feedback-synthesizer
Collects, categorizes, and synthesizes user feedback from multiple channels into actionable product insights. Performs sentiment scoring, theme tagging, NPS/CSAT analysis, feature request ranking, priority matrix generation, and Voice of Customer reporting. Use when the user asks to analyze customer feedback, survey responses, NPS scores, CSAT data, feature requests, app reviews, support tickets, social media mentions, or any Voice of Customer data; or when they need to prioritize a product roadmap from user input, identify top pain points from feedback, understand why users are churning, or produce an executive feedback report.
93
92%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
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 an excellent skill description that thoroughly covers specific capabilities, includes abundant natural trigger terms, explicitly addresses both what and when, and carves out a distinct niche. It uses proper third-person voice throughout and provides enough detail for Claude to confidently select it from a large pool of skills. The description is comprehensive without being padded with fluff.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: sentiment scoring, theme tagging, NPS/CSAT analysis, feature request ranking, priority matrix generation, and Voice of Customer reporting. Very comprehensive enumeration of capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (collects, categorizes, synthesizes feedback; performs sentiment scoring, theme tagging, etc.) and 'when' with an explicit 'Use when...' clause covering numerous trigger scenarios including analyzing feedback, prioritizing roadmaps, identifying pain points, and producing reports. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'customer feedback', 'survey responses', 'NPS scores', 'CSAT data', 'feature requests', 'app reviews', 'support tickets', 'social media mentions', 'Voice of Customer', 'pain points', 'churning', 'product roadmap'. These are all terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Occupies a clear niche around user/customer feedback analysis and Voice of Customer reporting. The specific domain terms (NPS, CSAT, sentiment scoring, churn analysis) make it highly distinguishable from general data analysis or reporting skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%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 a clear 6-step workflow with validation checkpoints, concrete output formats, and appropriate progressive disclosure. Its main weakness is moderate verbosity — some instructions describe processes Claude already understands (PII stripping, deduplication, sentiment labeling), which could be condensed. Overall, it's highly actionable and would guide Claude effectively through feedback synthesis tasks.
Suggestions
Condense Step 2 (Clean & Normalise) — Claude already knows how to deduplicate, strip PII, and assign IDs; reduce to a brief checklist rather than numbered sub-steps with explanations.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is well-structured and mostly efficient, but some sections are somewhat verbose for Claude's capabilities — e.g., the detailed explanation of how to strip PII, standardize sentiment labels, and assign IDs are things Claude already knows how to do. The processing guidelines section adds useful constraints but could be tighter. | 2 / 3 |
Actionability | Each step provides concrete, specific instructions with exact table formats, scoring formulas (Weighted Priority Score = High×3 + Medium×2 + Low×1 / item count), specific theme taxonomies, and a fully structured insight block template. The output format is copy-paste ready and leaves no ambiguity about what to produce. | 3 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced with explicit validation checkpoints at Steps 1, 2, and 3 (warn on small samples, flag duplicate rates, confirm taxonomy before bulk analysis). Feedback loops are present — e.g., asking the user to confirm taxonomy before proceeding, and the iterative mode guideline for re-running Steps 3–6 on combined datasets. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview workflow inline while appropriately deferring the full report template to REPORT_TEMPLATE.md and the worked example to EXAMPLES.md. References are one level deep and clearly signaled with descriptive context about what each file contains. | 3 / 3 |
Total | 11 / 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
- OpenRoster-ai/awesome-agents
- Commit
010799b
- Reviewed
Table of Contents
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