agentmods.dev

langbot-testing

A testing guide for LangBot's web interface and core product flows. LangBot is an application for building and running AI bots, while a web interface lets you configure and test them in a browser.

In plain words
What is it for?
It helps test chats, model-provider setup, bots, knowledge bases, plugins, skills, MCP tools, agent runners, and performance or reliability scenarios.
Why use it?
It helps verify that product behavior works in a real browser and use backend logs to investigate failures, instead of checking code alone.

Skill for Claude CodeCodex

18k 2d ago A scan 58 tokens original Apache-2.0
View the source file langbot-app/LangBot
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/langbot-app/langbot/langbot-testing
Any agent
npx skills add langbot-app/LangBot --skill langbot-testing
Clone the repo
git clone --depth 1 https://github.com/langbot-app/LangBot

Made for: Claude Code, Codex.

Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 908 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00058 $0.00908
Opus 5 $0.00029 $0.00454
Sonnet 5 $0.00012 $0.00182
Haiku 4.5 $0.00006 $0.00091

Measured 2d ago against content hash becdd878b0ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

langbot-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (fixtures/mcp/qa_mcp_echo_server.py, fixtures/plugins/qa-agent-runner/components/agent_runner/default.py, fixtures/plugins/qa-agent-runner/main.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/skills/langbot-testing/SKILL.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LangBot Testing

Use this skill when an agent needs to verify LangBot behavior through the WebUI instead of only reading code.

Routing

  • General WebUI testing: read references/web-ui-testing.md.
  • Pipeline Debug Chat: read references/pipeline-debug-chat.md.
  • Dify AgentRunner: read references/dify-agent-runner.md.
  • Model provider setup or test button: read references/model-provider-testing.md.
  • Plugin install/runtime/tool/page smoke: read references/plugin-e2e-smoke.md.
  • Local Agent Runner: read references/local-agent-runner.md.
  • Local Agent Runner path coverage: read references/local-agent-runner-coverage.md.
  • Diff-aware AgentRunner QA after code changes: read references/agent-runner-qa-workflow.md.
  • Agent Runner release gate: read references/agent-runner-release-gate.md.
  • Sandbox-backed skill authoring: read references/sandbox-skill-authoring.md.
  • LangRAG knowledge bases: read references/langrag-knowledge-base.md.
  • MCP stdio tool testing: read references/mcp-stdio-testing.md.
  • Performance, reliability, or chaos probes: read references/performance-reliability-testing.md.
  • Drive a live instance over MCP (not raw HTTP): use the langbot-mcp-ops skill — the instance exposes an MCP server at http://<host>:5300/mcp (reuses API keys). Useful for setting up bots/pipelines/models as test fixtures programmatically.
  • Known failures and fixes: read references/troubleshooting.md.
  • Reusable test groups: run bin/lbs suite list and bin/lbs suite plan <suite-id> before manually assembling a case set.

Rules

  • Read ../.env first and use LANGBOT_FRONTEND_URL and LANGBOT_BACKEND_URL instead of hardcoded ports.
  • If a standalone frontend dev server is running, LANGBOT_FRONTEND_URL may point to LANGBOT_DEV_FRONTEND_URL; otherwise it may point to the backend WebUI.
  • Confirm the backend and frontend are actually running before testing.
  • Run bin/lbs fixture check before fixture-heavy MCP, RAG, multimodal, or plugin smoke tests.
  • For runner externalization release checks, run bin/lbs test run agent-runner-release-preflight before the full agent-runner-release-gate suite so configuration blockers are separated from product failures.
  • Read Manual Readiness in bin/lbs test plan <case-id>; manual_check means the declared preconditions or setup still need operator confirmation for this run.
  • Use an authenticated browser profile prepared by langbot-env-setup.
  • Do not expose API keys, OAuth secrets, tokens, or localStorage token values in output.
  • A WebUI test is not complete until the visible UI result is checked against backend logs or network behavior.
  • A performance result is not complete without metrics evidence and a clear split between LangBot overhead and external provider/tool/network time.
  • A chaos or reliability result is not complete until the fault scope, cleanup, and recovery checks are recorded.
  • For a suite, use bin/lbs suite start <suite-id> to create the suite evidence root, per-case directories, and suite-start.json/suite-start.md handoff files; use bin/lbs test result <case-id> to write final per-case result.json, then run bin/lbs suite report <suite-id> --evidence-dir <dir>.
  • Do not mark a case pass until test result --evidence covers every value in the case's evidence_required.
  • For runner-specific Debug Chat cases, use the case-specific pipeline env declared by automation_pipeline_url_env / automation_pipeline_name_env; do not silently reuse a generic LANGBOT_PIPELINE_URL.

Read the full file on GitHub · 45 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 45 lines · 58 tokens per session scan A becdd878b0ee

Subscribe to this mod's changes

langbot-testing is a skill published in the GitHub repository langbot-app/LangBot (17,616 stars, last pushed 2d ago), licensed Apache-2.0. It adds 58 tokens to every session and 908 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

DebuggingLLMMemory and contextTestingagentopenaiqqtelegramwechat
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