Sleuth Skills Pricing, Plans & Features in 2026 - The missing link in Engineering management | Pensero [Let's talk](../book-demo) [Login](/auth/login/) [Login](/auth/login/) [Let's talk](../book-demo) [Login](/auth/login/) [Blog](../blog) / Article ## Sleuth Skills Pricing, Plans & Features in 2026 Explore Sleuth pricing, plans and features in 2026 with a clear comparison of costs, deployment insights and plan limitations. ![](https://framerusercontent.com/images/WF5wXySb5oYBsfMDPbU0hRuXY3M.png?width=1600&height=900) ![](https://framerusercontent.com/images/GjPJ8lgQ2s9KH4YirhymwwZxVY.png?width=1152&height=1152) Pensero · Pensero Marketing · May 13, 2026 Sleuth operates two distinct products in 2026. The first is its engineering analytics platform focused on deployment health, DORA metrics, and change failure rates, which is what most searches for "Sleuth pricing" are looking for. The second is Sleuth Skills, a newer product built for the AI-assisted development era that manages AI skills, Model Context Protocols, and agent configuration across engineering teams. This guide covers Sleuth Skills specifically, including what the current pricing model means for enterprise teams and how it compares to the broader market for engineering intelligence. ## **Sleuth Skills: What It Is** Sleuth Skills is an AI skills management platform that automatically extracts, maintains, and distributes contextual knowledge from a codebase to power AI coding tools. Rather than requiring developers to manually configure prompts, context files, or MCP settings for each AI client they use, Sleuth Skills builds that knowledge layer automatically from existing repositories, Jira tickets, pull request history, Confluence documentation, and Notion pages, then delivers the right context to the right engineer through a single command-line utility. The platform integrates with the major AI coding clients including Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, Cline, and others. When a developer installs the utility, all skills, MCPs, and context stay automatically up to date as code changes. The goal is that engineers focus on work rather than configuration. ## **Sleuth Skills Pricing in 2026** Sleuth Skills is currently free to use during its beta period. The product is built for enterprise and will move to paid enterprise pricing after the beta ends. There is no self-serve paid tier: organizations that want to move beyond the beta will need to book a demo and engage with the sales team for custom pricing and terms. The enterprise tier includes the full feature set: Skill Extraction covers auto-extraction for every repository in the organization, draft and approval workflows, teams synchronized from GitHub, skills that auto-update when code changes, and context pulled from code repositories, Jira tickets, pull requests and commit history, and documentation via Confluence and Notion. Skill Sharing covers loading only the skills a user needs, shared libraries included automatically, and the ability to include all skills or only approved skills depending on governance requirements. Skill Workflows covers AI-generated drafts, draft and approval workflows, and the ability to assign skill approvers and publishers to maintain quality control over the knowledge base. Enterprise plan inclusions are SAML SSO, on-premise GitHub support, dedicated Customer Success, 24/7 support, and custom billing and terms. Knowledge is not locked to the platform. Organizations can download any AI assets at any time or sync assets with a GitHub repository, meaning the skills and MCPs built in Sleuth Skills remain accessible regardless of the platform relationship. ## **What Sleuth Skills Covers and Where It Has Limits** Sleuth Skills solves a specific and real problem: AI coding tools are only as good as the context they work with, and maintaining that context manually across a large engineering organization is operationally unsustainable. Auto-extraction from real development artifacts, combined with approval workflows that prevent low-quality context from reaching production AI tooling, addresses the configuration overhead that slows AI adoption at scale. The product is narrow by design. It manages the knowledge and context layer for AI coding tools. It does not measure whether those tools are improving delivery, whether quality is holding, whether AI-adopter teams are outperforming non-adopters, or whether the investment in AI tooling is paying off relative to what comparable organizations are achieving. Those are measurement questions, and Sleuth Skills is a configuration and distribution product. The beta pricing, free for enterprise during the evaluation period, makes the barrier to trial low. The enterprise-only commercial model after beta means smaller teams will either need to negotiate or look elsewhere. Data security is SOC 2 Type II compliant. Sleuth collects and stores only necessary metadata including PR titles and commit messages, does not store code, and follows standard best practices for data handling. ## **How Pensero Complements Sleuth Skills** [Pensero](https://pensero.ai/) is an empowerment tool for engineering performance that brings together real signals from GitHub, Jira, and the tools your team already uses to uncover how work moves, where it gets blocked, and how development practices and AI usage translate into real business impact. Sleuth Skills and Pensero address different layers of the AI coding problem. Sleuth Skills manages what context AI tools have access to. Pensero measures what those tools actually produce and whether it is making the engineering organization more effective. For organizations that have invested in AI coding tools and want to answer the board-level question, which is not "did we configure our AI tools well?" but "is AI actually making us more productive or just changing how work is done?", Pensero is the measurement platform that sits alongside any AI tooling stack. Pensero scores every work item for magnitude and complexity automatically using a combination of multiple AI models and agents working in concert, creates a unified view of delivery that goes beyond activity counts, and connects AI adoption directly to delivery, quality, and business outcomes. Key capabilities relevant to AI-adopting organizations: - **Benchmark:** ranks the engineering organization against all other Pensero customers on 10 performance dimensions using real anonymized production data. AI adoption rate and its downstream effects on delivery and quality are part of the benchmark, so organizations know not just whether they are adopting AI tools but whether that adoption is translating into competitive performance. - **Calibrate:** puts AI adopters and non-adopters side by side on 11 complexity-weighted metrics with the industry median as a built-in reference line. Teams that rolled out Cursor to one cohort and Claude Code to another can compare delivery, quality, and cycle time across those groups with data rather than intuition. This is the analysis most boards are now asking for. - **AI impact measurement:** tracks AI-generated versus human-authored code at the work-item level across Copilot, Cursor, Claude Code, and Gemini, then benchmarks adoption rates and downstream quality effects against real peers. Because the foundation is complexity-weighted, Pensero can tell you not just whether AI-adopter teams produced more output but whether the work was more valuable and whether quality held. - **R&D cost attribution:** automatically converts engineering activity into CapEx, OpEx, and [R&E attribution](https://pensero.ai/blog/r-d-deductions) backed by real delivery artifacts, with geography-aware team structure and office-level attribution supporting Section 174/174A documentation and audit-ready capitalization reporting. No estimates, no manual reconstruction, no year-end fire drills. - **Executive Summaries:** translate engineering data into plain-language TLDRs that every leader understands without manual interpretation. Pensero integrates with GitHub, GitLab, Bitbucket, Jira, Linear, GitHub Issues, Slack, Notion, Confluence, Google Calendar, Cursor, Claude Code, Microsoft Teams, Google Drive, GitHub Copilot, and more. Customers include TravelPerk, Elfie.co, Caravelo, ClosedLoop, and Despegar. Compliance: SOC 2 Type II, HIPAA, GDPR. Pricing as of March 2026: free tier up to 10 engineers and 1 repository; $50/month premium; custom enterprise pricing. The information about Section 174/174A in this article is for informational purposes only and should not be construed as tax advice. Tax treatment of R&E costs depends on specific facts and circumstances, industry classification, and company structure. Organizations should consult with qualified tax professionals, CPAs, or tax counsel before making R&E capitalization or expensing decisions. Pensero provides documentation tools to support [tax compliance](https://www.forbes.com/sites/nathangoldman/2025/04/22/simplifying-tax-compliance-criteria-may-enhance-corporate-innovation/) processes, but cannot provide tax advice or guarantee specific tax treatment outcomes. ## **Frequently Asked Questions** ### **What is the difference between Sleuth and Sleuth Skills?** Sleuth originally built its reputation as a deployment analytics platform focused on DORA metrics, change failure rates, and deployment health. Sleuth Skills is a separate, newer product that manages AI skills, MCP configuration, and context distribution for AI coding tools across engineering teams. They serve different purposes and are priced separately. ### **How much does Sleuth Skills cost in 2026?** Sleuth Skills is currently free during its beta period. After beta, it is priced as an enterprise product with custom billing and terms. Organizations interested in post-beta pricing need to book a demo to discuss commercial arrangements. ### **What AI coding clients does Sleuth Skills support?** Sleuth Skills supports Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, Cline, and others. The integration works through a single command-line utility that developers install once, after which all skills and MCPs stay automatically up to date across connected clients. ### **Does Sleuth Skills store code?** No. Sleuth Skills collects and stores only necessary metadata such as PR titles and commit messages. It does not store copies of code repositories. The platform is SOC 2 Type II compliant. ### **Will knowledge built in Sleuth Skills be locked to the platform?** No. Organizations can download any AI assets at any time or sync assets with a GitHub repository, keeping the knowledge accessible regardless of the platform relationship. ### **What is the difference between Sleuth Skills and Pensero?** Sleuth Skills manages the context and configuration layer for AI coding tools, ensuring engineers have the right skills and MCPs available automatically. Pensero measures whether AI coding tools are delivering value, scoring work for complexity and quality, benchmarking AI adoption against real industry peers, and connecting AI usage to delivery outcomes and financial compliance. The two products address different problems and can be used together. ### **Does Pensero measure the impact of AI coding tools?** Yes. Pensero tracks AI-generated versus human-authored code at the work-item level across the major AI coding tools, benchmarks adoption rates and downstream quality effects against real peers, and enables cohort comparison between AI adopters and non-adopters on 11 complexity-weighted metrics. This makes AI ROI measurable rather than assumed. # Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. [Let's talk](../book-demo) # Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. [Let's talk](../book-demo) # Get months of engineering performance data now Stop deciding on gut feel. Get 90 days of objective data in minutes. 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