ad-angle-miner

Mine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes and recommended ad formats per angle.

Quality

66%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/ads/composites/ad-angle-miner/SKILL.md

Quality

Content

57%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 content is highly actionable with concrete API calls, query templates, and a complete output format, but it runs long and inlines material (API specifics, scoring rubric, output template) that would benefit from being split into referenced files. Missing validation checkpoints on the batch scoring step cap workflow clarity at 3.

Suggestions

Add an explicit validation/checkpoint in Phase 2–3 (e.g. 'Before ranking, confirm each angle has ≥2 independent source quotes; drop angles that don't') to satisfy the batch-operation feedback-loop requirement.

Move the detailed Apify HTTP request examples, output-field schemas, and the full output markdown template into referenced files (e.g. references/sources.md, references/output-template.md) and link to them, leaving SKILL.md an overview.

Trim the 'Core principle' paragraph and consolidate the angle-category and scoring tables to reduce padding and respect the token budget.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and action-oriented, but at ~300 lines it carries padding (the 'Core principle' framing and verbose category/score tables) that could be tightened without losing meaning.	3 / 5
Actionability	Provides concrete, executable guidance — exact Apify actor names, HTTP run/poll/dataset calls, web_search query templates, output field lists, and a full output markdown template — with only minor gaps (placeholder-laden request bodies and omitted auth-header specifics).	4 / 5
Workflow Clarity	Phases 0–4 are clearly sequenced with a poll-until-SUCCEEDED loop, but the batch scoring/ranking and angle-extraction steps have no validation checkpoints, so the missing-feedback-loop cap for batch operations holds it at 3.	3 / 5
Progressive Disclosure	The skill is a single monolithic SKILL.md with no bundle files; the API reference details, scoring rubric, and output template that could live in separate one-level-deep files are all inlined, though internal section headers provide reasonable navigation.	3 / 5
	Total	13 / 20 Passed

Description

75%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 specific, distinct, and rich in natural trigger terms, but it omits an explicit 'Use when...' clause, leaving the activation trigger only implied and capping completeness. Adding a concrete trigger line would lift it to the top tier.

Suggestions

Append an explicit 'Use when...' clause, e.g. 'Use when planning ad campaigns, needing fresh ad angles, or mining reviews/Reddit/support tickets for ad messaging.'

Add common user phrasings as trigger terms such as 'ad copy', 'ad messaging', 'hook', or 'value proposition' to broaden trigger coverage.

Keep the concrete action list but ensure the opening sentence ties the output (ranked angle bank) directly to the activation scenario.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Mine the highest-converting ad angles', 'Extracts actual pain language, competitor weaknesses, and outcome phrases', and 'Outputs a ranked angle bank with proof quotes and recommended ad formats per angle' — giving comprehensive, specific coverage rather than abstract language.	5 / 5
Completeness	The 'what' is clear and concrete, but there is no explicit 'Use when...' trigger clause, so the 'when' is only weakly implied — the missing-trigger-guidance rule caps completeness at 3.	3 / 5
Trigger Term Quality	Strong natural terms ('ad angles', 'customer reviews', 'Reddit complaints', 'support tickets', 'competitor ads', 'pain language'), but a few common phrasings users would actually say (e.g. 'ad copy', 'ad messaging', 'hook') are absent.	4 / 5
Distinctiveness Conflict Risk	Carves a clear niche (extracting ad angles from customer voice data with a ranked angle bank output) with distinctive triggers, making collision with other skills unlikely.	5 / 5
	Total	17 / 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/ads/composites/ad-angle-miner/SKILL.md
Commit: e1592ee

Reviewed: 3 days ago

Table of Contents

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