research-synthesis

Synthesize user research into themes, insights, and recommendations. Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps.

Quality

68%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./design/skills/research-synthesis/SKILL.md

Quality

Content

37%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill provides a well-structured output template that gives Claude a clear picture of the desired deliverable, and the connector integration suggestions are useful. However, it critically lacks a synthesis workflow — there are no steps for how to actually analyze raw research data into themes and insights, which is the core intellectual work of the skill. The content reads more like an output specification than an actionable skill.

Suggestions

Add a step-by-step synthesis workflow (e.g., 1. Read all data, 2. Code observations, 3. Cluster into themes, 4. Validate theme prevalence, 5. Generate insights, 6. Prioritize recommendations) with explicit validation checkpoints.

Remove the 'Tips' section or fold its content into the workflow steps — Claude already understands the difference between observations and interpretations.

Add a concrete worked example showing how raw input data (e.g., 2-3 short interview excerpts) maps to a completed theme in the output template.

Trim the 'What I Accept' list to a single sentence since Claude can infer valid input types from context.

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary sections like 'What I Accept' which is a list Claude could infer, and the Tips section explains basic research methodology concepts (observations vs interpretations) that Claude already knows. The output template is detailed but justified as a concrete deliverable format.	2 / 3
Actionability	The skill provides a detailed output template which is concrete and useful, but lacks executable steps for how to actually perform the synthesis — there's no process for coding transcripts, identifying themes, or resolving conflicting data. It describes what the output should look like but not how to get there from raw inputs.	2 / 3
Workflow Clarity	There is no multi-step workflow defined at all. The skill jumps from 'here are inputs' to 'here is the output format' with no sequenced process for how to analyze the data — no steps for reading transcripts, coding data, clustering themes, validating patterns, or iterating. For a synthesis task that inherently involves multiple analytical steps, this is a significant gap.	1 / 3
Progressive Disclosure	The skill references CONNECTORS.md and a 'user-research' skill for related content, which shows some progressive disclosure. However, there are no bundle files to support the references, and the main content could benefit from splitting the detailed output template into a separate reference file while keeping a concise overview in the SKILL.md.	2 / 3
	Total	7 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description that clearly articulates what the skill does (synthesize user research into actionable outputs), when to use it (with specific input types like interview transcripts and survey results), and uses natural trigger terms that users would actually say. The only minor note is the use of second person 'you have' in the trigger clause, but this is within the 'Use when' framing which is a standard pattern from the good examples.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'synthesize user research into themes, insights, and recommendations' and further specifies 'distilled into patterns, user segments, and prioritized next steps.' These are concrete, actionable outputs.	3 / 3
Completeness	Clearly answers both what ('Synthesize user research into themes, insights, and recommendations') and when ('Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps').	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'interview transcripts', 'survey results', 'usability test notes', 'support tickets', 'NPS responses', 'user research', 'themes', 'insights', 'user segments'. These are terms a user would naturally use when seeking this kind of analysis.	3 / 3
Distinctiveness Conflict Risk	Clearly occupies a distinct niche of user research synthesis with specific input types (interview transcripts, survey results, usability test notes, support tickets, NPS responses) and specific outputs (themes, patterns, user segments, prioritized next steps). Unlikely to conflict with generic data analysis or other skills.	3 / 3
	Total	12 / 12 Passed

Validation

90%

Warnings & errors only

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: anthropics/knowledge-work-plugins
Commit: fb45dfd

Reviewed: about 7 hours ago

Table of Contents

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