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agentsociety-analysis

Use when an experiment run has completed and the user wants rigorous interpretation, claim-driven charts, bilingual reports, or cross-hypothesis synthesis from simulation data. Also use when multiple charts or PNG/JPG assets must be assembled into one labeled composite figure. Requires high-quality narrative and evidence traceability, not only harness gate PASS.

76

Quality

96%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

92%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, highly actionable overview that delegates detail to clearly labeled one-level-deep references and frames a validation-gated multi-stage workflow with feedback loops. Minor conciseness trim is possible but it does not materially detract from quality.

DimensionReasoningScore

Conciseness

The body is dense and mostly actionable with minimal conceptual re-explanation, but the harness-vs-LLM table and a few explanatory sentences ('Use the Python interpreter from `.env`...') could be trimmed slightly. It sits above the 'mostly efficient' anchor but does not fully earn the lean-every-token-counts level.

4 / 5

Actionability

Provides copy-paste-ready CLI commands with full argument signatures in the Quick Reference table, concrete directory contracts, a numbered produce flow, and a Common Mistakes table with specific fixes — covering the common cases executably.

5 / 5

Workflow Clarity

Sequences a six-stage pipeline (frame→explore→claims→refine→produce→synthesis) with a diagram, explicit per-phase validate-* gates, and a producer→reviewer feedback loop ('loop until PASS'), satisfying the validation-checkpoint and feedback-loop requirements.

5 / 5

Progressive Disclosure

Acts as a clear overview with a dedicated 'Shared References' section signaling ~23 one-level-deep reference files, six stage files, checklists, subagent prompts, and assets, each with a descriptive label and easy navigation.

5 / 5

Total

19

/

20

Passed

Description

100%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 third-person, concise, and explicitly covers both capabilities and trigger conditions with concrete, natural language. It is well-distinguished from sibling skills and free of vague fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'rigorous interpretation, claim-driven charts, bilingual reports, or cross-hypothesis synthesis' plus 'assembled into one labeled composite figure' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' (interpretation, charts, reports, synthesis, composite figures) and 'when' via two concrete 'Use when...' trigger clauses, matching the top anchor.

5 / 5

Trigger Term Quality

Includes natural user-facing terms ('experiment run has completed', 'charts', 'bilingual reports', 'simulation data') plus file extensions ('PNG/JPG assets') and synonyms, matching the comprehensive-coverage anchor.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (post-run AgentSociety simulation analysis) with distinctive triggers like 'completed experiment run' and 'harness gate PASS', minimizing overlap with other skills.

5 / 5

Total

20

/

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.

Validation15 / 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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