Skill Validation Techniques

A mistake found in most student resumes: A skills section packed with soft skills — but no proof you've actually used them. Here are some of the usual suspects: • Teamwork • Leadership • Communication All good skills, and many roles ask for them. But here’s the problem: anyone can claim them. Without clear evidence of how you’ve applied them (and the impact they had) they won’t help you stand out. Generally speaking, your skills section should focus on hard, verifiable skills: • Technical tools (e.g., Python, Adobe Illustrator) • Certifications (e.g., Excel Certification) • Languages (e.g., Spanish Fluency) And even then, those skills should appear in your bullet points — with context and outcomes. If the skills section is the only place where they’re mentioned, you’re expecting the recruiter to blindly believe you actually have them. Don’t do that. Give them proof. Here’s how: • Choose the skill(s) you want to highlight • Identify the experience(s) where you've used them • Show how you used the skill to create positive results Let's give you a couple of examples: Instead of simply listing "Teamwork" in your skills section, craft a bullet that showcases how you've used that skill: • Revised the chapter’s student engagement plan in partnership with the chapter president, faculty advisor, and events chair, resulting in... Instead of simply listing "Excel" in your skills section, craft a bullet that showcases how you've used that skill: • Conducted investment analysis using Excel by compiling data on historical returns and risk metrics, creating charts and pivot tables to compare asset performance to... And so on. Bottom line: If these skills only appear in your skills section, you leave the recruiter guessing if you actually have the skills or if you've simply included them for keyword alignment. You don't want to leave them guessing. You want to show exactly how and where you've used your skills and to what end. Skills without context create doubt. Skills with context build credibility.

Catalina S. told me something that completely reframes how we should think about skills validation. After 10+ years leading workforce transformation at Vodafone, T-Mobile, and DataCamp, she dropped this truth bomb during our latest Business AI Playbook episode: "Companies don't just want employees to know things, they want employees who can do things." Most L&D teams are still stuck measuring completion rates and quiz scores. But Catalina's seeing something different work: Evidence-based skill validation that proves real-world capability. Here's what she's implementing right now: → AI-powered surgical feedback — Johns Hopkins is using AI to analyze actual surgical videos, providing objective feedback on technique and precision, not just theoretical knowledge → Peer-led GenAI Scouts — A global engineering org turned employees into instructional designers, achieving 90% engagement and 20-40% time savings on repetitive tasks in just 6 months → Real-world retail simulations — AI roleplay environments where new hires practice customer interactions, earning badges only after demonstrating 3 successful and 3 unsuccessful scenarios with lessons learned → Skills data as strategic inventory — Finally giving companies visibility into their actual internal capabilities while supporting employee growth aspirations Catalina's challenge to every L&D leader: "We need to shift from knowledge retention to evidence-based skill validation." The companies getting this right aren't just improving training metrics. They're fundamentally changing how their workforce approaches capability development. 🎥 Watch the full conversation below 🔄 Share this if you think proving skills matters more than passing tests What's the most creative approach you've seen to validate real-world skills? #BusinessAIPlaybook #LearningInnovation #SkillsValidation #AITransformation #FutureOfWork

Yuhao Wu, Tung-Ling (Tony) Li and myself just published "Behavioral Integrity Verification for AI Agent Skills" https://lnkd.in/gfgNhWUj : how can an agent trust a skill before acquiring it? When an agent picks up new skills on the fly — say OpenClaw, Hermes Agents — it mostly trusts the skill's description to decide whether to use it. The code, the metadata, the embedded instructions in SKILL.md — the agent rarely looks past the surface. So our research question became a simple one: what a skill actually does versus what it claims to do — how big is that gap, and does it matter? To find out, we pulled down ~50k third-party skills off a public repo and ran the comparison. The answer was 80% of the time, the gap is real — and the bigger surprise was that almost all of it is sloppy documentation, not malware. Of the deviations we could classify, 81.1% trace to developer oversight and only 18.9% to adversarial intent. That reframed the whole project for us. Most agent-safety work asks "is this skill malicious?" — a yes-or-no classifier on a known threat. We got more interested in measuring the gap itself, because once you have a typed deviation between declared and actual behavior, downstream questions like "is it malicious?", "is it leaking credentials?", or "did the developer just forget to document something?" all become consumers of the same evidence. So we built BIV. The fun engineering part is that it is genuinely a system, not a prompt chain — deterministic code analyzers (AST traversal, inter-procedural taint tracing, regex over JS/TS and shell) run alongside LLM extractors for the natural-language parts of a skill, and both sides project into a shared 29-capability vocabulary. After that, behavioral integrity is just set arithmetic. Boring on purpose, and reusable. The empirical side is the part we are most excited to share. We validated the extraction pipeline against a three-model judge panel (Claude Opus 4.6, Gemini 3 Pro, GPT-OSS 120B), and we ablated the downstream malicious-skill detector across five LLM backends spanning Anthropic and Google. The ~250k deviations we surfaced clustered into 137 categories, four of them novel compound-threat motifs (Exfiltration Chains, RCE Chains, Code Obfuscation, Data Lineage Violations) that no single-capability scanner can see. Plugging the structured evidence into an LLM judge with a relaxed-veto override hit F1 0.946 on a ~900-skill malicious-skill benchmark, against 0.93 for a single-pass LLM auditor and 0.44 for a rule-based scanner. We would love to hear what you think.

FEEDBACK NEEDED: What Data Should Be Included in Open Badge 3.0 to Support Verification and Validation of Skills? As employers, educators, and workforce systems increasingly shift toward skills-based hiring and advancement, Open Badge 3.0 (OBv3) provides a vital standard for issuing verifiable, portable, and machine-readable digital credentials. To ensure badges support trustworthy validation of skills, the following data elements are essential: ✅ Core Metadata for Identity and Trust Issuer Identity: Verified organizational metadata (e.g., legal name, credential registry ID, website) to authenticate source. Recipient Identity: Cryptographically linked (not publicly exposed) identifier ensuring badge belongs to the verified individual. Issue and Expiry Dates: Timestamped evidence of when the badge was earned and if/when it expires. 🛠 Skill Evidence and Validation Competency Frameworks: Align the badge to recognized skill/competency frameworks (e.g., ESCO, O*NET, Credential Engine). Assessment Description: Clear articulation of how skills were evaluated—exam, performance, portfolio, etc.—and by whom. Demonstration Evidence: Link to artifacts or media (e.g., project, video, rubric) showing real-world skill application. Level of Proficiency: Indicate depth of mastery using taxonomies like Bloom’s or CEFR (if applicable). 🔗 Transparency and Interoperability Credential Registry Links: Direct connection to authoritative registries like the Credential Engine for transparency, comparability, and validation. Metadata Standards: Conform to schema.org, JSON-LD, and IMS Global/1EdTech standards for machine readability and system integration. Verifiable Claims: Use cryptographic signatures and tamper-proof digital wallets to ensure authenticity. 📊 Learner Context and Use Related Pathways: Reference how the skill connects to education, career, or industry pathways. Alignment to Job Roles: Include job role tags (e.g., from O*NET or SOC codes) where skill is commonly applied. Endorsements: Validation from third-party employers or industry groups strengthens badge credibility. --- Summary: To make Open Badge 3.0 a trusted mechanism for verifying and validating skills, it must include structured, transparent, and portable data—who issued it, what it represents, how it was earned, and how it connects to real work. This is essential in the age of AI-driven hiring and skills-based opportunity.

I have taken many DS/ML interviews 6 areas AI/ML engineers mess up in interviews🙂 Nowadays, companies test practical problem-solving with real scenarios, like below: 1. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 "A RAG system is accurate but slow. Users are complaining." What separates strong candidates: → Ask about current performance metrics first → Investigate bottlenecks systematically (embedding, retrieval, generation) → Propose A/B testing for trade-offs → Consider caching strategies before model changes 2. 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗠𝘆𝘀𝘁𝗲𝗿𝘆 "Model performs well weekdays, poorly weekends." Good responses identify: → Different user behavior patterns → Upstream data pipeline changes → Training data bias toward weekday samples → Infrastructure load variations 3. 𝗧𝗵𝗲 𝗕𝘂𝗱𝗴𝗲𝘁 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 "Design a recommendation system with $10K monthly budget." Smart approaches include: → Start with simple collaborative filtering (if possible) → Use pre-trained embeddings before custom training → Implement incremental scaling based on user growth → Plan for hybrid online/offline architecture 4. 𝗧𝗵𝗲 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 "Production accuracy drops from 95% to 60% overnight." Effective investigation order: 1. Check data pipeline health 2. Verify monitoring isn't broken 3. Compare input distributions 4. Look for upstream service changes 5. Prepare rollback before deep-diving 5. 𝗧𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗗𝗲𝗲𝗽-𝗗𝗶𝘃𝗲 "Build real-time customer support ticket summarization." Strong answers address: → Input validation for different ticket formats → Quality control mechanisms (confidence thresholds, human review) → Fallback strategies when LLM services fail → Feedback loops for continuous improvement → Success metrics beyond accuracy 6. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗲-𝗼𝗳𝗳 𝗧𝗿𝗶𝗮𝗻𝗴𝗹𝗲 "Fast, cheap, accurate - pick two." Impressive responses: → Frame decision around business impact → Provide concrete examples for different use cases → Discuss how to redefine requirements → Explain stakeholder communication strategy What actually gets candidates hired: ✅ Systematic thinking over encyclopedic knowledge. ✅ Asking clarifying questions before solving. ✅ Acknowledging unknowns and explaining learning approach. ✅ Discussing trade-offs and constraints upfront. The golden answer pattern: "Based on the constraints, here's my approach... - First, I'd validate assumptions by... - Then I'd implement a minimal solution to... - Finally, I'd iterate based on metrics like..." Practice framework: → Pick any ML system (Netflix recommendations, Search, RAG Chatbot) → Document how to improve one aspect → Include success metrics, constraints, and trade-offs → Present solution as systematic process Hope this helps! -- ♻️ Repost if you find it helpful! ➕ We often this discuss on these topics here 👇 ➕Join 35.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

Recruiters! If you’re serious about skills-based hiring, you need a stage in your process that actually validates the skills.👇 - Not a vibe. - Not a great anecdote. - Not a 15-minute chat where they say all the right things. Real skills. In context. Here’s how to build that into your hiring flow: 1️⃣ Create a Skills Stage for Every Role - Devs? Give them a code problem that reflects your stack. - CS? Have them reply to a tricky customer scenario. - Marketers? Ask for a teardown or content plan. Doesn’t matter if it’s engineering, marketing, or ops. Give them something that mirrors the actual work. 2️⃣ Use a Rubric - If you’re scoring off gut feel, you’re not running a skills-based process. - Set clear criteria for what “good” looks like. - Align with the team before the first interview, not after the sixth. - Same inputs, same standard = less bias, better decisions. 3️⃣ Score First, Talk Second - Get feedback submitted before the debrief. - No discussion, no “what did you think?” - Just: score it based on the rubric, submit, then meet. This keeps decisions focused on evidence, not opinion. The goal of a skills-based stage is to give the right candidates a chance to shine, even if they didn’t go to the right uni or haven’t done your exact job before. You don’t need more top-of-funnel noise, you need better signal in the middle of your process. Skills-based hiring is how you get there. What’s working for you when it comes to testing skills fairly and efficiently? Tell me below 👇 ------------------------------------------------------------------------- Hi 👋 I’m Luke. I empower recruiters with data. Want to get data-driven for free? Link in my profile for my free weekly newsletter. #recruitment #recruiting #recruiters #talentacquisition

Everybody is talking about the skills-based organization, but how do you create it, use skills in everything from hiring to people development, from workforce planning to pay, and from talent mobility to strategic insights? We know that companies struggle, with only 1 in 5 effectively using skills for recruiting decisions, 12% for career development, and 8% for pay decisions. One of the problems companies face: skills validation so we can trust the insights and be confident in skills-based decisions. I had the opportunity to discuss with Jim Hemgen, Director of Talent Development of Booz Allen Hamilton on our latest The Josh Bersin Company WhatWorks podcast about this topic. In a highly competitive labor market, Jim and his team faced issues in employee career development and retention. About 60% of employees searching for internal opportunities may leave if they do not find suitable roles. This prompted Booz Allen to develop a skills-based ecosystem to enable internal career moves, reduce attrition, and at the same time build skills for the future across hundreds of domains including AI. Jim explains the key areas his work focused on: - Skills validation and badging: Booz Allen introduced a skills validation and badging system to assess and recognize employee skills through proficiency levels such as foundational, practitioner, and expert. Built on Workera, validated skills enhance the trust and credibility of the talent marketplace. - AI readiness: Initiatives to make employees AI-ready are part of the strategy. Booz Allen focuses on preparing its workforce to integrate AI in their roles, which is critical for organizational success - supporting their journey to become superworkers. - Continuous improvement and collaboration: To foster a continuous improvement approach, Jim and his team engage with industry peers and tech partners to refine their strategies and ensure alignment with future needs. - Engaging managers and employees: To encourage learning and focus on the most important new skills, business ownership, storytelling, and rewards contribute to the company’s success in ongoing skills development. The result is a highly trusted, validated skills system that the company can use for strategic workforce planning and that supports employees in their career journey. Today more than one third of Booz Allen staff have completed validated credentials for their career. Listen in to learn more about Jim's journey and how they solved key business problems with a skills-based approach. Let us know what you think. How are you advancing on your company's skills journey and where do you see AI skill building fit in? #skillsbasedorganization #skillsvalidation #AIdevelopment #superworkers Josh Bersin Stella Ioannidou Veronica Dinis https://lnkd.in/g4XsuBJX

Skill Assessment: The Game-Changing 4-Day Blueprint Most teams are playing Career Roulette. Not You. No guessing. No assumptions. Just clarity and action. (Note: If you have not DEFINED the Skills to be Assessed, Start there. - check yesterday’s post for guidance.) Here is the 4-Step playbook. To map Your team's capabilities - Fast! 𝗦𝘁𝗲𝗽 1: 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 (Day 1) Don’t overcomplicate it. Speed + Simplicity = Results. Tap into these 3 feedback channels: • Self-Assessment: What do they believe they are great at? • 360 / Peer Review: What do peers see that they don’t? • Leadership Evaluation: What do you see from the top? Tip: Use a simple 1-5 rating system. No overthinking. Example scorecard for each role: - Technical Proficiency - Customer Service Care - Problem-Solving Speed - Collaborative Potential 𝗦𝘁𝗲𝗽 2: 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 - 𝗣𝗹𝗮𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (Day 2) Before you collect feedback, lock in these critical details: - Objective: Why are we doing this? - Metrics: What skills are we actually measuring? - Timeline: When will it start and finish? - Analysis: How will we interpret the results? - Next Steps: What will we do with the data? This step prevents confusion and creates alignment. Skipping this step may end up with data overload and no direction. 𝗦𝘁𝗲𝗽 3: 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 - 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝘁𝗵𝗲 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 (Day 3) Data only works if people are honest. Here’s how you get it: - Anonymize it: People are more honest this way. - Ensure Psychological Safety: No fear of being punished for honesty. - Train Assessors: Consistent evaluation beats biased judgment. With this approach, You will get truth instead of sugar-coated feedback. 𝗦𝘁𝗲𝗽 4: 𝗦𝗸𝗶𝗹𝗹 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵 & 𝗚𝗮𝗽 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (Day 4) The data is in. Now, take action. Here’s how you do it fast: - Identify Top 3 Skill Strengths & Gaps - Align Skills to Business Goals: Results start here. - Develop an Improvement Plan (more on this tomorrow) This is where good teams become great. You are not just collecting data You are building a team of peak performers. No Team? This blueprint works for personal development too. Which skill is most critical for your team to assess right now? P.S. I just ran this process with a team and found our top development need is Marketing. What is Yours?

Skill testing: I'm a recent convert to the value of 3rd party skill testing. Its a data driven process to assist in hiring qualified candidates. Also, a great tool to provide skill assessments and identify developmental opportunities for an existing team. I am really pleased with Ramsay's online testing tools, I'll admit to some skepticism in the beginning, however the results have won me over. Ramsay provides options for testing skills for numerous maintenance categories, such as an Electrician, Automation tech, Mechanic, Planner, Leadership, Maintenance manager, and more. Each of these tests provide a numerical breakdown of skill mastery for a particular role, each with a granular breakdown of skill related categories. Ramay's online Dashboard is used for comparing candidate scores to a national, and a local standard, then providing charts and graphs to chart and compare results while highlighting and grouping developmental opportunities. I also built an Excel tool to scale the scores against the tested role's actual pay band (25th to 75th percentile pay range). Intended to assist in identifying a fair wage based on actual skill. If desired, I can individually weight each category to place added emphasis on say, Hydraulics vs. Machine shop. With an existing team, a pay per skill wage check could also be conducted to validate a raise, or enable the creation of a development plan to close any identified gaps. The goal is to ignore opinions and perceptions, and instead focus on a scorable metric. Last, it's a great tool to evaluate direct reports, to determine if their current salary falls into the correct spot of their pay band. If someone is underpaid, you can develop a metric driven stratgy to address at mid-year reviews. Please remember, year end/bonus time is designed to celebrate and reward performance, to motivate and retain talent, not to ballance pay deficincies across the team. As a best practice, always try to correct pay disparity at mid year reviews whenever possible. Third party testing can be a valuable tool to help create a justifiable business case to support this. Thanks, Shorb

I created 15 Claude Cowork skills for Tenex in the last 24 hours. One thing I was quickly reminded of: Claude Code skills and Claude Cowork skills are packaged differently. Claude Cowork skills are packaged as .skill files, essentially zip files. Inside each package, I standardized the structure: <Skill Name>/ ├── SKILL.md ├── README.md ├── reference/  │  ├── gotchas.md  │  ├── <connector>-fields.md  │  └── examples/  │    └── *.md └── scripts/ I also kept a readable SKILL.md outside the zipped .skill file. That made the skill understandable to both the AI system and the humans reviewing it, while making GitHub review and diff management much cleaner. Instead of only dealing with a compressed .skill file, we could clearly see what changed across versions. The goal was not just to create useful skills. It was to create an approved internal system for building, validating, updating, packaging, and distributing Claude Cowork skills across the organization. So I built three internal Claude Cowork skills to manage the process: 1. Create Skill Turns a team member’s Notion doc or workflow notes into an org-standard Claude Cowork skill. 2. Validate Skill Checks the skill, required connectors, permissions, and runs a test use case to ensure it works as intended. 3. Update Skill Applies revisions with version history, approval patterns, and clean change tracking. The biggest surprise: connectors are where a lot of the leverage is. In the last 24 hours, I connected into 10+ systems, including QuickBooks, DocuSign, Square, PayPal, and others. Some connectors are surprisingly deep. Others are still limited. Some have rate limits. Some do not expose the workflows teams actually need yet. And some older platforms were clearly not built for this AI-native layer of work. That is the shift more people need to see coming: It will not be enough for software companies to say, “We have an API.” The real question will be: Can an AI agent safely, reliably, and deeply work inside your system on behalf of a user or organization? If the answer is no, that platform is going to feel increasingly outdated. The future of work is not just people using AI tools. It is organizations building approved, reusable, versioned workflows that let AI operate safely inside the systems where work already happens. Skills are one layer. Connectors are another. Governance, validation, versioning, and updates are what make it real at scale. Production-grade AI connectivity is going to become one of the deciding factors in a company’s success or failure over the next few years. If you’re building software, make sure you’re on the right side of this movement. And if you’re building skills: keep going. Even as someone who builds skills every day, I was surprised by how useful they become when paired with deep connector integrations into the tools where work actually happens. Share this with someone who is building Claude skills for their org! 💙