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data-storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

Quality

57%

Does it follow best practices?

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/data-storytelling/SKILL.md

The canonical home for this skill is data-storytelling in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

53%Weight 40%Scale 1-5

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

The body is a well-organized library of concrete, usable storytelling frameworks, templates, and visualization techniques, held back by generic instructions with no validation checkpoints, several sections restating knowledge Claude already has, and completely failed progressive disclosure — everything is inlined and the lone reference points to a nonexistent file. Fixing the dangling resources/implementation-playbook.md reference (or splitting the frameworks/templates into real reference files) is the highest-leverage improvement.

Suggestions

Fix progressive disclosure: either create resources/implementation-playbook.md (and align the directory name with the skill's bundle layout) or split the Story Frameworks and Presentation Templates into reference files under references/, keeping SKILL.md as a concise overview with clearly signaled one-level-deep links.

Tighten conciseness by cutting the 'Transition Phrases', 'Handling Uncertainty', and Do's/Don'ts sections or condensing them to one-line reminders — Claude already knows standard transition language, confidence intervals, and 'avoid jargon'.

Strengthen workflow clarity: replace the generic Instructions with a concrete sequence (identify audience → pick a framework → draft headline-first → verify each claim is backed by a number in the story) that includes an explicit validation checkpoint before delivery.

DimensionReasoningScore

Conciseness

The three worked Story Frameworks, Visualization Techniques, and Presentation Templates are curated house-style exemplars that earn their tokens, but several sections restate what Claude already knows: 'Transition Phrases' ("The data reveals..."), 'Handling Uncertainty' (confidence intervals, ranges), the generic Do's/Don'ts ('don't use jargon', 'don't data dump'), and the basic Setup→Conflict→Resolution / narrative-arc concepts. This fits 'Mostly efficient but includes some unnecessary explanation or could be tightened'. Not a 2 because there is no library/tutorial padding or over-explanation of concepts, and the bulk is a usable example library; not a 4 because the known-material sections constitute a noticeable trim opportunity.

3 / 5

Actionability

The body delivers three complete worked story templates (churn, Q4 trend, market comparison), copy-paste-adaptable slide/dashboard templates, concrete headline formulas with BAD/GOOD pairs, and a runnable matplotlib annotation pattern (ax.annotate, axvspan, axhline). This matches 'Mostly executable guidance; concrete code or commands with minor gaps'. Not a 5 because the matplotlib snippet references undefined variables (dates, revenue, launch_date) without sample data, and the 'Instructions' section is abstract ('Apply relevant best practices and validate outcomes') rather than executable.

4 / 5

Workflow Clarity

The six-step Narrative Arc and the template slide flows (headline → context → discovery → recommendation → ask) give a rough, usable sequence, but the dedicated 'Instructions' section is generic boilerplate ('Clarify goals, constraints, and required inputs', 'validate outcomes') with no concrete checkpoints, no guidance on choosing between the three frameworks, and no feedback loop for verifying a draft against audience needs. This matches 'Steps listed but validation gaps; sequence present but checkpoints missing or implicit'. Not a 4 because no explicit checkpoints exist anywhere; not a 2 because the arc and templates do define a coherent order to follow. (No destructive/batch cap applies.)

3 / 5

Progressive Disclosure

No bundle files exist at all (no references/, scripts/, or assets/ directories), yet the body inlines ~470 lines of framework examples, visualization techniques, and templates — content that clearly belongs in separate reference files — and its single external pointer, 'If detailed examples are required, open resources/implementation-playbook.md', targets a file that does not exist (a dangling reference). This matches 'Minimal structure; content that clearly belongs in separate files is inlined'. Not a 1 because the body has clear, well-ordered section headers and is navigable; not a 3 because the one reference present is broken and nothing is actually split out of SKILL.md.

2 / 5

Total

12

/

20

Passed

Description

45%Weight 40%Scale 1-5

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 names the domain and a single transformation, but it is padded with aspirational buzzwords, provides no trigger guidance for when to use the skill, and lacks the natural keywords (storytelling, presentations, reports) users would actually say. It reads closer to the bad examples' family ('Processes data') than to the good examples, which pair concrete actions with explicit 'Use when' clauses.

Suggestions

Add an explicit trigger clause, e.g., 'Use when building executive presentations, quarterly business reviews, investor decks, or data-driven reports' — this also lifts the completeness cap.

Enumerate concrete capabilities instead of one generic verb, e.g., 'Structure data narratives (hook → insight → recommendation), write insight-driven headlines, and design story-based slides and one-page dashboards'.

Include natural user vocabulary and synonyms such as 'data storytelling', 'tell a story with the data', 'executive summary', and 'QBR' to improve trigger matching and distinctiveness.

DimensionReasoningScore

Specificity

The only concrete action is "Transform raw data into compelling narratives"; the trailing "that drive decisions and inspire action" is buzzword padding rather than a capability. This matches the anchor 'Names the domain but actions are minimal or generic' (cf. "Processes PDF files"), and the judging guidelines explicitly penalize vague fluff. It is not a 3 because unlike 'Processes PDF files and extracts content' there is no second concrete action, and not a 1 because the input ('raw data') and output ('narratives') are at least named.

2 / 5

Completeness

The 'what' is stated (transform raw data into narratives for decisions), but there is no 'when' clause at all — no 'Use when...' or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. This matches 'Has a clear what but when is missing or only weakly implied'. Not a 4/5 because 'when' is entirely absent; not a 2 because the 'what' is stated rather than vague-only.

3 / 5

Trigger Term Quality

Keywords present are "raw data", "narratives", and "decisions" — some relevant terms — but common natural phrases users would say are missing: "data storytelling", "presentation", "executive summary", "QBR", "report". This fits 'Some relevant keywords but missing common variations or synonyms' ("Works with PDF files"). Not a 4 because the natural vocabulary of the skill's own name ('storytelling') is absent; not a 2 because the terms present are domain-relevant rather than fully generic.

3 / 5

Distinctiveness Conflict Risk

"Transform raw data into compelling narratives that drive decisions" carves out a data-communication niche, but the generic phrasing could plausibly trigger presentation, writing, or visualization skills instead. This matches 'Somewhat specific but could still overlap with similar skills'. Not a 4 because no distinct trigger phrases (e.g., 'executive presentations', 'QBR') mark out its territory; not a 2 because the data-narrative focus does narrow it.

3 / 5

Total

11

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills/skills/data-storytelling/SKILL.md

Reviewed

Table of Contents

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