competitor-content-tracker

Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.

Quality

87%

Does it follow best practices?

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Securityby

Low

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Quality

Content

82%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 highly actionable, well-sequenced orchestration skill: every phase has executable commands, concrete config and output templates, and practical extras (cost table, scheduling). Weaknesses are minor — some duplicated trigger/summary content and the absence of validation or error-recovery checkpoints for failed scrapes.

Suggestions

Remove the 'Trigger Phrases' section (it duplicates 'When to Use') and tighten the opening paragraph, which restates the frontmatter description.

Add brief validation guidance between collection and synthesis: check that each scraper returned data before synthesizing, and specify what to do when a blog feed, LinkedIn profile, or Twitter search fails or returns empty.

Move the ~40-line markdown digest template into a references/ file (e.g., references/digest-template.md) and link to it from Phase 5 to keep the overview lean.

Dimension	Reasoning	Score
Conciseness	The body is efficient — phases deliver commands and collection lists without explaining concepts Claude already knows — with only minor trimmable redundancy: the 'Trigger Phrases' section duplicates 'When to Use', and the opening paragraph restates the frontmatter description. Not score 5 because of that duplication; not score 3 because padding is minimal.	4 / 5
Actionability	Every phase provides copy-paste-ready commands with concrete flags and paths (scrape_blogs.py, scrape_linkedin_posts.py, search_twitter.py), plus a complete config JSON, output file paths, and a cron example. Placeholders like <YYYY-MM-DD> are appropriate parameterization, not gaps, so this exceeds the 'minor gaps' level-4 anchor.	5 / 5
Workflow Clarity	Phases 0-5 are clearly sequenced with per-phase data-collection lists and concrete output/save steps. Not score 5 because there are no validation checkpoints (e.g., what to do when a feed fails or a competitor returns no posts); not score 3 because the sequence is complete and operations are read-only, so the destructive/batch validation cap does not apply.	4 / 5
Progressive Disclosure	No bundle files exist, and the single-file body is well-sectioned with clear headers covering intake through output. Not score 5 because the ~40-line digest output template is inlined content that could live in a one-level-deep reference file, and no external references are used to split detail out of the overview.	4 / 5
	Total	17 / 20 Passed

Description

88%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 strong description: concrete multi-channel capabilities, named upstream skill chains, and an explicit 'Use when...' clause with user-voiced trigger phrasing. Keyword coverage is good but misses a few common synonyms, and there is minor overlap risk with general social-monitoring skills.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — 'Monitor competitor content across blogs, LinkedIn, and Twitter/X', 'Surfaces new posts, trending topics, and content gaps' — with comprehensive coverage of the skill's scope and named channel enumeration. Not score 4 because there are no meaningful gaps in the action coverage for this domain.	5 / 5
Completeness	Clearly answers 'what' (monitors competitor content across three named channels, surfaces posts/trending topics/gaps, chains three named skills) and 'when' with the explicit clause 'Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement'. Not score 4 because the 'when' is explicit and phrased in concrete, user-voiced terms.	5 / 5
Trigger Term Quality	Natural trigger terms are present — 'competitor content', 'weekly digest', 'what competitors are publishing', 'LinkedIn', 'Twitter/X' — giving good keyword coverage. Not score 5 because common variations like 'competitor analysis', 'social media monitoring', or 'track competitors' are missing.	4 / 5
Distinctiveness Conflict Risk	The niche is distinct — recurring competitor content monitoring with a digest output, anchored by three named upstream skills — making wrong-skill triggering unlikely. Not score 5 because it could still minorly overlap with broader social-media monitoring or competitive-analysis skills.	4 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: gooseworks-ai/goose-skills
Path: skills/competitive-intel/composites/competitor-content-tracker/SKILL.md
Commit: cc3e518

Reviewed: 7 days ago

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