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概要と使いどころ

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)

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。

narrative-io/narrative-skills-marketplace92026年10月13日 更新

Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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。

narrative-io/narrative-skills-marketplace92026年10月13日 更新

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.

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。

jgsystemsconsulting/jgs-se-knowledge-packs82026年10月9日 更新

Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (August 2022 edition 8.02) with DOT&E-facing enterprise emphasis. Use for T&E enterprise roles (DOT&E vs USD(R&E)), DT&E/OT&E/LFT&E, cybersecurity T&E, MOSA/automation/digital enablers, suitability/reliability growth, and TEMP/Strategy/STE planning. Distinct routing from dod-te-guidebook (same guidebook family, pathway-deep pack). Guidance only—defer to DoDI 5000.89 and Title 10 for binding requirements.

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。

jgsystemsconsulting/jgs-se-knowledge-packs82026年10月9日 更新

Ideenschürf-Workflow für festgefahrene, schwere Probleme (Beweise, Forschungsfragen, hartnäckige Design-/Architekturprobleme): einen Ideenspeicher über acht Schürftechniken füllen (Wiedererkennung, Fern-Disziplin-Analogie, Alltags-Allegorie, Störgefühl/Ästhetik, Märchen-Reframing, Web-/Literatur-Recherche, Geschwisterprojekte, Bestandsquerlauf), dann gegen bereits Versuchtes filtern, eine Idee wählen und bis zur Substanz verfolgen. Nutze diesen Skill, wenn ein Problem trotz mehrerer Anläufe feststeckt, wenn „neue Ideen für X" gebraucht werden, bei „wir drehen uns im Kreis", oder für periodische Innovations-Läufe über ein Projekt. Für breites, freies Ideensammeln ohne festgefahrenes Problem stattdessen brainstorm (SCAMPER, Six Hats etc.).

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。

ellmos-ai/skills82026年10月12日 更新

Spezialist fuer den gesamten Bewerbungsprozess. Analysiert Stellenanzeigen, optimiert Profile (LinkedIn/CV) und generiert massgeschneiderte Anschreiben. Generiert ASCII-Lebenslaeufe aus einer SQLite-Datenbank und Ordnerstruktur. cv_generator.py ist standalone portiert -- keine BACH-Runtime noetig.

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。

ellmos-ai/skills82026年10月12日 更新

Große Kandidatenlisten durch optionale Themen-Pfützen, Filterstufen und wiederholt neu gemischte Bündel schrittweise reduzieren. Nutzen, wenn der User Skills, Aufgaben, Ideen, Dateien oder andere Einträge gruppenweise auswählen, nach Merkmalen filtern oder nur innerhalb gemeinsamer Themenbereiche mischen will, ohne übrige Einträge endgültig zu verwerfen.

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。

ellmos-ai/skills82026年10月12日 更新

pingpong

無料

Betreibt eine zeitlich begrenzte, sitzungsgebundene Funkstelle über einen gemeinsam synchronisierten Ordner. ListenSync scannt systematisch nach Aufträgen und Neuigkeiten; WriteSync versendet Deltas und Receipts. Verwenden bei PingPong, Dosentelefon, ListenSync, WriteSync, Sync-Listener, Auftragswache, News-Scan oder Goal-/Loop-Überwachung.

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。

ellmos-ai/skills82026年10月12日 更新

Manages your own skill landscape: surveys and compares existing skills (Audit mode), researches the web for new skills/plugins (Explore mode), and is at the same time the installer that generates lean subskills (Skill-Finder, family umbrella, maintenance skills) instead of loading a monolith. Use this skill for "compare/audit skills", "which skills are duplicated", "form skill families", "clean up/consolidate skills", "maintain the skill register", "find skills/plugins for topic X", "install new skills", "browse the skill marketplace", or for `/skill-explorer`. Delivers a sub-report per family and a globally numbered decision list; installs/uninstalls only after a security check and explicit approval.

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。

ellmos-ai/skills82026年10月12日 更新

Wählt vor komplexer Multi-Agent-Arbeit gemeinsam mit dem Nutzer eine passende, begrenzte Orchestrierung und erzeugt einen kompakten Session-Vertrag. Nutze den Skill bei /choose-your-orchestrator, bei unsicherer Kombination aus Orchestrator, Schwarm, Modellrouting oder Nutzerpräferenzmodell sowie vor Agentenprogrammen, die Modellslots, Parallelität, Spawn-Tiefe, Schreibgrenzen, Abnahme und Eskalation ausdrücklich festlegen müssen.

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。

ellmos-ai/skills82026年10月12日 更新

Anbieterneutrales Onboarding-Protokoll für einen neuen, neu aufgesetzten oder ausgetauschten Arbeitsplatzrechner. Es richtet Betriebssystemvoraussetzungen, Agentenlaufzeiten, gemeinsame Regelflächen, portable Skills, verifizierte Konfiguration und Nachweise nach der Installation ein, ohne Zugangsdaten, private Prompts oder host-spezifische Konfiguration in ein Repository zu kopieren.

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。

ellmos-ai/skills82026年10月12日 更新

Dach-Skill für Cloud-gestützte Kommunikationsprotokolle zwischen Agenten auf unterschiedlichen Maschinen (Ping-Pong, agent-beam, Listener und künftige Protokolle). Nutzen, wenn Arbeit über Maschinen hinweg per gemeinsamem Sync-Ordner oder Message-Yard koordiniert wird.

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。

ellmos-ai/skills82026年10月12日 更新

Ordnet freiwillig bereitgestellte Kontaktdaten datensparsam und erkennt mögliche Dubletten.

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。

ellmos-ai/skills82026年10月12日 更新

Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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。

ellmos-ai/skills82026年10月12日 更新

Interaktives Reißverschluss-Audit-Verfahren für eine Serie von Apps/Prüfobjekten: der User testet live und gibt kurzes Feedback im Chat, der Agent startet dabei bereits das nächste Objekt UND wertet parallel das Feedback des vorigen aus (strukturiert erfassen, Reparaturaufträge sofort an Worker delegieren) — statt sequenziell zu warten. Nutze diesen Skill bei "human-loop-audit", "lass uns die Apps im Reißverschluss durchtesten", "ich teste, du wertest aus und reparierst", oder wenn eine Reihe von GUIs/Produkten gemeinsam mit dem Nutzer durchgetestet werden soll und Befunde sofort in Reparaturen münden sollen. Abschluss ist eine strukturierte Befundliste je Objekt (Funktioniert/Defekt/Wunsch) plus die Liste der bereits gestarteten Reparaturen.

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。

ellmos-ai/skills82026年10月12日 更新

Prepares a DRAFT internal audit pack from the audit plan: scope as stated, criteria with clauses quoted from the documents provided, document requests per auditee with due dates, open interview questions per process and role, previous findings to follow up and a readiness checklist. Never pre-judges conformity, writes findings or selects the sample; the lead auditor decides. Use when the user asks to "prepare the internal audit", "build the audit pack from this plan", "draft the document requests and interview questions" or "get us ready for the supplier audit". Do not use for evidence requests built from a control list, use control-evidence-request-pack instead; to chase actions from earlier audits, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Turns a control list and an audit scope into draft evidence requests grouped by control owner, each with the control as stated, the audit period, evidence examples (commonly requested) and a proposed due date derived from the fieldwork dates. Never judges whether evidence is sufficient, never selects the sample and never sends a request; the auditor decides. Use when the user asks to "prepare the evidence requests", "draft the PBC list", "build the evidence request pack for the audit" or "what do we need to ask each control owner for". Do not use for document requests and interview questions built from an audit plan, use audit-prep-pack instead; to check claims in a report against sources, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Reviews a set of knowledge files or a knowledge base listing and returns a draft hygiene table, one row per file: duplication signals with the matching file, staleness signals, conflicts between files on the same topic with both passages quoted, missing or unverified owners, and one proposed action per file (Keep, Update, Merge into, Retire, Assign owner, Confirm with owner) for the owner to decide. Never edits, merges, moves or deletes a file. Use when the user asks to "review our knowledge base for duplicates", "which KB articles are stale", "find conflicting articles", "which articles have no owner" or "run a hygiene check on these files". Do not use for review-date sweeps of a document library with owner reminders, use sharepoint-review-sweeper instead; for writing one article, use knowledge-article-drafter. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Designs a pre-launch evaluation plan for a declarative agent from its instructions text, skills, capabilities, knowledge sources and sample requests: a behaviour contract of testable statements quoted from the text, test prompts by scenario family and tier with setup, expected behaviour, pass and fail criteria and red flags, a red-flag catalogue, a blank scoring sheet, a coverage table and a go or no-go checklist the owner completes and decides. Use when the user asks to "write test cases for my agent", "how do I evaluate this agent before launch", "build a test plan for the agent", "what should I check before publishing this agent" or "give me a go no-go checklist". Do not use for finding injection or leakage exposures in the instructions, use agent-instructions-red-team instead; do not use for reviewing one SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Builds a DRAFT data dictionary from a schema export, table definition, sample rows or a partial existing dictionary: one row per field with the name as stated, type as stated or observed, meaning quoted from the documentation supplied, owner, allowed values, and UNKNOWN wherever no source defines the field, plus a gap list for the data owner. Never infers a definition from a column name. Use when the user asks to "build a data dictionary for this table", "document these columns", "what does each field mean", "turn this schema export into a dictionary" or "fill in the field definitions from these documents". Do not use for profiling values or finding patterns in the data, use dataset-insight-pack instead; for metric definitions, use kpi-definition-sheet; for logging data defects, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs off.

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。

kesslernity/awesome-copilot-agent-skills82026年9月19日 更新

Read a media file's real structure with ffprobe through media_run - the JSON invocation and how to read it, what every family of field means, how to count frames, sample without decoding everything, detect HDR and interlacing, and diagnose a missing duration, a stream with no frames or a container that lies. Covers when media_probe's own report is already enough.

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。

vecnode/vncode82026年10月13日 更新

speak-as

無料

Write a public post in the owner's own voice with a role model's style and idea palette layered on top, reading a style-palette file so the same engine works for any thinker the owner has profiled. Draft-first. Triggers: "/speak-as <name>", "write in the style of <X>".

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。

tonydzi/second-brain-starter-kit82026年10月14日 更新

share-fix

無料

Turn a fix you made for a whole class of bugs into help for others: reverse-search GitHub for people hitting the same symptom, pick the 3-5 best live threads and post one tailored answer or gist each, no copy-paste fan-out. Use after a verified class fix. Triggers: /share-fix, /sf, share the fix.

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。

tonydzi/second-brain-starter-kit82026年10月14日 更新