'justification' 검색 결과

Build and deliver a raise request that actually works — the evidence, the number, the timing, and the exact words — instead of hoping it gets noticed. Use when asked how do I ask for a raise, I deserve more money, prepare me to ask for a raise, or negotiate a pay increase at my job. Produces a value case built on your actual contributions and market rate, a specific target number with justification, the right timing and person, a script for the conversation, and responses to the likely pushbacks — turning 'I want more' into a business case your manager can say yes to.

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Use when strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Organize a Power BI or Analysis Services semantic model end to end so it is ready for report authors, Excel users and Copilot - table groups, display folders, Title Case business names and naming conventions, hierarchies, sort-by columns, hiding, Analyze in Excel (IsAvailableInMDX), descriptions, format strings, data categories, context annotations, DAX formatting and UDFs, calculated-object justification, duplicate removal, perspectives, translations and security roles. Use whenever the user asks to "organize", "tidy", "clean up", "polish", "prepare", "standardize" or "make report-ready" a model or one table in it (for example "organize the date table"), to "fix naming conventions", "rename fields", "make names human readable" or "fix abbreviations", even when they name only one of these aspects, and whenever a model is driven through the Tabular Editor 3 MCP or the te CLI for that purpose. Not for DAX performance tuning (dax skill) or model design and star-schema changes (semantic-model skill).

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Audits existing table permissions on a Power Pages site by analyzing them against site code and Dataverse metadata, then GRADES the configuration with deterministic 1–5 scores across 3 security/usability/correctness categories (9 dimensions). Detection is anchored by a recurring-issue catalog so findings and severities stay consistent run-to-run. Generates an HTML report with an at-a-glance verdict and per-category scores in the Overview plus a single list of **major/minor** issues (each major issue labelled permissions-only or upstream-caused with a short justification), and suggested fixes. Use when the user wants to review, verify, check, score, or grade table permissions for security issues.

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Develop a business case for an initiative using costs, benefits, risks and evidence. Use when a sponsor needs budget or timeline justification.

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Scope management for legal matters — baseline capture, in-flight change control, OOS documentation, and scope retrospective. Use when asked to review scoping assumptions, assess whether work is in or out of scope, draft a scope change notice, track scope changes, prepare an OOS justification, run an OOS report, prepare a scope call agenda, or review what changed on a matter. Trigger on: 'scope change', 'out of scope', 'OOS', 'scope creep', 'is this in scope', 'the client wants us to also', 'additional work', 'scope review', 'what changed from the original scope', 'we need to revisit the quote', 'the budget assumed', 'OOS report', 'scope call with the client', 'run a scope report'.

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Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a structured AskUserQuestion multi-option picker for user agency, and dispatches the chosen mode. Mode D (Atlas Fleet) is selectable only when detect_capabilities returns tier=premium (licensed atlas-launcher detected); otherwise it is a locked Atlas Pro teaser. Plan Mode is NOT used (spec section 13.5): AskUserQuestion is the sole user-agency mechanism. Declares HANDOFF complete so EXECUTE can follow.

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Standardize BRD and BRD-lite discovery for business goals, stakeholder impact, current-to-future state, and measurable value outcomes. Use when creating BRD, business case, project justification, ROI narrative, or AS-IS to TO-BE scope.

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Audits changes for additive bias and Iron Law compliance. Use when reviewing completed work before merging or after AI-assisted implementation.

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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

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Designs and implements data retention schedules compliant with GDPR Article 5(1)(e) storage limitation principle. Maps data categories to retention periods with legal basis justification, regulatory minimum holding periods, and automated review triggers for schedule maintenance. Activate for retention policy, storage limitation, data lifecycle, retention period queries.

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Generate prioritized CVE watchlists and actionable security recommendations for repositories. Use when analyzing CVE scan results, creating security reports, prioritizing vulnerability remediation, or generating security gate reports for CI/CD. Takes CVE scan results (JSON/SARIF from npm audit, pip-audit, Snyk), reachability analysis, and cutoff date as input. Combines severity, reachability, exploitability, and dependency criticality to rank CVEs by practical risk. Outputs markdown reports with concrete next-step guidance (immediate upgrade, monitor, ignore with justification, apply mitigation) suitable for issue trackers, security reviews, and CI security gates.

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Verifying YAGNI, cutting scope, and proposing complexity reductions. A 'subtraction' agent questioning the justification for every feature, dependency, doc, and config. Does not write code.

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Audits a methods section against a 45-item reporting checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT, covering pre-registration, recruitment, randomization and treatments, sample flow and attrition, sample-size justification, three-tier results labeling, conjoint reporting, validity, and open-science infrastructure. Use when checking whether a methods section reports enough, preparing a submission or replication archive, asking what CONSORT, JARS, or DA-RT require, or documenting a deviation from a pre-analysis plan. Writing that plan before data collection goes to pre-registration-writing.

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The Mileva Method (CRISP) — Phase 1: Clarify. Problem definition, eliciting, painkiller test, buy vs build, AI justification gate, Go/No-Go. Use at the start of any AI implementation project. Triggers on "clarify", "phase 1", "start discovery", "define the problem", "what problem are we solving", or at the beginning of a new client engagement.

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The Mileva Method (CRISP) — Phase 5: Prove. Success validation against Phase R baseline metrics, test log review, and AI justification validation. Use after deployment. Triggers on "prove", "phase 5", "validate", "did it work", "success criteria", "measure results", or after go-live.

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Systems-level performance engineer: identifies hotspots, provides optimized code with resource justifications.

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Perform a ruler-and-compass construction with step-by-step justification for each operation, producing a constructible geometric figure from given elements. Covers classical Euclidean constructions including perpendicular bisectors, angle bisectors, parallel lines, regular polygons, and tangent lines. Use when given geometric elements (points, segments, angles) and asked to produce a Euclidean construction, verify constructibility, or generate ordered construction steps for educational or documentation purposes.

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Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

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Review AI Gene Review pull requests and curation changes for GO term quality, evidence support, core function identification, and annotation action justification. Use for PR review comments, review follow-up automation, and QA of gene review YAML.

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Build a business case with costs, benefits, and ROI. Use when the user says "cost-benefit analysis", "business case", "ROI calculation", "is this worth it", "justify the investment", "payback period", "benefits realization", "financial justification", "total cost of ownership", "should we build or buy", "investment appraisal" - even if they don't explicitly say "CBA".

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Audit Go nolint directives for staleness and lazy justifications. Mechanically verifies each suppression with golangci-lint, then runs adversarial Red/Blue/White debates on the top candidates for removal. Use when inheriting a Go codebase, during periodic cleanup, or when nolint count is growing unchecked.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.