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agentsociety-use-dataset

Use when external datasets need to be searched, inspected, or downloaded for experiments or analysis.

63

Quality

75%

Does it follow best practices?

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SecuritybySnyk

Low

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tessl review fix ./extension/skills/agentsociety-use-dataset/v1.0.0/SKILL.md
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.

The content is a well-structured, lean overview with a concrete command table, a clear workflow, correct routing to sibling skills, and genuinely useful one-level-deep references that exist in the bundle. Remaining gaps are minor: slight redundancy in the Common Mistakes table and the absence of a complete copy-paste command example and failure-handling steps in the body itself.

DimensionReasoningScore

Conciseness

The body is efficient — terse command tables, a compact workflow graph, and pointers instead of inlined details — with only minor trims available: Common Mistakes rows 1 and 3 both restate 'read the README first', and the dot graph largely restates the flow already implied by the table. It is not 5 due to that redundancy, and not 3 because there is no unnecessary explanation of concepts Claude already knows.

4 / 5

Actionability

The Quick Reference table gives concrete commands with flags ('search --category --tags --limit --skip', 'download <id>', 'cat <id> <path>') plus the invocation path '.agentsociety/bin/ags.py', so guidance is mostly executable. It is not 5 because no complete copy-paste invocation example appears in the body itself (the full command form lives in references/listing-guide.md), and the exact subcommand composition and Python interpreter path must be assembled from .env/CLAUDE.md context.

4 / 5

Workflow Clarity

The workflow 'browse -> readme -> download -> inspect -> update' is a clear, correctly ordered sequence, with the README-first checkpoint emphasized in bold and update-checking covered both in the flow and the Common Mistakes table. It is not 5 because there are no explicit validation/feedback steps for failure cases (e.g. a failed or partially extracted download), though the operations are non-destructive so no cap applies.

4 / 5

Progressive Disclosure

The body is a lean overview, and both detailed topics are pushed to clearly signaled, one-level-deep references ('see references/listing-guide.md', 'see references/metadata-format.md') that exist in the bundle and match their described purposes. This matches the 'clear overview with well-signaled one-level-deep references' anchor; the scripts/use.py bundle file is appropriately kept out of the main file.

5 / 5

Total

17

/

20

Passed

Description

71%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 concise, uses an explicit 'Use when...' trigger, and names three concrete actions, giving it solid specificity and completeness. Its main weaknesses are moderate conflict risk with generic data-analysis skills and keyword coverage that misses common variations like 'find data' or 'browse datasets'.

Suggestions

Add a leading what-clause naming the platform and operation set, e.g. 'Search, inspect, and download existing datasets from the AgentSociety platform. Use when external datasets are needed for experiments or analysis.'

Include a few more natural trigger phrases users would say, such as 'find data', 'browse datasets', or 'get a dataset'.

Add a distinguishing cue against sibling skills (e.g. 'for existing/remote datasets, not creating or uploading them') to reduce conflict with agentsociety-create-dataset and generic analysis skills.

DimensionReasoningScore

Specificity

The description lists three concrete actions — 'searched, inspected, or downloaded' — against a named domain (external datasets), matching the 'several specific actions; minor gaps' anchor. It is not 5 because coverage is incomplete ('inspected' is generic and sub-operations are unspecified), and not 3 because three concrete actions exceed the '1-2 concrete actions' anchor.

4 / 5

Completeness

An explicit 'Use when...' trigger clause is present, and the 'what' is conveyed through the embedded actions 'searched, inspected, or downloaded' — both elements exist but compressed into one sentence, matching 'both what and when; when could be more explicit'. It is not 5 because the what and when are not clearly separated with concrete trigger phrases, and not 2/3 because both are explicitly stated rather than missing or merely implied.

4 / 5

Trigger Term Quality

Natural user phrasing is present — 'external datasets', 'searched', 'downloaded', 'experiments or analysis' — giving good keyword coverage. It falls short of 5 because common variations users would say ('find data', 'browse datasets', 'data') are absent, and it exceeds 3 since multiple relevant keywords are included.

4 / 5

Distinctiveness Conflict Risk

'External datasets... for experiments or analysis' is somewhat specific but could still overlap with generic data-analysis or data-fetching skills, and it does not distinguish consuming existing datasets from the sibling create/upload skill or name the platform. It is not 4 because overlap risk with closely related data skills is more than minor; not 2 because the dataset-consumption framing is reasonably scoped.

3 / 5

Total

15

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
tsinghua-fib-lab/AgentSociety
Reviewed

Table of Contents

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