BC AI Agent Evaluator
AI-powered agent evaluation and benchmarking assistant — design evaluation suites, run structured assessments (task completion rate, latency, safety, reasoning accuracy), compare multi-agent frameworks (CrewAI, LangChain, AutoGen), generate benchmark reports, and guide developers in selecting the right evaluation methodology. Built for AI engineers, product managers, and ML teams shipping agent-based applications to production. Keywords: AI agent evaluation, agent benchmarking, LLM testing, CrewAI, AutoGen, LangChain, SWE-bench, AgentBench, AI quality assurance, agent reliability.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 3
✓ No critical or high findings
Medium and low: 3
-
low Risky intent
intent-offensive-securitySKILL.md:33Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- **Red Teaming Support** — Design adversarial tests to probe agent safety and edge cases
-
low Risky intent
intent-offensive-securitySKILL.md:49Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)- "agent red teaming"
quoted -
low Risky intent
intent-offensive-securitySKILL.md:162Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- **PromptFoo** — prompt testing and red teaming
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 56/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (AI Agent Evaluator) differs from the folder (ai-agent-evaluator)
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 66 steps
- 100Execution cost. Instruction body is 1853 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 587: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 66 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.