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dark-social-attributor

Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show social drives signups without click data"; produces a share-link/UTM hygiene spec for owned share surfaces, a self-reported attribution field design that replaces an existing form field (free-text first, coded later), a GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation) with every derived number hard-labeled Estimated/proxy, and a branded-search-lift proxy from GSC plus Wikipedia pageviews — the declared dark-social method behind ECHO O2. Not for paid-channel attribution reconciliation (platform-claimed vs analytics conversions) — use attribution-reconciler. 暗社交归因/直接流量分解/自报来源字段/分享链路UTM

67

Quality

83%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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, domain-specific instruction skill with a clear procedure, explicit done-when criteria, and strong safety/validation checkpoints. Main weaknesses are repeated handoff routing across four sections and reliance on referenced files that are not bundled with the skill.

Suggestions

Consolidate the sibling-skill handoff map into one place (e.g., Reference Materials or Next Best Skill) and reference it from the scope guard and procedure instead of restating it four times.

For the GA4 direct-traffic decomposition and branded-search-lift steps, include a concrete runnable query or script invocation alongside the heuristic description to close the actionability gap.

Verify or bundle the referenced files (echo-benchmark.md, owned-community-loop.md, CONNECTORS.md, SECURITY.md, pageviews.py) so the progressive-disclosure depth is real rather than pointing outside the skill bundle.

DimensionReasoningScore

Conciseness

Mostly efficient and free of basic-concept padding, but the handoff/routing map (email-sequence-designer, consent-registry, attribution-reconciler, channel-registry, social-measurement-loop) is restated across the scope guard, procedure step 7, Reference Materials, and Next Best Skill sections and could be tightened.

3 / 5

Actionability

Concrete, actionable guidance throughout — a runnable `python3 scripts/connectors/pageviews.py` command, a specific UTM taxonomy example, the 'REPLACE the lowest-value existing form field' rule, and three copy-paste Quick Start prompts — with minor gaps where decomposition/lift heuristics are described but not turned into runnable queries.

4 / 5

Workflow Clarity

A clear 8-step numbered procedure with an explicit 'Done when' checklist and security validation (untrusted-input treatment, ask-before-writing, propose-only writes), but no explicit validate→fix→retry feedback loop for the analysis steps.

4 / 5

Progressive Disclosure

Well-organized into Quick Start, Skill Contract, Data Sources, Instructions, Reference Materials, and Next Best Skill with clearly signaled one-level references; gap is that no bundle files are present and all references point outside the skill bundle into a parent repo, so the referenced depth cannot be verified.

4 / 5

Total

15

/

20

Passed

Description

100%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 dense, specific description that answers what and when with concrete natural-language triggers and an explicit boundary against the sibling attribution-reconciler skill. Its only weakness is verbosity, but the length is packed with specifics rather than fluff.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'share-link/UTM hygiene spec', 'self-reported attribution field design', 'GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation)', and 'branded-search-lift proxy from GSC plus Wikipedia pageviews' — covering the capability space comprehensively.

5 / 5

Completeness

Explicitly answers both 'when' ('Use when the user asks to...') and 'what' ('produces a share-link/UTM hygiene spec...branded-search-lift proxy') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Embeds natural user phrasings — 'figure out where our direct traffic really comes from', 'measure dark social', 'add a how-did-you-hear-about-us field', 'show social drives signups without click data' — plus domain terms (dark social, direct traffic, GSC, UTM) and Chinese synonyms, giving comprehensive trigger coverage.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche and explicitly fences off the sibling lane — 'Not for paid-channel attribution reconciliation...use attribution-reconciler' — minimizing wrong-skill triggering.

5 / 5

Total

20

/

20

Passed

Validation

75%

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

Validation12 / 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: 30 suspicious

Warning

referenced_paths_exist

Referenced path issues: 3 missing, 3 deeper-than-1-level

Warning

Total

12

/

16

Passed

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

Table of Contents

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