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Pipedrive CRM integration optimized for SMB sales teams
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Based on SOC occupation classification
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
JavaScript and TypeScript documentation generation using JSDoc and TSDoc. Parse source code, generate API documentation, validate coverage, and integrate with TypeDoc for comprehensive developer documentation.
Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
Zero-knowledge circuit development using Circom and Noir languages. Supports constraint optimization, ZK-friendly cryptographic primitives, proof generation (Groth16, PLONK), and Merkle tree implementations.
| name | pipedrive-connector |
| description | Pipedrive CRM integration optimized for SMB sales teams |
| allowed-tools | ["Read","Write","Glob","Grep","Bash","WebFetch"] |
| metadata | {"specialization":"sales","domain":"business","priority":"P2","integration-points":["Pipedrive REST API"]} |
| graph | {"domains":["domain:sales"],"skillAreas":["skill-area:sales-methodology","skill-area:customer-success","skill-area:revenue-operations"],"workflows":["workflow:customer-journey-optimization"],"roles":["role:account-executive","role:sales-engineer","role:customer-success-manager"]} |
The Pipedrive Connector skill provides integration with Pipedrive CRM, a sales-focused CRM designed for small and medium businesses. This skill enables deal and pipeline management, activity tracking, lead inbox operations, and custom field handling with an emphasis on simplicity and sales velocity.
Retrieve all deals in the active pipeline with their current stage, value, and days since last activity.
Create follow-up activities for all deals that have been idle for more than 7 days without customer contact.
Process incoming leads from the lead inbox, enriching data and routing to appropriate pipeline stages.