langchain-core-workflow-b
Build LangChain agents with tool calling for autonomous task execution. Use when creating AI agents, implementing tool/function calling, binding tools to models, or building autonomous multi-step workflows. Trigger: "langchain agents", "langchain tools", "tool calling", "create agent", "function calling", "createToolCallingAgent".
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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npx tessl skill review --optimize ./plugins/saas-packs/langchain-pack/skills/langchain-core-workflow-b/SKILL.mdQuality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-structured skill description with strong trigger term coverage and clear completeness, explicitly addressing both what the skill does and when to use it. The main weakness is that the specificity of capabilities could be improved by listing more concrete actions beyond the general 'build agents with tool calling'. Overall, it would perform well in a multi-skill selection scenario.
Suggestions
Add more specific concrete actions to improve specificity, e.g., 'Build LangChain agents with tool calling, define custom tools, configure agent executors, handle multi-step reasoning chains, and manage agent memory for autonomous task execution.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (LangChain agents with tool calling) and a general action (build agents for autonomous task execution), but doesn't list multiple specific concrete actions like binding tools to models, creating custom tools, handling agent memory, etc. The actions mentioned are somewhat generic. | 2 / 3 |
Completeness | Clearly answers both 'what' (build LangChain agents with tool calling for autonomous task execution) and 'when' (explicit 'Use when' clause with multiple trigger scenarios, plus a dedicated 'Trigger' list). Both components are explicitly stated. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'langchain agents', 'langchain tools', 'tool calling', 'create agent', 'function calling', and the specific API name 'createToolCallingAgent'. These are terms users would naturally use when seeking this functionality. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific LangChain agent and tool-calling focus. The explicit trigger terms like 'createToolCallingAgent' and 'langchain agents' create a clear niche that is unlikely to conflict with general coding skills or other AI framework skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with complete executable code examples covering the full agent workflow. Its main weaknesses are the lack of validation checkpoints between steps (important for agent debugging) and some content bloat from including both a Python equivalent and an alternative approach (bindTools) inline rather than as separate references. The error handling table is a nice practical addition.
Suggestions
Add validation checkpoints between steps, e.g., 'Test each tool individually before passing to AgentExecutor' with a quick verification snippet.
Move the Python equivalent and Step 6 (bindTools alternative) to separate referenced files to reduce the main skill's length and improve progressive disclosure.
Add a brief troubleshooting feedback loop: 'If agent produces unexpected results, enable verbose mode, inspect intermediateSteps, and verify tool schemas match expected inputs.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code examples, but includes some unnecessary elements like the Python equivalent section (which adds significant length for a secondary language), inline comments explaining obvious things, and the mock weather data that pads the tool definitions. The overview and prerequisites are concise, but overall the skill could be tightened. | 2 / 3 |
Actionability | Every step provides fully executable, copy-paste ready TypeScript code with concrete examples. Tool definitions include complete Zod schemas, the agent setup is complete with all imports, and the invocation examples show expected outputs. The error handling table provides specific fixes for specific errors. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced from tool definition through agent creation, execution, memory, streaming, and direct binding. However, there are no validation checkpoints — no guidance on verifying tool definitions work before wiring them into the agent, no error recovery feedback loops, and no explicit verification that the agent is functioning correctly before adding memory or streaming. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and a logical progression, and it references external resources and a next-steps skill. However, with no bundle files, the entire skill is a monolithic ~200-line document. The Python equivalent and the direct binding approach (Step 6) could reasonably be split into separate reference files to keep the main skill focused. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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