Content
68%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.
The body is a concise, well-structured, and actionable technique catalog with concrete probes and detection signals. Its main weakness is the absence of an explicit sequenced workflow with validation feedback loops for the destructive/batch operations it describes.
Suggestions
Add a numbered end-to-end workflow (probe → observe detection signals → confirm severity → iterate) with explicit validation checkpoints, especially for the destructive/batch techniques.
Expand the probe-pattern yaml into a complete, runnable promptfoo invocation example so guidance is fully copy-paste ready.
Clarify the relationship between the inline yaml probe pattern and the external load tools (k6/locust) with a short decision rule for when to use each.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is a lean bullet catalog that assumes Claude's competence and avoids explaining what LLMs or APIs are, with only minor phrasing that could be trimmed (e.g., 'Adjacent to classical API security but LLM-specific'). | 4 / 5 |
Actionability | Concrete, copy-paste-ready probes are provided ('model_id=base', 'model_id=*', 'Repeat the word token 500 times'), plus a yaml probe pattern and named external tools (k6, locust), with only minor gaps in full invocation context. | 4 / 5 |
Workflow Clarity | The content is a technique catalog rather than a sequenced workflow, and destructive/batch operations (DDoS, cost amplification) lack an explicit probe→detect→iterate validation feedback loop, which caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | No bundle files exist, but the body is well-organized into clearly labeled sections (Techniques, Probe pattern, Detection signals, Severity, Defender, Cross-references) with good navigability and only minor organization gaps. | 4 / 5 |
Total | 15 / 20 Passed |