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tracking-leak-detector

Detect gaps between expected and actual conversion tracking across ad platforms (Meta, Google, LinkedIn, TikTok, Reddit) and CRMs. Surfaces blind spots in pixel/CAPI/Enhanced Conversions that silently erode ROAS measurement.

Quality

75%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/tracking-leak-detector/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

A well-structured, genuinely actionable skill body whose tool documentation, detection thresholds, and example workflows are its strengths. Its main weaknesses are token padding — a duplicated intro line, a marketing footer, and a date stamp — and the absence of any concrete remediation guidance or verification checkpoints.

Suggestions

Remove the promotional 'About this skill' footer, the 'Last Updated: March 2026' stamp, and the opening line that duplicates the frontmatter description to reclaim tokens.

Replace the vague 'Provide specific remediation steps' with a short table of concrete fixes per leak type (e.g., zero_conversion_spend → re-verify pixel installation and conversion action mapping).

Add a brief verification step to the workflows, such as re-running attribution_health_check after fixes to confirm the leak is resolved.

DimensionReasoningScore

Conciseness

The tool tables and IF/THEN detection rules are lean and earn their tokens, but the body duplicates the frontmatter description verbatim in its opening line and closes with ~10 lines of promotional links ('About this skill', Synter CTAs) plus a time-sensitive 'Last Updated: March 2026' stamp — a substantial block of non-instructional padding.

3 / 5

Actionability

Concrete, executable guidance throughout: named tool invocations with parameters ('Call detect_tracking_leaks with days=30'), specific numeric thresholds ($100 spend, 1.5/0.5/0.3 ratios, 20% UTM), and two worked example sequences. Only gap: remediation is always deferred as 'provide specific remediation steps' without any specifics.

4 / 5

Workflow Clarity

The Example Usage section gives clearly sequenced, numbered workflows (health check → detect → report → prioritize) for two realistic scenarios. These are read-only diagnostic operations so the destructive/batch validation cap does not apply, but explicit verification or interpretation checkpoints are absent, keeping it below the top anchor.

4 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent) and the single ~155-line file is well-sectioned with clear headers, appropriately inline for a tool-invocation skill. Minor organization gaps: the promotional 'About this skill' footer is misplaced content, and the platform/CRM tables could live in a reference file.

4 / 5

Total

15

/

20

Passed

Description

70%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.

A specific, well-targeted description with strong domain vocabulary and low conflict risk, but it omits any explicit 'when to use' trigger guidance, which both caps completeness and leaves a few natural user phrasings uncovered. Adding a 'Use when...' clause with concrete trigger phrases would lift it to the top band.

Suggestions

Append an explicit trigger clause such as: 'Use when conversions seem unexpectedly low, a platform shows 0 conversions despite spend, CRM and ad-platform counts disagree, or before scaling spend.'

Include natural user phrasings like 'attribution gaps', 'tracking audit', and 'is my tracking working?' to broaden trigger-term coverage.

Optionally name one more concrete capability (e.g., 'audits UTM coverage and view-through tracking') to move specificity from several actions toward comprehensive coverage.

DimensionReasoningScore

Specificity

Two concrete actions ('Detect gaps between expected and actual conversion tracking', 'Surfaces blind spots in pixel/CAPI/Enhanced Conversions') plus enumeration of five platforms and three tracking mechanisms make it more concrete than the anchor-3 example, though it stops short of comprehensive capability coverage.

4 / 5

Completeness

The 'what' is clearly and specifically stated, but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines; the 'when' is only weakly implied by the problem framing.

3 / 5

Trigger Term Quality

Good natural keywords — 'conversion tracking', 'ROAS', 'pixel', 'CAPI', platform names — that users in this domain would actually say; a few common phrasings like 'attribution', 'audit', or 'why are my conversions low' are missing, so it falls just short of the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

A clear niche — detecting discrepancies between expected and actual tracking across named ad platforms and CRMs, with distinct terms like 'pixel/CAPI/Enhanced Conversions' and 'ROAS measurement' — making conflict with other skills minimal.

5 / 5

Total

16

/

20

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

Synter-Media-AI/free-skills

Path

skills/tracking-leak-detector/SKILL.md

Reviewed

Table of Contents

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