data-storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
76
47%
Does it follow best practices?
Impact
95%
1.06xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/business-analytics/skills/data-storytelling/SKILL.mdQuality
Discovery
67%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 has a solid structure with an explicit 'Use when' clause that clearly communicates both purpose and triggers. However, the capabilities described are somewhat abstract ('compelling narratives', 'persuasive structure') rather than listing concrete actions, and the trigger terms could be broader to capture more natural user language. The skill's niche overlaps with several adjacent domains like data visualization, reporting, and presentation creation.
Suggestions
Replace abstract phrases like 'compelling narratives' and 'persuasive structure' with concrete actions such as 'generate annotated charts, write narrative summaries, structure key takeaways'.
Expand trigger terms to include common user language like 'dashboard', 'charts', 'graphs', 'metrics', 'KPIs', 'insights', or 'data summary'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (data storytelling) and some actions ('visualization, context, and persuasive structure'), but these are somewhat abstract rather than concrete specific actions like 'create charts, write narrative summaries, build slide decks'. | 2 / 3 |
Completeness | Clearly answers both 'what' (transform data into compelling narratives using visualization, context, and persuasive structure) and 'when' (presenting analytics to stakeholders, creating data reports, building executive presentations) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes some relevant terms like 'analytics', 'stakeholders', 'data reports', 'executive presentations', but misses common variations users might say such as 'dashboard', 'charts', 'graphs', 'insights', 'KPIs', 'metrics', or 'data summary'. | 2 / 3 |
Distinctiveness Conflict Risk | Could overlap with general data visualization skills, report generation skills, or presentation creation skills. The 'data storytelling' angle provides some distinction, but 'data reports' and 'executive presentations' are broad enough to conflict with other skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a comprehensive blog post or training manual on data storytelling than a concise, actionable skill file for Claude. It contains extensive generic advice (transition phrases, do's/don'ts, narrative arc concepts) that Claude already knows, while lacking specific executable workflows and validation steps. The content would benefit enormously from aggressive trimming and splitting into referenced sub-files.
Suggestions
Cut the content by 60-70%: remove generic storytelling concepts (narrative arc, three pillars, transition phrases, do's/don'ts) that Claude already knows, and focus only on the specific frameworks and templates that add unique value.
Split into referenced files: keep a concise overview in SKILL.md with links to FRAMEWORKS.md, TEMPLATES.md, and VISUALIZATION.md for the detailed examples.
Add validation checkpoints: include steps like 'verify data accuracy before presenting', 'cross-check calculations', and 'validate chart labels match underlying data' to prevent errors in stakeholder-facing deliverables.
Make the skill more actionable by providing specific instructions on what tools to use (e.g., matplotlib, plotly) with complete executable code examples, rather than ASCII art mockups of slides.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~300+ lines. Much of the content is generic storytelling advice, transition phrases, and do's/don'ts that Claude already knows. The frameworks are illustrative but repetitive—three full story frameworks with similar structures. Concepts like 'Setup → Conflict → Resolution' and 'rule of three' are well-known to Claude and waste tokens. | 1 / 3 |
Actionability | The frameworks provide concrete markdown templates and one executable Python matplotlib example, which is useful. However, most content is descriptive markdown templates and ASCII art mockups rather than executable code or specific tool commands. The skill describes what good data stories look like rather than giving Claude precise instructions on how to produce them. | 2 / 3 |
Workflow Clarity | The presentation templates (Template 2: Data Story Flow) provide a clear slide-by-slide sequence, and the frameworks show a logical progression. However, there are no validation checkpoints—no steps for verifying data accuracy, checking visualization correctness, or getting stakeholder feedback before finalizing. For a skill involving data presentation to executives, some verification steps would be important. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of text with no references to external files. All content—frameworks, visualization techniques, templates, writing techniques, best practices—is inlined in a single massive document. Much of this could be split into separate reference files (e.g., FRAMEWORKS.md, TEMPLATES.md, VISUALIZATION.md) with the main skill providing a concise overview. | 1 / 3 |
Total | 6 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- wshobson/agents
- Commit
112197c
- Reviewed
Table of Contents
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