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ai-discoverability

Improve a documentation site's visibility in AI training data and AI-powered search. Use when someone wants to submit their site to llms.txt directories (llmstxthub.com, llmstxtdirectory.org, GitHub thedaviddias/llms-txt-hub), build backlinks from AI-indexed developer sources, get listed in G2/Capterra/developer directories, improve AI discoverability, or create a prioritized strategy for appearing in AI search results and citation recommendations.

86

1.12x
Quality

86%

Does it follow best practices?

Impact

88%

1.12x

Average score across 3 eval scenarios

SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%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, actionable skill body with strong progressive disclosure (verified reference file, clearly signaled) and a validation gate for #25. It falls slightly short of top marks on conciseness, actionability, and workflow clarity due to minor placeholder gaps in the #26 template and the absence of completion checkpoints in the authority-building workflow.

Suggestions

Tighten #26's tier plan: remove or compress the 'Impact:'/'Gets indexed by...' rationale lines that restate the skill's premise, keeping est. times and actions only.

Replace key placeholder rows with one concrete worked example (e.g., a filled-in Stack Overflow tag or a named niche directory), so the tier plan is copy-paste ready rather than partially templated.

Add a verification checkpoint to #26 (e.g., after Step 1, confirm which 'best [category] software' lists currently accept submissions before building the Tier 1 plan around them), mirroring the explicit stop-gate that #25 already has.

DimensionReasoningScore

Conciseness

Mostly efficient templates, checklists, and specific search strings with no padding that explains concepts Claude already knows, but lines like 'Impact: High — G2 pages are heavily indexed by AI training data' and 'Shows real API usage / Gets indexed by GitHub search + AI training data' are minor over-explanation that could be trimmed.

4 / 5

Actionability

Concrete guidance throughout — exact URLs, copy-paste search queries ('Search: "[product name] review" site:g2.com OR site:capterra.com'), the exact PR row format, and a prerequisite check — but several template rows remain placeholders ('[Niche-specific directory] — [URL]', '[relevant tag]', 'Target publications: [research and list 2-3 relevant blogs]'), leaving minor gaps in executability.

4 / 5

Workflow Clarity

Item #25 has a clear sequence with an explicit validation gate ('If llms.txt is missing, stop and note: Item #1 must be completed before #25') and a submission checklist, but #26's Steps 1-4 lack checkpoints verifying research completeness or submission outcomes — most checkpoints present with minor gaps. The destructive/batch cap does not apply since these are research and planning operations.

4 / 5

Progressive Disclosure

The reference is well-signaled at the top ('Load `references/directories.md` for the current list of llms.txt directories and submission details') and again in the registration process, the file exists on disk, is one level deep, and appropriately holds the volatile directory data while output templates stay inline — a clear overview with well-signaled references.

5 / 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, action-enumerating, with an explicit 'Use when...' trigger clause and clearly named submission targets. Minor room for improvement in synonym coverage (e.g., 'AI SEO', 'GEO') and slightly sharper distinction from generic SEO/backlink requests.

DimensionReasoningScore

Specificity

Enumerates multiple concrete actions with named targets — 'submit their site to llms.txt directories (llmstxthub.com, llmstxtdirectory.org, GitHub thedaviddias/llms-txt-hub)', 'build backlinks from AI-indexed developer sources', 'get listed in G2/Capterra/developer directories', 'create a prioritized strategy for appearing in AI search results and citation recommendations' — matching the comprehensive-coverage anchor. The only mildly abstract phrase ('Improve a documentation site's visibility') is immediately grounded by these specifics, so this is not the minor-gaps level below.

5 / 5

Completeness

Explicitly answers both: the 'what' ('Improve a documentation site's visibility in AI training data and AI-powered search') and the 'when' ('Use when someone wants to submit their site to llms.txt directories... build backlinks... get listed in G2/Capterra...'), with concrete trigger phrases throughout — the clear both-what-and-when anchor.

5 / 5

Trigger Term Quality

Good natural-keyword coverage ('llms.txt', 'backlinks', 'G2', 'Capterra', 'AI discoverability', 'AI search results', 'citation'), but common user phrasings like 'AI SEO', 'GEO/generative engine optimization', or 'get cited by ChatGPT/Perplexity' are missing, which fits 'a few natural terms missing' rather than comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The llms.txt directory triggers carve a distinct niche, but 'build backlinks' and 'get listed in G2/Capterra' carry minor overlap risk with general SEO/marketing skills, fitting 'mostly distinct; minor overlap risk' rather than minimal conflict risk.

4 / 5

Total

18

/

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

kubical-ai/aeo-geo-optimization

Path

claude-code/skills/ai-discoverability/SKILL.md

Reviewed

Table of Contents

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