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ad-campaign-analyzer

Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.

54

Quality

61%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./skills/ad-campaign-analyzer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 content is a well-sequenced, actionable instruction skill with explicit thresholds, formulas, rollback rules, and uncertainty guardrails — strong on actionability and workflow clarity. Its main weaknesses are an editorial opening and duplicated trigger lists that pad the token budget, and a monolithic single-file structure with no progressive disclosure despite being long enough to warrant reference files.

Suggestions

Cut the editorial "Core principle" paragraph and deduplicate the "When to Use" and "Examples" lists into a single trigger section to recover tokens.

Move the Phase 5 output-format template and the 2D statistical-significance method detail into separate reference files (e.g. references/output-template.md, references/significance-methods.md) and link to them one level deep.

Add an explicit validate→fix→retry loop for the budget-reallocation step (e.g., re-run the diagnostics after any reallocation and confirm CPA moved within the predicted interval before further changes).

DimensionReasoningScore

Conciseness

The body is mostly efficient — dense template tables and formulas assume Claude's competence and avoid explaining basics like ROAS — but the editorial "Core principle" paragraph and near-duplicate "When to Use" (11 items) and "Examples" (10 items) lists are noticeable padding that could be trimmed.

3 / 5

Actionability

Provides concrete formulas (Channel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)), explicit thresholds (CPA > 3x target, CTR < 50% of campaign average), a structured significance-test template, and a full output scaffold — actionable guidance with only minor gaps from the placeholder template rows.

4 / 5

Workflow Clarity

Phases 0–5 are clearly sequenced with checkpoints (non-comparable channels flagged, uncertainty validated before action, bounded tests with predefined stop/rollback rules, confirm-before-write); falls short of 5 only because the validate→fix→retry feedback loop is implicit rather than an explicit re-run step.

4 / 5

Progressive Disclosure

No bundle files exist and the ~370-line skill is a single monolithic document; sections are well-organized, but the long Phase 5 output template and statistical-method detail that clearly belong in separate reference files are inlined with no one-level-deep references signaled.

3 / 5

Total

14

/

20

Passed

Description

58%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 states concrete capabilities in third person and carves a distinct niche, but it omits any explicit "Use when..." trigger guidance and uses somewhat technical language over natural user phrasing. Adding a trigger clause with common user terms would lift the completeness and trigger-term dimensions.

Suggestions

Append an explicit "Use when..." clause naming natural user triggers (e.g., "Use when the user asks to analyze ad performance, decide which ads to pause, or reallocate ad budget across Google/Meta/LinkedIn").

Replace jargon like "evidence-labeled" and "overstating causality" with the phrasing a founder actually says ("ROAS", "which ads should I kill", "where am I wasting spend").

Add common synonyms and platform names (Google Ads, Meta Ads, LinkedIn, ROAS, CPA) so the trigger term coverage reaches a 5.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete actions — "Analyze cross-channel campaign data", "quantify uncertainty", "propose evidence-labeled budget tests" — with only minor coverage gaps, matching the several-specific-actions anchor rather than the comprehensive 5.

4 / 5

Completeness

Has a clear "what" but no "Use when..." clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3; the "when" is only weakly implied by the campaign-data framing.

3 / 5

Trigger Term Quality

Contains relevant domain keywords ("cross-channel campaign data", "budget tests") but leans technical ("quantify uncertainty", "evidence-labeled", "overstating causality") and misses the natural phrases users actually say like "ad performance", "ROAS", or "which ads to pause".

3 / 5

Distinctiveness Conflict Risk

The ad-campaign niche with uncertainty quantification and causality hedging is mostly distinct with only minor overlap risk against a general analytics skill, fitting the mostly-distinct anchor below the clear-niche 5.

4 / 5

Total

14

/

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.

Validation15 / 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
Reviewed

Table of Contents

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