resilient-data-gathering

Fallback pattern when primary tools fail - embed known data in scripts and persist to JSON for auditability

Quality

55%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

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tessl review fix ./benchmarks/gdpval/skills/resilient-data-gathering/SKILL.md

Quality

Content

56%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 skill is highly actionable with executable code and a clear sequenced workflow, but it is verbose due to repeated full scripts and keeps everything in a single long file with no progressive disclosure. Adding a validation retry loop and splitting redundant examples would improve it.

Suggestions

Remove the redundant Complete Fallback Workflow example or move it to a separate reference file, keeping one canonical script inline.

Add an explicit validate-fix-retry loop in Step 5 so failed JSON verification triggers correction rather than a bare assert.

Extract the Best Practices code snippets into a referenced file (e.g. best_practices.md) to reduce inline bulk and improve navigation.

Dimension	Reasoning	Score
Conciseness	Full Python scripts are repeated across Step 3, Best Practices, and the Complete Fallback Workflow example, creating noticeable redundancy; not a 3 because the padding is substantial, not a 1 because it avoids explaining concepts Claude already knows.	2 / 5
Actionability	Concrete, executable Python and bash code with complete scripts covers the common cases; not a 5 because Step 4 uses placeholders like "[paste the full script from Step 3]" rather than literal copy-paste.	4 / 5
Workflow Clarity	A clear six-step sequence includes a decision point (Step 1) and a verification step (Step 5); not a 5 because the main flow lacks an explicit validate-fix-retry feedback loop when verification fails.	4 / 5
Progressive Disclosure	The body is well-sectioned but ~290 lines with all content inlined and no reference files; not a 4 because bulk scripts that could be split out are inline and there are no signaled references.	3 / 5
	Total	13 / 20 Passed

Description

55%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 answers both what and when concisely with two concrete actions, but relies on semi-technical language and lacks natural user-facing trigger terms and synonyms. It is reasonably distinct but could sharpen its trigger phrasing.

Suggestions

Add natural trigger terms a user would say, e.g. "Use when web search, shell, or code tools fail repeatedly" instead of "primary tools fail".

Include synonyms/file cues (e.g. "JSON", "fallback", "embedded data") to broaden keyword coverage.

Name a third concrete action (e.g. "verify persisted output") to lift specificity toward comprehensive coverage.

Dimension	Reasoning	Score
Specificity	The description names two concrete actions — "embed known data in scripts" and "persist to JSON for auditability" — fitting the 1-2 concrete actions anchor; not a 4 because it does not list several specific actions.	3 / 5
Completeness	Both what ("embed known data in scripts and persist to JSON") and when ("Fallback pattern when primary tools fail") are present, with the when explicit enough to avoid the cap; not a 5 because the trigger lacks concrete, specific phrases.	4 / 5
Trigger Term Quality	Terms like "primary tools fail" and "auditability" lean technical/generic and miss the natural phrases a user would say; not a 3 because common variations and synonyms are absent.	2 / 5
Distinctiveness Conflict Risk	The fallback-when-tools-fail niche is mostly distinct from other skills; not a 5 because "primary tools fail" is a somewhat generic trigger with minor overlap risk.	4 / 5
	Total	13 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: HKUDS/OpenSpace
Path: benchmarks/gdpval/skills/resilient-data-gathering/SKILL.md
Commit: 3827781

Reviewed: 7 days ago

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