「duplicates」の検索結果

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

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Deploy DefectDojo as a centralized vulnerability management dashboard that ingests findings from 200+ security scanners, deduplicates results, tracks remediation metrics, and integrates with CI/CD, Jira ticketing, and Slack notifications via its REST API. Use when consolidating scanner output into one dashboard or automating vulnerability ticketing and executive reporting.

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。

Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds.

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Keeps the CRM current without the owner ever opening it. Creates and updates contacts and deals from email, calendar, and call transcripts; logs activity automatically; drafts follow-ups on deals that have gone quiet; and maintains a next-step queue so nothing sits unowned. Runs on demand or on a schedule, includes a hygiene sweep for duplicates and stale records, and builds a lightweight CRM in a spreadsheet for owners who don't have one. Use this whenever CRM data or pipeline hygiene comes up — including phrasings like "update my CRM," "log this call," "clean up HubSpot," "clean up Salesforce," "add this to the deal," "turn my meeting into a deal note," "which deals have gone quiet," "I hate data entry," or "my pipeline is out of date." Reach for it when the owner describes a call or meeting worth recording, even without naming the CRM.

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。

Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.

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Maintain the slopus/happy open source project. Triage issues, draft closing comments, find duplicates, check if bugs are fixed on main, and engage with community contributors. NEVER posts comments or closes issues without showing exact text and getting approval first.

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Weekly snack-request batching for {{office_channel}}. Collects Slack shortcut submissions since the last order, consolidates duplicates and standing staples, builds a cart against {{preferred_vendor}} within {{weekly_budget}}, and posts it for the office manager to approve before anything is placed.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。

Turn a rough report into a Linear issue someone can pick up — reproduce the claim, check for duplicates, and fill in team, priority, and labels. Use when the user says to file, open, or create an issue, hands over a bug report or error, or asks to get something into Linear.

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Triage a GitHub issue into something actionable — reproduce the claim, find duplicates, judge severity, and apply labels, milestone, and assignee. Use when the user asks to triage an issue, sort the backlog, deal with new issues, check whether a bug is already reported, or says "what should we do with this issue".

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Imports a pedestrian into CARLA on UE 5.8 as a spawnable, animating walker — imports the skinned FBX bound to CARLA's shared pedestrian skeleton, duplicates a donor walker blueprint and repoints it at the new mesh, and registers it in WalkerParameters.json as walker.pedestrian.<id>. Can also export a shipped walker to FBX, which is how you obtain a rig-conforming mesh to start from. Use when the user asks to "import a walker/pedestrian", "add a custom character", "clone a walker", or has a walker that spawns but stands still.

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。

Help clarify human bug reports, search for duplicates, and gather diagnostic evidence separately from a concise issue draft.

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。

Systematic workflow for auditing TypeScript codebases for import hygiene issues (value vs. type imports, duplicates), missing base class extensions, and incomplete barrel file re-exports, with actionable fix patterns.

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。

Review auto-memory entries and propose promotions to OPENSPACE.md, OPENSPACE.local.md, or retained auto-memory topics; detect duplicates, stale facts, conflicts, and ambiguous entries before making changes.

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。

Triage a batch of raw security findings. Verify each is real, collapse duplicates, re-rank by derived exploitability, and tag with an owner. Takes a directory or file of scanner output and writes TRIAGE.json + TRIAGE.md sorted by what actually needs engineering attention. Use when asked to "triage findings", "validate scanner output", "prioritize vulns", or "review the backlog". Runs interactively by default; pass --auto to skip the interview.

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。

Use when opening OpenRig fleet terminals into cmux — turning a rig, pod, mission, slice, or saved view into live agent tiles via `rig terminal --provider cmux`, or driving cmux on an agent's request. Same OpenRig view semantics as openrig-herdr (the verbs, honest-partial/degrade, read-only cross-rig, scroll/copy, same-size-only duplicates); cmux is the **best-effort** provider (herdr is the default and the proof-gated one). Prefer openrig-herdr unless cmux is specifically wanted.

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。

Search the vault using QMD semantic search. Use PROACTIVELY before reading files. Preference order: (1) MCP tools — mcp__qmd__query, mcp__qmd__get, mcp__qmd__multi_get, mcp__qmd__status — if they appear in your tool menu, use them first; (2) CLI `qmd --index <name> ...` as fallback; (3) Grep/Glob only when QMD is not installed. Trigger proactively for past decisions, incidents, people, meetings, architecture, patterns, or any vault content — and after creating/editing notes to check for duplicates and related content.

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。

Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies to produce an overall score, grade, and actionable suggestions. Triggered when users ask to check data quality, find missing or duplicate values, detect outliers, validate formats, profile data, or clean data.

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。

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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Use this skill to cut an APM release from the current worktree: assess whether the cycle since the last tag warrants a patch or minor bump (semver discipline against the merged-since-last-tag diff), sanitize the [Unreleased] CHANGELOG block into a dated version block with one concise "so what" entry per merged PR (drop internal-only churn, consolidate duplicates), bump pyproject.toml + uv.lock, run the CI-mirror lint chain, and open the release PR. Activate on "ship a release", "cut v0.x", "release prep", "bump and PR", "open release PR", "what kind of release do we need", or any phrasing that ends in opening a release PR -- even when the user does not say "skill". Stops BEFORE tagging; tagging stays a human gate that triggers the release workflow. Refuses to bump to a major (>= 1.0.0) version without explicit operator confirmation.

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。

Analyze a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。

Use whenever the agent creates, writes, moves, or renames a file in a team/delegate (shared) workspace, OR when the user asks to organize, clean up, restructure, audit, or find files in any workspace or the Vault, OR when starting a multi-file task or named project. Enforces a purpose-based folder convention (flat mode: notes/, data/, outputs/, scripts/, archive/; project mode: projects/<slug>/{docs,assets,source,reports,research}/), per-agent namespacing under shared/<agent_key>/, and pre-write discovery via memory_search, vault_search, knowledge_graph_search to surface related files and avoid duplicates. Trigger before any write_file or exec at workspace root, when starting a project, generating reports/assets/exports, delegating, or when the user says "messy", "where did I save", "tổ chức lại", "dọn workspace", "tạo report", "find related", "search vault". Do NOT trigger for read-only ops, edits inside an existing project tree (cloned repo), or short-lived files deleted in the same turn.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。