BC Agent Skills Framework Explorer
AI-powered assistant for exploring, understanding, and building with AI agent skills frameworks — covers Anthropic agent-skills, OpenAI agent SDKs, LangChain tools, CrewAI protocols, and open-source agent skill marketplaces. Built for AI developers and agent builders. Keywords: agent-skills, AI agent framework, LangChain tools, CrewAI, OpenAI Agents SDK, Claude Agent SDK, agent tool ecosystem, skills marketplace, MCP tools, n8n agent nodes, agent protocol.
As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
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 · 0
✓ No critical or high findings
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 50/100
- 0Result and completion. Does not say what the result is
- 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
- 40Consistency. Frontmatter name (Agent Skills Framework Explorer) differs from the folder (agent-skills-framework-explorer)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4654 tokens
- 100Steps. 70 steps
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
- -2187 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 460: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 70 items
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.