Content
85%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, well-sequenced security hunting skill with concrete payloads and explicit validation feedback loops. Its main weakness is progressive disclosure: it is a long single-file document whose sizable attack catalogs would benefit from being split into referenced bundle files.
Suggestions
Move the OData and NSwag/Swagger attack catalogs into separate reference files (e.g. references/odata-bypasses.md, references/swagger-exposure.md) and keep SKILL.md as an overview that links to them one level deep.
Trim the OData/NSwag introductory paragraphs to the minimum context needed, since Claude already understands these technologies.
Consider extracting reusable payload wordlists (mass-assignment fields, __proto__ gadgets) into a scripts/ or references/ file to shorten the inline body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly dense and actionable with minimal padding, but the OData and NSwag introductory paragraphs ('OData (Open Data Protocol) is the query layer behind...') explain context Claude largely already knows and could be trimmed. | 4 / 5 |
Actionability | Copy-paste-ready payloads, curl probes, jq one-liners, and concrete parameter patterns cover the common cases across every attack class with no pseudocode. | 5 / 5 |
Workflow Clarity | Multi-step hunt sequences are explicitly numbered with validation checkpoints ('Do not stop at a 200 response to __proto__; prove that polluted prototype state reaches a later operation', 'use errors as routing feedback') and escalation gates, satisfying the destructive/batch feedback-loop requirement. | 5 / 5 |
Progressive Disclosure | The body is a ~260-line monolith with clear section headers but no bundle files or external references; large standalone attack catalogs (OData, NSwag) that belong in separate reference files are inlined rather than split out. | 3 / 5 |
Total | 17 / 20 Passed |