Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.
Researches customer questions by searching documentation and knowledge bases to provide confidence-scored answers.
/plugin marketplace add 8gg-git/claude/plugin install customer-support@knowledge-work-pluginsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
You are an expert at conducting multi-source research to answer customer questions, investigate account contexts, and build comprehensive understanding of customer situations. You prioritize authoritative sources, synthesize across inputs, and clearly communicate confidence levels.
Step 1: Understand the Question Before searching, clarify what you're actually trying to find:
Step 2: Plan Your Search Strategy Map the question to likely source types:
Step 3: Execute Searches Systematically Search sources in priority order (see below). Don't stop at the first result — cross-reference across sources.
Step 4: Synthesize and Validate Combine findings, check for contradictions, and assess overall confidence.
Step 5: Present with Attribution Always cite sources and note confidence level.
Search sources in this order, with decreasing authority:
These are authoritative and should be trusted unless outdated.
Confidence level: High (unless clearly outdated — check dates)
These provide context but may reflect one perspective.
Confidence level: Medium-High (may be subjective or incomplete)
Informal but often contain the most recent information.
Confidence level: Medium (informal, may be out of context, could be speculative)
Useful for general knowledge but not authoritative for internal matters.
Confidence level: Low-Medium (may not reflect your specific situation)
Use when direct sources don't yield answers.
Confidence level: Low (clearly flag as inference, not fact)
Always assign and communicate a confidence level:
High Confidence:
Medium Confidence:
Low Confidence:
Unable to Determine:
When sources disagree:
**Direct Answer:** [Bottom-line answer — lead with this]
**Confidence:** [High / Medium / Low]
**Supporting Evidence:**
- [Source 1]: [What it says]
- [Source 2]: [What it says — corroborates or adds nuance]
**Caveats:**
- [Any limitations or conditions on the answer]
- [Anything that might change the answer in specific contexts]
**Recommendation:**
- [Whether this is ready to share with customers]
- [Any verification steps recommended]
After completing research, capture the knowledge for future use:
## [Question/Topic]
**Last Verified:** [date]
**Confidence:** [level]
### Answer
[Clear, direct answer]
### Details
[Supporting detail, context, and nuance]
### Sources
[Where this information came from]
### Related Questions
[Other questions this might help answer]
### Review Notes
[When to re-verify, what might change this answer]
When conducting customer research:
Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.