user-research-synthesis
When the user has raw customer interview transcripts, survey responses, support tickets, or other qualitative data and needs to extract actionable insights.
76
71%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/user-research-synthesis/SKILL.mdQuality
Discovery
64%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description does a good job specifying input data types and trigger scenarios but falls short on describing concrete output actions — 'extract actionable insights' is too vague. The 'when' is reasonably well-covered through the opening clause, but the 'what' needs more specificity about the actual operations performed (e.g., theme extraction, sentiment analysis, pattern identification).
Suggestions
Replace 'extract actionable insights' with specific concrete actions like 'identify recurring themes, code qualitative responses, surface sentiment patterns, and generate summary reports'.
Add an explicit 'Use when...' clause to reinforce trigger conditions, e.g., 'Use when the user asks to analyze feedback, synthesize interview notes, or find patterns in qualitative data'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description mentions specific input types (interview transcripts, survey responses, support tickets, qualitative data) but the output action is vague — 'extract actionable insights' doesn't specify concrete actions like coding themes, generating summaries, or identifying sentiment patterns. | 2 / 3 |
Completeness | The description answers 'when' quite well (when the user has raw transcripts, surveys, support tickets, etc.) but the 'what' is weak — 'extract actionable insights' is too vague to clearly communicate what the skill actually does. There is no explicit 'Use when...' clause, though the 'When the user has...' framing partially serves this purpose. | 2 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would actually say: 'customer interview transcripts', 'survey responses', 'support tickets', 'qualitative data', and 'actionable insights'. These cover common variations of how users describe this type of work. | 3 / 3 |
Distinctiveness Conflict Risk | The mention of specific qualitative data types (interview transcripts, survey responses, support tickets) provides some distinctiveness, but 'extract actionable insights' is generic enough to overlap with analytics, summarization, or general data analysis skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, well-structured skill that provides highly actionable guidance with concrete templates, output formats, and realistic examples. Its main weakness is verbosity in the Frameworks & Best Practices section, which includes general research methodology knowledge that Claude likely already possesses. The workflow is clear and well-sequenced, and the examples effectively demonstrate expected outputs.
Suggestions
Trim the Frameworks & Best Practices section by removing general research knowledge Claude already knows (e.g., what JTBD means conceptually, what triangulation is) and keep only the operational directives specific to this workflow.
Consider extracting the detailed Frameworks & Best Practices into a separate RESEARCH-PRACTICES.md file and referencing it from the main skill, keeping only the 2-3 most critical practices inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally well-written but includes some unnecessary explanations that Claude already knows (e.g., explaining what JTBD is, explaining that 'customers hire products to make progress in their lives,' explaining what triangulation means). The Frameworks & Best Practices section contains several items that are general research knowledge rather than specific operational guidance. The content could be tightened by ~30% without losing actionable value. | 2 / 3 |
Actionability | The skill provides highly concrete guidance: specific output formats with table structures, exact sentence templates ('We learned that [finding] which means [implication] so we should [recommendation]'), JTBD framing templates, and two detailed examples showing expected input/output. The workflow steps are specific and instructive rather than vague. | 3 / 3 |
Workflow Clarity | The 8-step workflow is clearly sequenced with logical ordering (read first, then metadata, then analysis, then synthesis). Step 1 explicitly addresses bias prevention by requiring complete reading before summarizing. Step 8 provides a confidence assessment checkpoint. The workflow handles both single-transcript and multi-transcript scenarios with distinct output formats for each. | 3 / 3 |
Progressive Disclosure | The skill references related skills (prd-writing, competitive-analysis, feedback-synthesis) for chaining, which is good. However, the Frameworks & Best Practices section is quite long and could be split into a separate reference file. The content is well-structured with clear headers but is somewhat monolithic at ~120 lines of substantive content, with the best practices section inline rather than referenced. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- shawnpang/startup-founder-skills
- Commit
4ad31b4
- Reviewed
Table of Contents
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