“values”的搜索结果

Multi-backend observability CLI for querying metrics, logs, and traces across 12 backends. This skill should be used when the user runs obz commands, constructs metric/log/trace queries, configures providers, parses obz JSON output, or troubleshoots obz errors. Covers all core commands (metric query/list/info/labels/label-values/series, log search, trace search/get), global flags, time formats, output modes, config files, and exit codes.

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Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.

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GreptimeDB provider for obz. Covers PromQL metric query, labels, label-values, and series commands via GreptimeDB's Prometheus-compatible API. Use this skill when the user mentions "GreptimeDB", "Greptime", "obz metric -p greptimedb", or needs to query metrics stored in a GreptimeDB instance.

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Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, not fixed values. Use when building React Native, Flutter, or native mobile apps.

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Review the UI and style files changed in the working tree against this repo's own design system, before they are saved or committed. Runs the same deterministic check as the CLI's --check flag (no tokens, about a second): hardcoded colours where a token exists, near-identical colour twins, off-scale spacing, radii, font sizes and shadows, typefaces outside the system, arbitrary Tailwind values, static inline styles, !important, duplicate component definitions and imports of the copy to avoid, new tokens that copy an existing one, chart colours written by hand where the repo keeps a chart palette (and one calm warning asking for a palette where it keeps none), on a repo built on MUI, Mantine, Chakra or Ant Design, colours or pixel sizes written onto kit components where the theme has a value, and on a Tailwind theme or a shadcn repo, palette classes such as text-gray-500 where the theme names a colour of that kind. Findings name the file, the line and the fix. This is the small, fast check for what just changed; the roast skill is the full scan of the whole repo with a report.

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"Apply Upper Echelons Theory (Hambrick and Mason, 1984) to analyze how top management team characteristics — demographics, experiences, values — shape strategic choices and organizational outcomes. Use this skill when the user needs to evaluate TMT composition effects on strategy, predict strategic direction from leadership profiles, assess whether managerial discretion enables or constrains executive influence, or when they ask 'does leadership background matter for strategy', 'how does TMT composition affect decisions', or 'why did this management team make that choice'.".

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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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"Apply auction theory to compare the four canonical auction formats and assess revenue equivalence. Use this skill when the user needs to choose an auction format, evaluate bidding strategies, or determine when revenue equivalence breaks down due to risk aversion, asymmetry, or correlated values.".

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"Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.".

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Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, creative concept diversity for Meta Advantage+ (why six variants of one ad are not a diverse library, and what to brief instead), creator-made ad creative on a spend-share deal, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

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MobX observable state management patterns with mobx-react-lite. Use when implementing reactive client state with observables, computed values, actions, and the observer HOC.

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SCSS Modules - locally scoped component stylesheets, cascade layers, the Sass module system, mixins, and build-time versus runtime values

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TypeScript Result/Either types for type-safe error handling, railway-oriented programming patterns, error as values

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Unistyles 3.0 styling - C++ powered StyleSheet superset with zero re-renders, theming, breakpoints, variants, dynamic functions, runtime values

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React Native Reanimated 4 - shared values, animated styles, spring/timing/decay, layout animations, gesture integration, scroll-driven animations, interpolation, worklets, CSS animations

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Use when testing JWT-based authentication for algorithm confusion, alg:none bypass, weak HMAC secrets, missing expiration, kid parameter injection, and token storage in localStorage. Trigger on: Authorization: Bearer tokens, JWTs in cookies, any base64url encoded header.payload.signature pattern, OAuth2 access tokens, API authentication tokens, SSO tokens, JWKS endpoints. Detects RS256→HS256 confusion, public key as HMAC secret, unverified kid values used in file reads or SQL queries, and JWT cracking with short secrets.

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CRLF injection playbook. Use when user input reaches HTTP response headers, Location redirects, Set-Cookie values, or log files where carriage-return/line-feed characters can split or inject content.

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Test horizontal and vertical authorization bypass via session ID swapping between accounts, IDOR through parameter manipulation (invoice=, user=, menuitem=, EventID=), and special header injection (X-Original-URL, X-Rewrite-URL, X-Forwarded-For, X-Remote-IP, X-Client-IP with 127.0.0.1/localhost/RFC1918 values). Tools: Burp Suite with Autorize/AuthMatrix extensions, OWASP ZAP Access Control Testing add-on.

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Bootstrap a new standalone skill repo under gaia-research/ from the canonical template set at gaia-research/gaia-research/templates/skill-repo/. Handles the full flow: interactive prompts for all placeholders, fetches templates via the GitHub API, substitutes values, creates the new repo (gaia-research/skill-<name>), writes README.md / SKILL.md / install.sh / .gitignore / LICENSE / powered-by-gaia.svg, makes the initial commit and pushes. Also supports update mode: re-applies the template structure to a pre-existing skill repo and opens a draft PR with the drift. Use when the user says "new skill repo", "bootstrap a skill repo", "/skill-template", "create skill-foo", "spin up a gaia-research skill repo", "template a new skill", "align skill-fuse to the template", or "/skill-template update <repo>". Requires: gh CLI (authenticated with gaia-research org access), python3, git.

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Hunts for Cobalt Strike beacon traffic by detecting default stager URI checksum8 values, default Malleable C2 profile artifacts, and default ports/named pipes in HTTP and process telemetry. Activates for requests to hunt Cobalt Strike, detect beacon stager URIs, or find default Malleable C2 indicators.

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Generate accurate Volcengine SDK examples by locating an API through API Explorer search, fetching its swagger, and calling api/common/explorer/make-code with user-provided Params. Use when the user asks how to write Volcengine SDK code, generate SDK samples, or call a Volcengine API in Python, Go, Java, PHP, cURL, Node.js. Supports Chinese and English API names such as "角色扮演", "AssumeRole", or "STS AssumeRole". Also use for explicit SDK configuration questions about retry, timeout, AK/SK, STS, AssumeRole, temporary credentials, proxy, connection pooling, SSL, debug mode, request signing, response parsing, and error handling. If the user only needs API parameters, enum values, required fields, error codes, response schemas, pagination, or API comparisons, hand off to volcengine-api. If they need CLI-based operations, hand off to volcengine-cli.

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Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Parse natural-language quantities, units, dates, and ranges ("5'11\"", "1.5 cups", "72 in to cm", "three days ago", "between 5 and 10 kg", "it's hot") into canonical, validated values with issue codes and spans — and humanize them back. Use when a form input, LLM tool schema, MCP handler, or import pipeline takes free-text measurements/units/dates. Covers Standard Schema fields (quantityField/dateField/lingoObject) for the AI SDK and MCP, the headless <lingo-input>, unit conversion, and format/humanize round-trips. Zero-dependency TypeScript, deterministic.

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