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| Plugin / developer | Category | Follow |
|---|---|---|
AtlasBurnAkhil AnandAsk your AI cost, runway, and guardrails from your real AtlasBurn data. Read-only. Publisher capabilities · listing …ow total AI spend, token usage, and request volume Break down AI costs by model and product feature Forecast monthly AI bill, daily burn, and budget runway Report guardrail status, budgets, and recent breaches Flag retry loops and runaway agent behavior Read-only: never changes settings or sees prompts and outputs Show in context →2 more matching sourcesPublisher full description …ve requests or unusual spending patterns, investigate retries and costly agent behavior, and review forecasts and budget guardrails. AtlasBurn helps teams understand what is driving their AI costs and spot problems before they grow into larger bills. The connection is read-only and content-blind: it reads cost and usage telemetry from your AtlasBurn workspace, never changes your settings, and never sees your prompts or model outputs. Show in context →Publisher description Ask your AI cost, runway, and guardrails from your real AtlasBurn data. Read-only. Show in context → |
Developer ToolsVersion 1.0.2 | View details → |
Codiedev ReportsCodiedevRead-only engineering reporting from Codiedev for company admins and team managers: delivery, AI impact, velocity, adoption, model usage ... Publisher capabilities · listing …verview for the company or a team AI impact on cycle time and review Velocity trends and throughput forecast AI adoption and skill reuse Model usage and reliability AI spend, ROI and cost per merge (admins) Show in context →3 more matching sourcesPublisher full description Codiedev Reports answers engineering leaders' questions from their Codiedev reporting, right in the conversation. Ask how engineering is doing this month, whether AI-assisted pull requests actually merge faster, which repositories drive AI… …me stages, the AI-vs-no-AI comparison with its classification coverage, velocity and the throughput forecast, daily activity and rate-limit pressure, adoption and skill reuse, model usage and reliability, capture coverage, and (admins only) spend versus the prior period, ROI, cost per merge, seats a… Show in context →Publisher keywords · listing engineering metrics ai adoption developer productivity dora ai spend reporting Show in context →Publisher description Read-only engineering reporting from Codiedev for company admins and team managers: delivery, AI impact, velocity, adoption, model usage and AI spend. Show in context → |
Developer ToolsVersion 1.0.0 | View details → |
Site SEO & AI Crawler AuditAgent Tools Publisher full description …to publish, and the check runs once it is live. It never looks up a person, a user name or an email address. Page and crawler checks: which of 14 AI crawlers robots.txt allows on a page and the rule that decided it; the title, description, canonical, Open Graph and Twitter tags and which are missing… …price section, viewport and a contact path, each with evidence and a fix. It is a checklist, not a forecast. Exposure check: exposed or clean with the top 3 risks and fixes, from dangling subdomains, leaked files such as .env, admin paths and masked API keys in the page's scripts, plus six legal si… Show in context →1 more matching sourcesPublisher capabilities · listing …rawlers Shows the rule that decided each crawler, the sitemaps and any noindex or noai directives Reads a page's title, description, canonical link, language and icons Reads Open Graph and Twitter share tags and lists the common ones that are missing Follows the redirect chain with each status code… Show in context → |
Developer ToolsVersion 1.5.0 | View details → |
ML RefereeSandra Carrico Publisher full description …r feature. For machine-learning researchers and practitioners working with supervised prediction or forecasting, it checks submitted inputs, runs supported evaluations with user authorization, and retrieves results. Results are evidence within the declared evaluation setup, not guarantees of future performance or deployment readiness. An ML Referee account is required. Access requirements are described on the ML Referee website. Show in context →2 more matching sourcesPublisher keywords · listing machine-learning feature-selection forecasting model-evaluation Show in context →Publisher description Evaluate candidate features for supervised machine learning and forecasting. Show in context → |
Developer ToolsVersion 1.0.0 | View details → |
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