skills/tacit-expertise-elicitation/SKILL.md
Techniques for surfacing implicit, hard-to-articulate knowledge that experts use in practice
npx skillsauth add curiositech/windags-skills tacit-expertise-elicitationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Need to elicit expert knowledge?
├── Expert can articulate their process clearly
│ ├── YES → Suspect rehearsed account
│ │ └── Use situational re-evocation with specific difficult case
│ └── NO → Says "I just know" or "it's intuition"
│ └── Use MELI protocol with four-pass structure
│
├── Time/resource constraints?
│ ├── Single expert, 2-3 hours → MELI interview
│ ├── Multiple experts, short timeline → Cognitive shadowing
│ ├── Cross-cultural context → Community workshop (elicitive approach)
│ └── Long-term institutional knowledge → Cognitive task analysis
│
├── Knowledge transfer context?
│ ├── Within same domain/culture → Direct MELI
│ ├── Cross-domain transfer → Elicitive methodology first
│ ├── Retiring expert → Community-embedded MELI
│ └── Failed previous attempts → Diagnose structural barriers
│
└── Community dynamics?
├── Competitive/political tensions → Use community setting
├── High trust environment → Individual or small group
└── Learning resistance → Focus on specific past moments
During elicitation session:
├── Expert gives smooth narrative → "Let's go back to that specific moment..."
├── Expert says "usually I..." → "In this particular case, what did you do?"
├── Expert rationalizes decision → "What did you notice first?"
├── Expert claims "best practice" → "What made you deviate from that?"
└── Expert shows uncertainty → "That uncertainty - what was driving it?"
Detection: Expert provides smooth, logical narrative with clear problem→analysis→solution arc Symptoms: No hesitation, no uncertainty mentioned, perfect hindsight clarity Fix: Interrupt with "Let's pause at that moment when you first noticed X. What exactly did you see?"
Detection: You're asking experts to explain their thinking while performing Symptoms: Stilted performance, rationalized explanations, changed decision patterns Fix: Use post-hoc structured retrospective with sensory detail reconstruction
Detection: Selecting interviewees based on titles, certifications, or years of experience Symptoms: Knowledge that doesn't transfer, generic advice, inability to handle edge cases Fix: Look for evidence of deliberate practice and demonstrated adaptive expertise
Detection: Extracting "universal principles" that should work everywhere Symptoms: Knowledge fails when applied in different contexts, local experts feel devalued Fix: Embed elicitation within target community, surface local knowledge first
Detection: Formal CPD/learning settings producing curated success stories only Symptoms: Repeated organizational failures, knowledge hoarding, surface-level sharing Fix: Create psychologically safe structured protocols that make curation difficult
Setup: Law firm's top mediator retiring, needs to transfer 20 years of expertise to junior staff.
Novice approach would be: "Tell us your best practices" → Gets rehearsed principles about "active listening" and "finding common ground"
Expert elicitation process:
Key insight captured: Expert wasn't following "separate parties when tensions are high" - they were reading micro-signals of performative vs. genuine negotiation stance.
Setup: Manufacturing company's quality control training keeps failing - new inspectors miss defects that experienced ones catch easily.
Previous failed approach: Experienced inspectors created checklist of "what to look for" → New inspectors followed checklist but still missed critical issues
Elicitation process:
Outcome: Training redesigned around pattern recognition rather than sequential checking.
Setup: International development organization wants to apply successful conflict resolution methods from Northern Ireland to post-conflict Africa.
Standard approach would be: Export the Northern Ireland framework directly → Predictable failure due to context mismatch
Elicitive approach:
Key principle: Local expertise gets elevated to equal status with imported expertise.
Knowledge elicitation session is complete when:
This skill should NOT be used for:
General interviewing → Use standard interview techniques instead
Performance evaluation → For assessment of individual performance, use performance management frameworks instead
Academic research → For theoretical knowledge generation, use research methodology instead
Crisis decision support → For real-time expert assistance, use decision support systems instead
Mediation practice itself → For conducting actual mediations, use conflict resolution skills instead
Organizational change management → For systemic organizational change, use change management frameworks instead
When expert knowledge is already well-documented → For codified knowledge transfer, use training design instead
When context cannot be preserved → For knowledge that must work across radically different contexts, use abstraction and generalization methods instead
This skill specifically targets the gap between what experts can say they do and what they actually do in practice - the tacit, context-dependent knowledge that resists explicit articulation but is essential for competent performance.
data-ai
license: Apache-2.0 NOT for unrelated tasks outside this domain.
development
Use when designing caching strategies (cache-aside, write-through, write-behind), implementing distributed locks, building rate limiters, leaderboards, real-time streams (XADD/consumer groups), pub/sub, or tuning eviction policies. Triggers: thundering-herd on cache miss, dogpile on key expiry, Redlock vs SET-NX-PX choice, sliding-window rate limiter, hot-key on a single cluster slot, big-key blowup, MULTI/EXEC across slots, KEYS in production. NOT for Redis Cluster operations/admin (different domain), embedded KV (SQLite, leveldb), in-process LRU caches, or Memcached.
tools
Drawing the `'use client'` boundary correctly in React Server Components apps (Next.js App Router, RSC frameworks) — leaf-pushing, slot composition, serialization rules, and environment poisoning prevention. Grounded in react.dev and Next.js 16 docs.
development
Use when designing rate limiting for an API, choosing between token bucket / sliding window / leaky bucket / fixed window, implementing it in Redis, deciding edge (Cloudflare/Upstash) vs origin enforcement, sizing per-user vs per-IP vs per-endpoint quotas, returning the right 429 response with Retry-After, or fixing the boundary-burst bug in fixed-window limiters. Triggers: 429 too many requests, INCR + EXPIRE, ZADD + ZREMRANGEBYSCORE + ZCARD, X-RateLimit-Remaining header, Cloudflare WAF rate limiting rules, Upstash @upstash/ratelimit, leaky bucket shaping vs policing, distributed rate limiter consistency. NOT for DDoS mitigation specifically (different scale), CAPTCHA / bot management, full WAF design, or per-user quota billing.