marketing-operations — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited marketing-operations (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
This skill covers marketing operations best practices across tech stack management, AI adoption and workflow automation, attribution and measurement infrastructure, lead scoring, data hygiene, and team operating systems. All practices are sourced exclusively from Exit Five podcast guests across 35 contributing episodes. Where guests disagree, both positions are presented with full attribution — do not treat contested areas as settled.
Guide users through a structured AI adoption progression: first a testing and learning phase where teams experiment with tools and approaches, then an acceleration and transformation phase where successful experiments are scaled into workflows. Emphasize that progress matters more than perfection in the early phase. Remind users to ensure all AI tools pass privacy, safety, and regulatory requirements before deployment, and to work cross-functionally with legal, IT, and engineering to maintain guardrails. (Source: Davang Shah, Episode #338)
When a user is getting started with AI workflows, instruct them to pick a single, small task that is core to their business and build automation for that first. Do not recommend replicating complex multi-step demos immediately. Examples of good starting points: automating guest research for webinars, extracting key questions from sales calls. Once that workflow is mastered, expand to more complex automation. This prevents overwhelm and builds confidence. (Source: Eoin, Episode #290)
When helping users build multi-step AI workflows (in Clay, Zapier, AirOps, or similar tools), instruct them to break the workflow into discrete steps where each step does one thing well, rather than one large end-to-end prompt. This makes workflows easier to debug and refine, and allows human checkpoints between steps for quality control. Example: instead of "generate a complete webinar package," break it into: (1) generate title options, (2) research guest, (3) generate agenda, (4) generate email copy — each testable independently. (Source: Dan, Episode #290)
Set up a Zapier Agent workflow that listens to sales call recordings (e.g., from Fathom), extracts competitor mentions and objections, and populates a structured spreadsheet with: date, meeting ID, meeting title, competitor name, context of mention, sentiment, objection category, severity, and whether it was addressed. Include quality control rules in the prompt (e.g., exclude self-mentions, spell competitor names consistently). The output feeds dashboards tracking brand share of voice, new competitor emergence, and objection patterns — enabling product marketers to prioritize content and messaging without manually reviewing transcripts. Expect the first version to require iterative prompt refinement through multiple test runs. (Source: Dan, Episode #290)
Before feeding sales transcripts or other raw data into AI analysis workflows, recommend adding a cleaning step that standardizes competitor names, fixes common transcription errors, and removes irrelevant sections. Alternatively, handle this in the prompt itself by specifying spelling conventions and exclusions. Cleaner input data produces better AI output. (Source: Dan, Episode #290)
Instruct users to set up proper integrations and MCP servers so that AI agents can pull data from multiple sources (CRM, email, ads platform, etc.), synthesize that data, and take actions across systems based on a single prompt. Example: an agent that monitors all LinkedIn campaigns daily, identifies underperforming ones, and pauses or reallocates budget when given permission. This requires proper system architecture and data flow setup. (Source: John Short, Episode #271)
Explain that MCP (Model Context Protocol) servers are instruction sets that tell LLMs how to interact with external tools and pull data with proper context. Instead of relying on basic API integrations, recommend setting up MCP servers so that when a user asks an LLM to retrieve data (e.g., "give me all recent activity on this contact"), the LLM knows exactly how to fetch and structure that information. This enables more powerful agent workflows where LLMs can take actions across multiple connected systems. (Source: Dan Guenet, Episode #271)
Recommend leveraging Claude's code generation capabilities to quickly build custom applications and workflows without traditional development overhead. Example use case: building a fully customizable onboarding workflow for customers instead of using off-the-shelf form tools. This allows non-engineers to prototype and deploy solutions faster, reducing time-to-revenue and enabling faster iteration on customer-facing processes. (Source: Justin Johnson, Episode #271)
Recommend using no-code tools like Lovable to build internal software that solves marketing team operational problems — not just landing pages. Example: a custom tool to automate newsletter workflow tasks that previously took 3+ hours per week. This eliminates dependency on engineering resources and allows marketing teams to ship their own solutions quickly. Applicable to any repetitive internal process: data synthesis, workflow automation, team coordination. (Source: Jess Lytle, Episode #319)
Recommend creating a custom ChatGPT instance loaded with institutional knowledge: ICP, pain points, company history, best-performing campaigns, best-performing emails, best and worst customers, and key strategic decisions. Use this to onboard new team members by giving them access to a searchable brain of company knowledge. New hires can ask the AI about past experiments, customer insights, or strategic context without waiting for a manager. (Source: Dave Gerhardt, Episode #288)
Recommend using AI tools to help marketers without technical backgrounds perform tasks that traditionally required engineering or design skills — creating videos, images, audio, and demos without specialized tools or external support. This democratizes technical execution and allows marketing teams to move faster. (Source: Andrew Davies, Episode #195)
Position AI as a tool to automate lower-value, non-creative work so the team can focus on higher-impact initiatives. Specific use cases: account identification and enrichment, prospecting at scale, on-brand content generation from prompts, landing page code generation, and campaign asset creation. The goal is compressing time-to-ship from weeks to days. This requires the team to be AI-literate and to view AI as a productivity multiplier. (Source: Gurdeep Dhillon, Episode #280; also Episode #203)
Note: How to approach AI tool procurement is contested — see Where Experts Disagree.
When a user inherits a budget with excessive technology spend, instruct them to conduct a martech audit to identify redundant, underutilized, or non-strategic tools. Consolidate vendors where possible and cut tools that don't directly support company goals. Reallocate freed annualized software costs to campaign spend and GTM activities. Prioritize tools that directly enable core marketing motions over nice-to-have platforms. (Source: Rowan Tonkin, Episode #197)
Note: Whether to minimize or expand the stack is contested — see Where Experts Disagree.
When a user is evaluating new tools, recommend establishing a corporate mandate that any new tool brought on must have an AI component built in. During procurement, require both a business case and a team need statement for each tool, and evaluate how it connects to or is additive to the existing stack. (Source: Sara Ajemian, Episode #288)
Note: This approach is contested — see Where Experts Disagree.
Instruct users not to implement complex tools (multi-touch attribution, high-volume lead routing, etc.) until their company has the foundational maturity to use them effectively. Early-stage companies should focus on basic data quality and core processes first. An outbound-driven business should prioritize data enrichment and prospecting strategy before investing in sophisticated lead routing tools. Complexity should scale with the ability to execute on fundamentals. (Source: Sean Lane, Episode #274 and Episode #187)
Recommend using shared tooling between marketing and outbound teams to create visibility across campaigns and intent signals. Specifically: implement Qualified to give both teams visibility into website visitor behavior and campaign performance. Experiment with Clay for data enrichment and phone number accuracy. Phone calls remain highly effective when data hygiene is strong and phone numbers are accurate. (Source: Kelly Cheng, Episode #297)
Recommend using a swag fulfillment platform (Sendoso) to store, manage, and ship branded merchandise at scale. Set up automations triggered by CRM events (e.g., deal closed) to automatically send swag without manual intervention. Leverage the platform's economies of scale to reduce per-unit costs and shipping rates compared to in-house fulfillment. (Source: Kris Rudeegraap, Episode #159)
Advise users to recognize that AI capabilities are being integrated into existing platforms (Gong, HubSpot, etc.). Shift the evaluation frame from "do we need an AI tool?" to "which tools have the best AI features for our use case?" (Source: Mychelle Mollot, Episode #182)
Note: This framing is contested — see Where Experts Disagree.
Instruct users to set up LinkedIn's CRM sync and Conversions API as mandatory infrastructure for measuring LinkedIn ad effectiveness beyond last-click attribution. CRM sync connects first-party CRM data with LinkedIn's platform data, enabling visibility into which accounts and contacts engaged with ads and later converted. Conversions API tracks downstream actions (website visits, content downloads, demo requests, pipeline stage changes) back to ad exposure. Together, these tools enable measurement across the full B2B deal cycle and multi-touch attribution. (Source: Davang Shah, Episode #338)
Recommend LinkedIn's Revenue Attribution Report as a free built-in starting point. Connect Salesforce CRM to LinkedIn Business Manager to see which campaigns influenced deals in the pipeline. While limited in scope, it's a quick way to see LinkedIn's impact on revenue without additional tools. (Source: Anthony Blatner, Episode #243)
For richer attribution, recommend pulling LinkedIn campaign data and CRM data into Looker or Tableau and creating blends that match leads/deals to their source campaigns. This allows users to see the full journey: which LinkedIn campaign a person saw, when they visited the site, and whether they converted. Requires more setup than built-in tools but provides much richer attribution. (Source: Anthony Blatner, Episode #243)
Recommend Hockey Stack and DreamData as platforms that use APIs to connect all advertising and marketing tools and show the full customer journey: LinkedIn impression → Google search → website visit → demo request → deal closed. Recommend Metadata IO for consolidating LinkedIn campaign data with website engagement data into a single source of truth. (Source: Anthony Blatner, Episode #243; Tagg Bozied, Episode #243)
If the user has HubSpot or another CRM with LinkedIn sync capability, recommend creating lists for target accounts and open opportunities and syncing them to LinkedIn so campaigns automatically update as the sales team adds/removes accounts or closes deals. This keeps LinkedIn targeting in sync with the sales pipeline without manual updates. (Source: Anthony Blatner, Episode #243)
Instruct users to connect their CRM to ad platforms (Google Ads, Meta, LinkedIn) to send downstream conversion signals (SQLs, meetings booked, trials started, deals closed) back to the platform. Tag different conversion types with relative values (e.g., a trial is worth 2x a form fill). This trains the bidding algorithm to optimize for quality leads, not just volume, and reduces reliance on platform-only metrics. (Source: Kym Parker, Episode #201)
Recommend implementing an integrated outbound strategy that runs email, LinkedIn outreach, and paid ads simultaneously and tracks the entire buyer journey across all channels in a single CRM record. Log every touchpoint chronologically. Use unique identifiers (work email, personal email, contact ID) to ensure all touchpoints map to the same contact record. Use Clay for enrichment to ensure the right identifiers to stitch data together. (Source: Alex Fine, Episode #245)
Instruct users to store all campaign data and pipeline metrics in Salesforce (where sales lives and works) rather than HubSpot, even if HubSpot has similar fields. Ensure all campaigns in Salesforce are tagged with campaign value. Pull all metrics from Salesforce into a unified dashboard (e.g., Tableau). This eliminates data disparity between marketing and sales and builds credibility. Sales should not have to use HubSpot to verify marketing's claims. (Source: Aditya Vempaty, Episode #235)
Instruct users that marketing ops and analytics should track and measure pipeline creation all the way through to qualified opportunities in the sales pipeline (typically defined as >25% win probability), not stop at form fills or demo requests. This provides true visibility into the efficiency of the entire pipeline creation machine and prevents false attribution of success at early funnel stages. (Source: Chris Walker, Episode #211)
Recommend working with the sales team to log the first meaningful meeting or conversation with each prospect in the CRM. This creates a leading indicator that correlates with future revenue but moves much faster than closed deals. Once this data exists, users can measure marketing's impact on overall meetings booked (regardless of source) and use that as the primary success metric for testing and optimization. (Source: Pranav Piyush, Episode #239)
Recommend adding a simple open text field (not a dropdown) to website forms asking "How did you hear about us?" Open text fields capture raw, unfiltered attribution data and can surface unexpected channels (e.g., LLM search, specific content pieces, word-of-mouth). Dropdowns force users to pick from predefined options and often result in lazy selections. Note: Kandji discovered that 17% of inbound traffic attributed to LLM search using this method. (Source: Sylvia LePoidevin, Episode #306)
Instruct users to create and maintain written documentation that defines what constitutes pipeline readiness, what qualifies as a first touch, and how influence versus attribution is measured. This removes ambiguity and reduces conflict between marketing and sales over credit for opportunities. The documentation should be agreed upon by both teams and used consistently. (Source: Ruth Zive, Episode #175)
Recommend setting up automated weekly dashboard refreshes (e.g., every Sunday night) pulling data from the source-of-truth system. Review metrics every Monday morning with the team. This cadence allows users to spot problems early and course-correct mid-quarter rather than discovering issues at quarter-end. Weekly visibility also prevents leadership from asking ad-hoc status questions. (Source: Aditya Vempaty, Episode #235)
Instruct users to track marketing expenses using accrual-based accounting, where all costs for an initiative are recorded in the period when the work occurs, not when payment is made. For example, all event expenses should be recorded in the month the event happens, not when the vendor invoice is paid. Do not worry about cash payment timing — that is finance's responsibility. (Source: Rowan Tonkin, Episode #197)
When an account is identified as high-intent (based on signal combination), recommend triggering an automated workflow that simultaneously activates multiple channels: advertising across channels, alerts to field evangelists to engage in communities, alerts to sales reps to identify connections on LinkedIn Sales Nav, and alerts to marketing to identify relevant contacts for outreach. This coordinated multi-channel approach increases visibility and engagement velocity. (Source: Jean Cameron, Episode #315)
Note: How to handle lead scoring and intent qualification is contested — see Where Experts Disagree.
Recommend replacing multiple disparate scoring models with a single machine learning scoring model aligned to the CRM's lifecycle object (e.g., Salesforce). Apply early-stage indicators at the top of the funnel to predict conversion from awareness through opportunity creation. Score buying committees (not just individuals) and continuously validate that scores correlate with actual conversion to meetings and opportunities. (Source: Morgan Cole, Episode #315)
Note: This approach is directly contested — see Where Experts Disagree.
Recommend eliminating lead scoring models that treat form fills and engagement actions as qualified leads. Stop internal debates about what constitutes a lead versus a contact. Only assign leads to sales when they have demonstrated clear buying intent (e.g., filled out a demo request form, reached a specific engagement threshold). Use AI to revisit the scoring model and set a clear engagement threshold before any contact is assigned to the sales team. (Source: Aditya Vempaty, Episode #304)
Note: This approach is directly contested — see Where Experts Disagree.
Recommend incorporating data quality into the sales team's performance structure or compensation framework. Frame it as a cost issue: poor data entry means lost money because duplicates and bad records waste SDR time and reduce efficiency. Make it clear that failing to maintain data hygiene directly impacts the ability to hit targets and earn compensation. (Source: Sara McNamara, Episode #256)
Recommend creating a shared project board (Google Sheets, Asana, Monday, etc.) that tracks customer interview candidates through stages: recommended by CS, ICP fit assessment, interview request sent, interview completed, insights documented. Collaborate with the customer success team to identify and vet candidates. This makes the process repeatable rather than ad-hoc. (Source: Shoshana Kordova, Episode #250)
After each customer interview, recommend documenting key excerpts (direct quotes or paraphrases) in a centralized spreadsheet or database (Google Sheets, Airtable, etc.) and tagging each insight by type: differentiator, ROI metric, testimonial, pain point, etc. This allows filtering and retrieval of relevant insights without re-listening to recordings. The database becomes a living resource accessible by product, sales, and other teams. (Source: Shoshana Kordova, Episode #250)
Rather than centralizing all data science, recommend allocating data scientists to specific business units while maintaining a shared measurement and brand tracking team. This allows each unit to have dedicated analytical support while ensuring consistency in how metrics are defined and tracked. The data science team's role is to surface product usage patterns and insights that inform content strategy and messaging. (Source: Kristine Segrist, Episode #277)
Recommend periodically pausing regular marketing execution to invest time in building or refining the marketing operating system — centralized repositories, intake processes, templates, goal alignment, and documentation. Frame this to leadership as an investment in future efficiency and team capacity, not as downtime. Use this time to reset processes, onboard new team members, and prepare for upcoming pivots. Evergreen campaigns continue running during this pause. (Source: Jessica Skovira, Ben Person, Episode #210)
Recommend creating and enforcing consistent naming conventions for files, folders, and projects so that any team member can find what they need without asking. This reduces friction, prevents duplicate work, and makes onboarding faster. (Source: Hannak Rankin, Episode #210)
Recommend aligning marketing to product cadence using two-month sprint cycles instead of traditional quarterly planning. This shorter cycle enables faster feedback loops, quicker hypothesis testing, and the ability to pivot messaging and creative based on market response. It also keeps marketing synchronized with how product and engineering operate. (Source: Holly Xiao, Episode #270)
Recommend using Jira's Goals feature to provide quick, asynchronous status updates with a tweet-length summary of what shipped, what's blocked, and what's coming. Link to a note or Confluence page for those who want more context. This keeps status updates out of meetings and allows people to stay informed without requiring synchronous time. (Source: Ashley Faus, Episode #264)
Recommend establishing a dedicated idea board (e.g., in Asana) where team members submit marketing ideas continuously. Once per month, hold a Friday afternoon meeting where everyone pitches their ideas. Select at least two ideas per two-week sprint to test, ensuring ideas are tied to specific KPIs or OKRs. This creates a structured funnel for experimentation without requiring ideas to be perfect before testing. (Source: Adam Goyette, Episode #164)
Recommend structuring the marketing function to run multiple initiatives in parallel rather than sequentially completing one initiative before starting the next. Parallel activity allows the team to simultaneously build brand and attention while building demand. This requires sufficient resources and clear prioritization but enables faster market impact. (Source: Andrew Davies, Episode #195)
When auditing a marketing function's demand generation, recommend prioritizing conversations with the marketing team, sales team, and product leadership to understand gaps and opportunities. Observe how the team is currently operating. Avoid spending weeks analyzing spreadsheets in isolation. This surfaces real operational constraints and team dynamics that data alone cannot reveal. (Source: Andrew Davies, Episode #195)
Recommend consolidating all event management (first-party, third-party, partner events, corporate events) under a single team and treating events as a system designed to accomplish specific outcomes. By centralizing event ownership, users can ensure consistency, measure cumulative impact across the event portfolio, and align events with broader marketing and business objectives. (Source: Gurdeep Dhillon, Episode #203)
Recommend using event management software (like Zuttle) to integrate registration data with Slack, automatically notifying sales reps when high-value prospects register (e.g., deal size over $100k, target accounts). This ensures reps are aware of key attendees before the event and can prepare accordingly. Repeat the process on-site to notify reps when VIP attendees check in. (Source: Stephanie Christensen, Episode #227)
Recommend setting up automated email sequences that trigger immediately when someone requests a demo. The first email should be automated (clearly labeled as such) and include a calendar link to book time directly, rather than waiting for a sales rep to manually respond. This ensures same-day response time and removes friction from the booking process. Follow-up emails can then be personalized by sales. (Source: Natalie Marcotullio, Episode #178)
Recommend connecting Google Ads and Bing Ads accounts to Optio, an AI tool that analyzes account data and makes optimization recommendations: identifying underperforming keywords to add to negative keyword lists, flagging underbidding opportunities, and suggesting headline variations. This automates manual optimization work, freeing time for strategy while ensuring ongoing account health. (Source: Adam Goyette, Episode #164)
Recommend starting CRO with Microsoft Clarity (free, requires only a code snippet) for heatmaps and session recordings, and Hotjar for affordable entry-level behavioral data. Both tools reveal friction points and user behavior without requiring large traffic volumes. Implement CRO as a tangible system with defined pieces and processes, starting with the marketing team before expanding to product. (Source: Haley Carpenter, Episode #282)
Rather than using a specific email volume threshold to decide on a dedicated IP, recommend evaluating how strategically important email is to the business. If email is core to GTM, invest in a dedicated IP to control sender reputation and avoid being impacted by other senders on shared IPs. If a dedicated IP is not affordable, evaluate platforms that rotate IPs across multiple addresses. (Source: Sara McNamara, Episode #256)
Support summary: 1 vs 1 vs 1 — three distinct positions with one supporter each
This is a genuine strategic disagreement about how to architect AI adoption, not a matter of company stage alone.
Position A — Invest in general-purpose AI tools, avoid specialized ones Lindsay O'Brien (Episode #304) recommends investing in general-purpose tools like Claude, ChatGPT, Notebook LM, and Zapier/Clay rather than specialized AI marketing tools. Her reasoning: general tools are more cost-effective, versatile, and can be combined to solve multiple problems. They reduce tool sprawl, keep costs down, and give teams flexibility. Only invest in niche tools when there's a specific, high-impact use case that general tools can't address.
Position B — Require an AI component in every new tool Sara Ajemian (Episode #288) recommends establishing a corporate mandate that any new tool brought on must have an AI component built in. Her reasoning: this ensures the tech stack evolves with AI capabilities and forces intentional procurement decisions, preventing tool sprawl through a gate rather than through minimalism.
Position C — Expect AI to be embedded in existing tools, not procured separately Mychelle Mollot (Episode #182) argues that AI will be embedded as a feature in core marketing tools (Gong, HubSpot, etc.) rather than existing as standalone tools. Her recommendation: shift the evaluation frame from "do we need an AI tool?" to "which tools have the best AI features for our use case?" Separate AI tool procurement is the wrong frame entirely.
Context dependency: Company size and budget may influence whether a mandate approach (Ajemian) or a minimalist general-tools approach (O'Brien) is more practical. However, the core tension — whether to seek AI as a standalone layer, require it in all tools, or expect it embedded in existing tools — is a genuine strategic disagreement about how to architect AI adoption regardless of company stage.
What to do: Present all three positions to the user and help them evaluate which framing fits their current stack maturity, budget, and procurement process. Do not recommend one approach as settled best practice.
Support summary: 3 vs 2 — slight majority favoring expansion, but both camps include credible voices
Position A — Minimize the tool stack
Position B — Actively integrate specialized tools
Context dependency: Minimization advice (Van Den Ingh, Tonkin) may apply more to teams that have accumulated tool sprawl or are resource-constrained. Expansion advice applies to teams with mature operations seeking incremental capability gains. However, both camps are making general recommendations about stack philosophy, not purely stage-specific ones.
What to do: Ask the user about their current stack state (sprawl vs. gaps), budget situation, and operational maturity before recommending a direction. Do not present either position as universally correct.
Support summary: 1 vs 1 — directly opposing answers to the same question
Position A — Eliminate traditional lead scoring Aditya Vempaty (Episode #304) recommends stopping lead scoring based on form fills and engagement actions entirely. His reasoning: lead scoring creates theater — endless internal debates about lead vs. contact definitions — without improving sales outcomes. Only pass contacts to sales when they demonstrate clear buying intent (e.g., demo request, specific engagement threshold). Use AI to set a clear threshold rather than a complex scoring model.
Position B — Replace with a machine learning scoring model Morgan Cole (Episode #315) recommends replacing multiple disparate scoring models with a single machine learning scoring model aligned to the CRM lifecycle object. His reasoning: a single ML model aligned to the CRM lifecycle replaces fragmented scoring approaches; scoring buying committees (not just individuals) and validating against actual conversion rates makes scoring more accurate and actionable.
Context dependency: Vempaty's advice may apply more to teams with immature or over-engineered scoring systems where the model has become bureaucratic overhead. Cole's ML approach assumes sufficient data volume and technical capability to build and validate a model. However, both are addressing the same question — how to qualify leads for sales — and give directly opposing answers.
Trend note: Both episodes are recent (late 2025), so no chronological pattern can be identified.
What to do: Ask the user about their current scoring system's complexity, their data volume, and whether their scoring model is actually correlating with conversion. If they have a broken, over-engineered system with no validation, Vempaty's approach may be more appropriate. If they have sufficient data and technical resources, Cole's ML approach may be more appropriate. Do not recommend one as universally correct.
| Episode | Guest(s) | Date |
|---|---|---|
| Episode #159 | Kris Rudeegraap | 2024-07-18 |
| Episode #161 | Max Van Den Ingh | 2024-07-25 |
| Episode #164 | Adam Goyette | 2024-08-05 |
| Episode #175 | Ruth Zive | 2024-09-12 |
| Episode #178 | Natalie Marcotullio | 2024-09-23 |
| Episode #182 | Mychelle Mollot | 2024-10-07 |
| Episode #187 | Sean Lane | 2024-10-24 |
| Episode #195 | Andrew Davies | 2024-11-21 |
| Episode #197 | Rowan Tonkin | 2024-11-28 |
| Episode #201 | Kym Parker | 2024-12-12 |
| Episode #203 | Gurdeep Dhillon | 2024-12-19 |
| Episode #210 | Jessica Skovira, Ben Person, Hannak Rankin | 2025-01-13 |
| Episode #211 | Chris Walker | 2025-01-16 |
| Episode #227 | Stephanie Christensen | 2025-03-13 |
| Episode #235 | Aditya Vempaty | 2025-04-07 |
| Episode #239 | Pranav Piyush | 2025-04-21 |
| Episode #243 | Anthony Blatner, Tagg Bozied | 2025-05-05 |
| Episode #245 | Alex Fine | 2025-05-12 |
| Episode #250 | Shoshana Kordova | 2025-05-29 |
| Episode #256 | Sara McNamara | 2025-06-19 |
| Episode #264 | Ashley Faus | 2025-07-14 |
| Episode #270 | Holly Xiao | 2025-08-04 |
| Episode #271 | John Short, Dan Guenet, Justin Johnson | 2025-08-07 |
| Episode #274 | Sean Lane | 2025-08-18 |
| Episode #277 | Kristine Segrist | 2025-08-28 |
| Episode #280 | Gurdeep Dhillon | 2025-09-08 |
| Episode #282 | Haley Carpenter | 2025-09-15 |
| Episode #288 | Sara Ajemian, Dave Gerhardt | 2025-10-06 |
| Episode #290 | Dan, Eoin | 2025-10-13 |
| Episode #297 | Kelly Cheng | 2025-10-23 |
| Episode #304 | Aditya Vempaty, Lindsay O'Brien | 2025-11-17 |
| Episode #306 | Sylvia LePoidevin | 2025-11-24 |
| Episode #315 | Jean Cameron, Morgan Cole | 2025-12-25 |
| Episode #319 | Jess Lytle | 2026-01-08 |
| Episode #338 | Davang Shah | 2026-03-17 |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.