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The template links a Gumloop agent to BasicOps through MCP so it can read task context, respond to mentions, and act within the workspace.
VulnScout assembles public vulnerability identifiers, exploitation-status signals, and remediation references into a structured research brief.
The agent uses Reducto to extract resume information and can connect the parsed profiles with the recruiting team's Ashby and Slack workflow.
The Reducto-powered agent parses contract documents and extracts the specific provisions requested by the reviewer.
The Reducto-powered agent processes scanned, handwritten, and table-heavy PDFs that need more than basic text extraction.
The small template accepts a public GitHub skill link and helps add its instructions to Gumloop for a repeatable agent task.
The flow takes a brand, competitor, time window, and focus area and generates an emailed competitive-advertising research report.
The published flow uses Gemini 2.5 Pro to analyze a long YouTube transcript and draft a blog article, X thread, and LinkedIn posts in a Google Doc.
The flow enriches lead rows, drafts personalized messages with Gemini, checks CRM duplicates, and logs outreach and reply status in Sheets.
The AI Parser turns unstructured sales notes into deal data that the workflow uses to create or update CRM opportunities.
A webhook-driven research agent uses Parallel MCP tools, returns structured findings, and emails a formatted report, with Apple Shortcuts as an optional trigger.
Voxxy analyzes Google reviews for a venue or multi-location business and organizes praised experiences, recurring problems, competitor context, and improvement priorities.
The agent reads an X timeline using explicit inclusion, exclusion, timeframe, and post-type instructions rather than returning an undifferentiated feed summary.
The agent combines inbox context with Exa searches to research people, announcements, and other external facts relevant to an email draft.
The agent reads a deal's context and searches a reference-customer sheet for a suitable match to help the sales team prepare a reference request.
The Gumloop agent reads a blog draft or URL, uses Semrush context, and proposes keyword, structure, and title improvements for editorial review.
The agent analyzes multiple Gong transcripts using configured sales frameworks and returns cross-call insights through Slack.
The agent uses product documentation to handle support questions and can convert unresolved customer conversations into Jira issues with configured routing fields.
The flow reads a documentation URL, uses AI to identify concepts that may confuse readers, and saves draft FAQs in a Google Doc.
The flow researches reviews and forums, classifies comments into useful categories, and generates a summary of recurring product feedback.
The flow combines a feature concept, audience, and problem statement with Olostep research to draft a market-fit assessment in Google Docs.
The flow researches competitor releases and market signals and prepares evidence-backed roadmap opportunities for a product team's review.
Olostep research and AI synthesis turn public forums and review material into a persona-specific pain-point report and product questions.
The flow combines Apollo contact context and Olostep company research with an explicit ideal-customer profile to prepare a fit assessment.
The flow combines a prospect's website with Olostep research on hiring and market signals to create a targeted sales battle card.
The flow monitors a chosen Gmail label and uses an agent's explicit response and voice instructions to decide when a draft is warranted.
Gumloop shares a flow based on its expert-vetting process, combining public research, company context, internal usage data, and written requirements before reporting the assessment.
The flow checks imminent calendar meetings and has an agent gather email, CRM, Slack, and attendee research, sending a Slack brief when useful context exists.
This agent first explores the connected data source and records schema context, then answers natural-language questions with structured results and optional visual output.
Using Parallel research tools, the agent evaluates technical and content signals, identifies competitors, and produces a prioritized SEO gap report.
The agent combines Google Ads account data with Semrush and Exa research to identify weak keywords, competitive positioning, and optimization candidates.
The flow retrieves top posts for a chosen subreddit and time window and uses AI to report themes, sentiment, and notable outliers.
The workflow researches the chosen AI topic and turns the findings into a LinkedIn draft and related follow-up material.
The workflow extracts key dates and actions from school-related messages and stores concise records in Notion for family or student follow-up.
The flow reads matching Gmail messages, extracts attachment text with a file reader, and summarizes the PDF contents for review.