LangBot is an open-source platform for connecting language models to instant-messaging services so developers can build chatbots that converse, use tools, and execute tasks. It supports bot management, knowledge bases, plugins, and multiple chat platforms, and the catalogue entries provide skills, MCP tools, and instructions for working with it.
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.
npx skills add langbot-app/LangBot --skill langbot-skills-maintenancegit clone --depth 1 https://github.com/langbot-app/LangBotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langbot-app/langbot/langbot-skills-maintenance)<a href="https://agentmods.dev/skills/langbot-app/langbot/langbot-skills-maintenance"><img src="https://agentmods.dev/badge/skills/langbot-app/langbot/langbot-skills-maintenance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/langbot-app/langbot/langbot-skills-maintenance"><img src="https://agentmods.dev/badge/skills/langbot-app/langbot/langbot-skills-maintenance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00539 |
| Opus 5.5 | $0.00020 | $0.00216 |
| Sonnet 5.5 | $0.00010 | $0.00108 |
| Haiku 4.5 | $0.00005 | $0.00054 |
Grade A, and why
langbot-skills-maintenance 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangBot Skills Maintenance
Use this skill before changing reusable assets in this repository.
Workflow
- Read
AGENTS.md,skills/.env, and the relevant existing skill files. - Classify the change:
SKILL.mdfor routing and concise operating rules.references/*.mdfor canonical detailed workflows.cases/*.yamlfor executable test-plan skeletons.suites/*.yamlfor reusable groups of case ids.fixtures/fixtures.jsonfor deterministic fixture readiness metadata.reports/evidence/<run-id>/automation-result.jsonas automation output andreports/evidence/<run-id>/result.jsonas final judgment output; neither is a catalog asset to commit.troubleshooting/*.yamlfor one reusable failure mode.
- Search existing assets before adding new files:
rg "<feature|error|case id>" skillsbin/lbs case listbin/lbs suite listbin/lbs fixture list
- Put detail in one canonical place and link to it from cases or routing bullets.
- Run the checks in
AGENTS.mdafter edits.
Entropy Rules
- Prefer extending an existing reference or troubleshooting entry when the root cause is the same.
- Keep cases short: setup, action, evidence, pass/fail checks. Do not paste long prompts or debug transcripts when a reference exists.
- Put machine-checkable inputs in
env,automation_env, or fixtures; put operator-confirmed assumptions inpreconditionssotest plancan surfacemanual_check. - Keep suites short: title, intent, tags, and ordered case ids. Do not duplicate case steps inside a suite.
- Keep fixture manifests factual: id, title, path, kind, and related case ids. Do not encode environment-specific absolute paths.
- Keep troubleshooting entries narrow: symptoms, patterns, likely causes, fixes, related assets.
- Do not hardcode local ports, browser profile paths, secrets, tokens, or provider keys.
- Use
bin/lbs index --checkto verify the committed index is current without writing it; runbin/lbs indexwhen the index needs regeneration.
What ships with it
1 file 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.
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.
- 1mo ago First seen · 41 lines · 49 tokens per session scan A ffcef2dfec7b
langbot-skills-maintenance is a skill published in the GitHub repository langbot-app/LangBot (18,191 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 539 once invoked, about $0.0002 per session on Opus 5.5. 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.
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