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industry-scanner

Daily industry intelligence scanner. Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas. Orchestrates existing scraping skills — does not reimplement data collection.

57

Quality

72%

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/competitive-intel/composites/industry-scanner/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

This is a highly actionable orchestration skill: every source has an exact command, the categorization and opportunity frameworks are concrete, and the output format is fully templated. Its main weaknesses are the absence of any error handling or validation across the batch of parallel source scans, and a monolithic 360-line body whose report template and strategy-pattern catalog should live in reference files.

Suggestions

Add validation/feedback steps for the batch data collection — e.g., check each source's exit status and output, retry or log-and-skip failed sources, and report which sources failed in the Scan Statistics section.

Move the ~90-line report template and the strategy-patterns catalog into reference files (e.g. references/report-template.md and references/strategy-patterns.md) with clear one-level pointers from the body.

Trim the redundant opening paragraph (it restates the frontmatter description) and compress the asides in Phase 3/4 to tighten token efficiency.

DimensionReasoningScore

Conciseness

Nearly all of the ~360 lines are concrete commands, config fields, and structured tables with almost no explanation of concepts Claude already knows. Minor trims are possible (the intro paragraph repeats the frontmatter description, asides like "The goal is signal, not volume"), so it fits the efficient-with-minor-trims anchor rather than the lean every-token-earns-its-place anchor given the sheer inline volume.

4 / 5

Actionability

Every data source has a copy-paste-ready bash command with flags and placeholder substitution (e.g. the blog, Reddit, Twitter, HN, review-site scripts), web-search query patterns are given verbatim, and the output template plus config field list make execution unambiguous. This matches the fully-executable anchor with specific examples covering the common cases.

5 / 5

Workflow Clarity

The five-phase sequence is clearly ordered and Phase 3 says to consolidate "after all data collection completes", but there are no validation checkpoints or feedback loops for a batch operation spanning up to nine parallel sources — nothing on what to do when a script fails, returns empty, or errors mid-scan. The rubric's scoring note caps workflow clarity at 3 for batch operations without feedback loops, which precludes the level-4 anchor.

3 / 5

Progressive Disclosure

Section structure is good and references to other skills are clearly signaled and one level deep ("Read skills/blog-feed-monitor/SKILL.md for full CLI reference"), but the skill ships no bundle files of its own while inlining roughly 90 lines of report template and 45 lines of strategy patterns that belong in reference files. This matches the anchor where content that should be separate is inline, rather than the good-placement anchor at 4.

3 / 5

Total

15

/

20

Passed

Description

58%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 gives a clear, third-person picture of what the skill does and how it relates to other scraping skills, but it never states when to invoke it. Adding an explicit "Use when..." clause and natural synonyms like competitive intelligence or market/competitor monitoring would lift both completeness and trigger-term quality.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user asks for an industry scan, daily/weekly industry intelligence, or competitive/market monitoring for a client."

Include natural synonyms users would actually say — "market research", "competitive intelligence", "competitor monitoring", "news monitoring" — to broaden trigger coverage.

Trim evaluative padding like "comprehensive" and spell out what the briefing contains (competitor news, trends, events) to sharpen specificity.

DimensionReasoningScore

Specificity

"Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas" names several concrete actions with only minor gaps, matching the anchor for several specific actions. Not a 5 because coverage is padded with "comprehensive" and the outputs are described at a slightly abstract level.

4 / 5

Completeness

The "what" is clear (scans multiple source types and produces a briefing plus GTM opportunity ideas), but there is no "Use when..." clause or equivalent trigger guidance — the rubric explicitly caps completeness at 3 for this. Not a 2 because the "what" is concrete and multi-part.

3 / 5

Trigger Term Quality

Relevant keywords are present ("industry", "scans", "news", "blogs", "social media", "trends", "signals"), but common natural variations users would say are missing, such as "market research", "competitive intelligence", "competitor monitoring", or "monitor". This matches the anchor for some relevant keywords with missing synonyms rather than the good-coverage anchor.

3 / 5

Distinctiveness Conflict Risk

"Daily industry intelligence scanner" that "orchestrates existing scraping skills" carves out a fairly distinct niche with only minor overlap risk against generic news-monitoring or deep-research skills. Not a 5 because the description does not explicitly separate its triggers from those adjacent skills.

4 / 5

Total

14

/

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
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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