anonymisation log and a confidentiality checklist that must be completed before publication. Use when the user asks to "write a case study from these notes", "turn this engagement into a customer story", "draft a success story for the website", "anonymise this project write-up" or "prepare a reference case for the proposal". Do not use for redacting identifiers from an existing document, use document-deidentification-pass instead; for checking whether a document's claims are substantiated, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.

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Proposes the piping content a P&ID adds to an accepted PFD process model: line inventory with off-page continuity and required ancillary lines, line identification elements per the project line numbering procedure, connection items at nozzles matched to a quoted schedule, per quoted standards, specification break questions and observations. Every proposal cites its source and revision; no source, no proposal; never sizes a line or selects a class. Use when the user asks to "run the piping enrichment", "start the discipline enrichment", "list the lines the P&ID adds to this PFD" or "fill the line identifiers from the numbering procedure" after gate G2. Do not use for instruments, loops or instrument tags, use instrumentation-and-control-enrichment instead; do not use before gate G2, use process-model-check. Drafts for human review; never approves, authorises or signs off.

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Reads a project or processing description and returns a DRAFT data protection impact assessment pack: processing description, necessity and proportionality questions, a risk table with blank rating cells, mitigations to consider and open questions. Never rates risk, determines the lawful basis or judges adequacy; the DPO assesses and signs. Works from descriptions, never personal data. Use when the user asks to "start a DPIA for the new monitoring tool", "draft a data protection impact assessment", "scaffold a DPIA from this processing description" or "what would the DPO need for a DPIA on this". Do not use for redacting or de-identifying a document, use document-deidentification-pass instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Drafts for human review; never approves, authorises or signs off.

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- Investigate a phone number — E.164 normalisation with libphonenumber, phoneinfoga scanning, carrier and line-type identification, VoIP and burner detection, messaging-app registration checks, and reverse-lookup and caller-ID sources.

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Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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Use when working with Codeowners — cODEOWNERS file management covering ownership mapping analysis, coverage gap detection, team assignment validation, orphaned path identification, and review requirement auditing. Use when auditing code ownership across repositories, identifying unowned code paths, validating team configurations, or optimizing pull request review workflows.

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.

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Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

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Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

CTF competition assistant helping participants solve security challenges. Challenge analysis, vulnerability identification, exploitation technique suggestions.

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Finish an existing Japanese business talking-head, lecture, interview, or testimonial rough cut as a credibility-first vertical social video. Use when the user wants an SNS short that preserves the speaker's meaning while tightening repeated ideas, adding a first-second thesis, readable full captions, restrained punch-ins, speaker identification, a registered CTA card, dialogue-first audio, and retention QA.

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Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.

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Conducts comprehensive network penetration tests against authorized target environments by performing host discovery, port scanning, service enumeration, vulnerability identification, and controlled exploitation to assess the security posture of network infrastructure. The tester follows PTES methodology from reconnaissance through post-exploitation and reporting. Activates for requests involving network pentest, infrastructure security assessment, internal network testing, or external perimeter testing.

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OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

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。