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share-of-voice-tracker

Use when the user asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额

68

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

71%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-structured, actionable measurement workflow with a clear sequenced procedure and explicit validation gates. Its main weakness is token efficiency: the same scope-guard and red-line compliance statements are repeated multiple times across sections where a single canonical statement would suffice.

Suggestions

Consolidate the closed-platform 'never scraped' red line and the channel-registry 'sole writer / operation: propose' rule into one canonical statement each (e.g., in Scope Guard and Skill Contract) and reference them by name elsewhere instead of restating them in Data Sources, Instructions, and Save Results.

State the competitor-tracker / offsite-signal-analyzer / social-pulse-monitor ownership boundaries once in Scope Guard; the Reference Materials and Next Best Skill sections already cover routing, so the inline restatements in the Instructions prose can be dropped.

Add one or two concrete command-line invocations of the named connectors (e.g., a sample gdelt.py or pageviews.py call with arguments) to lift actionability from instruction-style to copy-paste-ready.

DimensionReasoningScore

Conciseness

The body is information-dense and avoids explaining concepts Claude already knows, but the same compliance statements are repeated several times — the closed-platform 'never scraped' red line, the 'registry-events.py operation: propose' / channel-registry-sole-writer rule, and the competitor-tracker/offsite-signal-analyzer scope guard each appear 3-4 times — which is more than minor padding.

3 / 5

Actionability

Concrete guidance throughout: named connector scripts (hn.py, bluesky.py, gdelt.py, pageviews.py), exact memory paths (memory/social/share-of-voice-tracker/), the explicit SOV% formula, per-cell labels (Measured/proxy/Estimated), and the ≥5s gdelt spacing — but it is instruction-style without copy-paste-ready command invocations, leaving minor gaps.

4 / 5

Workflow Clarity

A clear 8-step numbered sequence with an explicit panel-break validation gate (step 2), a NEEDS_INPUT guard against inventing a panel, and a 'Done when' checklist; checkpoints are mostly present, though feedback loops are limited because the work is read-only analysis rather than destructive editing.

4 / 5

Progressive Disclosure

Well-organized into overview sections (Quick Start, Skill Contract, Data Sources, Instructions, Reference Materials, Next Best Skill) with a clean Reference Materials list of one-level-deep references (echo-benchmark.md, CONNECTORS.md, SECURITY.md, skill-contract.md) clearly signaled for navigation.

5 / 5

Total

16

/

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.

The description is exemplary: it pairs concrete trigger phrases with a precise statement of what the skill computes, names the variants and the locked-panel constraint, and explicitly fences off neighboring skills. Voice is appropriately third-person ('computes', 'Use when').

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'computes SOV% = brand mentions ÷ (brand + competitor panel mentions)', a 'sentiment-weighted SOV variant', a 'Wikipedia-pageviews attention-share alternative', and panel-break handling ('restart the series and log the break') — covering the domain comprehensively, matching the score-5 anchor.

5 / 5

Completeness

Explicitly answers both 'what' (the SOV% computation and its variants) and 'when' (the 'Use when the user asks to...' clause with concrete trigger phrases), matching the score-5 anchor exactly.

5 / 5

Trigger Term Quality

Quotes natural user phrases ('track our share of voice', 'what share of the conversation do we own vs competitors', 'trend our SOV this quarter') plus the SOV acronym and Chinese synonyms (声量份额/提及份额/注意力份额), giving comprehensive natural-term coverage including synonyms.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (SOV on a locked competitor panel) and explicitly disambiguates from adjacent skills ('Not for backlink or offsite SEO signals — use offsite-signal-analyzer'), minimizing conflict risk.

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: 32 suspicious

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

12

/

16

Passed

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

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