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exa-search

AI-powered web search via Exa with content extraction. Use when user says "exa search", "web search with content", "find similar pages", or needs broad web results beyond academic databases (arXiv, Semantic Scholar).

63

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

62%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, largely executable search skill with a clear sequenced workflow and explicit validation/failure handling. The main weaknesses are the pseudocode leaking into the Step 6 bash block, duplicated script-resolution boilerplate, and a monolithic SKILL.md that does not leverage bundle files for progressive disclosure.

Suggestions

Make the Step 6 wiki block executable: replace the English pseudocode ("for each research-paper hit in results:", "if URL matches arxiv.org/abs/<id>:") with real bash, or move the per-hit loop into the research_wiki.py helper so the snippet is copy-paste ready.

De-duplicate the git/ARIS_REPO resolution cascade (it appears in both Step 2 and Step 6); reference the integration contract once and inline only the skill-specific path.

Split the wiki-ingest workflow and integration-contract details into a referenced file under references/ so SKILL.md stays a lean overview and progressive disclosure becomes real rather than cross-skill links only.

DimensionReasoningScore

Conciseness

The body is mostly dense, actionable content without concept padding, but the git/ARIS_REPO script-resolution cascade is repeated nearly verbatim in Step 2 and Step 6, and the integration-contract boilerplate could be tightened. Not a 3 because of that duplicated, heavy resolution prose; not a 1 because there is no explanatory fluff about concepts Claude already knows.

2 / 3

Actionability

Most snippets are copy-paste ready ("pip install exa-py", "python3 \"$EXA_FETCHER\" search ... --content highlights", find-similar, get-contents), but the Step 6 wiki block mixes English pseudocode into a bash fence ("for each research-paper hit in results:", "if URL matches arxiv.org/abs/<id>:"). Not a 3 because that block is not executable; not a 1 because the majority of the code is concrete and runnable.

2 / 3

Workflow Clarity

A clear six-step sequence (Parse Args -> Locate Script -> Execute -> Present -> Follow-up -> Wiki) with explicit validation checkpoints: Step 2 fails fast with remediation when the helper is missing, and Step 6 uses warn-and-skip with per-hit logging for the batch ingest. Not a 2 because validation and error-recovery checkpoints are explicit rather than implicit.

3 / 3

Progressive Disclosure

Sections are well-organized and the shared-references links (integration-contract.md §2, wiki-helper-resolution.md) are clearly signaled and one level deep, but the skill ships no bundle files and SKILL.md is a ~200-line monolith with the resolution cascade inlined. Not a 3 because content is not actually split across files; not a 1 because references are signaled and navigation is easy via clear headers.

2 / 3

Total

9

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12

Passed

Description

90%

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, third-person description that clearly states both capability and trigger conditions and carves out a distinct niche from sibling academic-search skills. The only soft spot is specificity, where the what-clause lists few concrete actions and leans on the mild buzzword "AI-powered".

Suggestions

Drop the "AI-powered" qualifier and lead with concrete verbs (e.g., "Search the broad web via Exa and extract highlights, full text, or summaries from results.") to broaden the action list.

Add one or two more explicit actions to the what-clause (e.g., "find similar pages", "fetch content for known URLs") so capability breadth matches the trigger coverage.

DimensionReasoningScore

Specificity

Names the domain and a couple of concrete actions ("web search via Exa with content extraction", "find similar pages"), but the what-clause is not a comprehensive list of specific actions, and "AI-powered" is mild buzzword. Not a 3 because it lacks the multi-action breadth of the top anchor; not a 1 because actions are concrete rather than abstract.

2 / 3

Completeness

Explicitly answers both what ("AI-powered web search via Exa with content extraction") and when ("Use when user says ... or needs broad web results beyond academic databases"). Not a 2 because the when-clause is explicit, not merely implied.

3 / 3

Trigger Term Quality

Quotes natural trigger phrases a user would say: "exa search", "web search with content", "find similar pages", plus a needs-based trigger ("broad web results beyond academic databases"). Good coverage of natural terms; not a 2 because it supplies several realistic phrasings rather than a single keyword.

3 / 3

Distinctiveness Conflict Risk

The carve-out "beyond academic databases (arXiv, Semantic Scholar)" plus the Exa-specific trigger "exa search" give it a clear niche unlikely to fire for the sibling academic-search skills. Not a 2 because the boundary against overlapping skills is stated explicitly.

3 / 3

Total

11

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 4 suspicious

Warning

Total

13

/

16

Passed

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Reviewed

Table of Contents

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