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attribution-reconciler

Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量

70

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-3

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

A well-structured, highly actionable workbook with a clear validated workflow and clean one-level reference disclosure; the main drag is repetition of the roi-calculator handoff and the truth-set rule, which keeps conciseness from the top score.

Suggestions

State the roi-calculator delegation once in the contract and once in Step 7; remove the redundant restatements in the intro, Reference Materials, and Next Best Skill to reclaim conciseness.

Collapse the repeated 'the truth set is the order IDs, never a platform count' rule into its single authoritative statement near the top.

DimensionReasoningScore

Conciseness

Mostly efficient and assumes Claude's competence, but the roi-calculator delegation is restated ~5 times (intro, contract, Step 7, Reference Materials, Next Best Skill) and the 'order-ID truth set is the only rule' point is repeated, so it could be tightened.

2 / 3

Actionability

Concrete, executable guidance throughout: explicit join keys ('order ID preferred or timestamp + value'), labels (matched/double-counted/unmatched), a formula ('incremental orders ÷ exposed'), and exact memory paths and handoff targets.

3 / 3

Workflow Clarity

A clearly sequenced 7-step process with explicit validation checkpoints (Step 1 NEEDS_INPUT gate, 'normalize before matching', 'mark incrementality N/A if no holdout') and a 'Done when' checklist defining the terminal state.

3 / 3

Progressive Disclosure

A focused overview with one-level-deep references (roas-benchmark, CONNECTORS, SECURITY, sibling SKILL.md files), a skill contract, and a reference-materials list; no bundle files exist locally, and all referenced paths are clearly signaled sibling references rather than nested chains.

3 / 3

Total

11

/

12

Passed

Description

90%Weight 40%Scale 1-3

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 richly specified, well-triggered description with strong distinctiveness and completeness; its only weakness is the second-person voice ('you suspect'), which costs a specificity point per the rubric guidelines.

Suggestions

Rewrite 'when you suspect Meta and Google are double-counting' in third person (e.g. 'when platform-reported conversions disagree with GA4/ecommerce and Meta and Google appear to double-count the same sales') to remove the voice penalty.

The trailing raw-language tag '付费广告归因对账/去重/增量' duplicates the summary; consider folding it into the trigger phrasing rather than appending it verbatim.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete actions ('de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test'), but the second-person phrase 'when you suspect Meta and Google are double-counting' triggers the voice penalty that caps this at 2.

2 / 3

Completeness

Explicitly answers both what it does and when to use it via a leading 'Use when...' clause plus a standing-workbook trigger, and adds 'Not for...' delegation to three sibling skills.

3 / 3

Trigger Term Quality

Strong coverage of natural trigger phrases users would say ('platform-reported conversions disagree with GA4/ecommerce', 'Meta and Google are double-counting the same sales', 'monthly reconciliation workbook').

3 / 3

Distinctiveness Conflict Risk

A clear niche (paid-channel reconciliation against an order-ID truth set) with explicit 'not for' carve-outs naming ad-account-auditor, roi-calculator, and dark-social-attributor, making conflicts unlikely.

3 / 3

Total

11

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 19 suspicious

Warning

Total

13

/

16

Passed

Repository
aaron-he-zhu/aaron-marketing-skills
Reviewed

Table of Contents

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