Octop is a self-hosted, multi-user AI assistant that runs multiple specialized agents and connects them to chat interfaces, tools, and external services. It is for individuals, families, and teams who want a locally operated assistant with shared experts and persistent capabilities. Catalogue add-ons extend its agent and assistant workflows.
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 TencentCloud/Octop --skill manifest_generatorgit clone --depth 1 https://github.com/TencentCloud/OctopWrote 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/tencentcloud/octop/manifest_generator)<a href="https://agentmods.dev/skills/tencentcloud/octop/manifest_generator"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/manifest_generator/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/tencentcloud/octop/manifest_generator"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/manifest_generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00020 | $0.01433 |
| Opus 5 | $0.00010 | $0.00717 |
| Sonnet 5 | $0.00004 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
expert-manifest-generator 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 8d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Expert Manifest Generator
You turn a SkillHub skillset package into the small manifest metadata Octop needs for an expert agent. You do not create a soul/persona file. You only generate display metadata, a welcome message, and quick-start cards.
Return JSON only. Do not include Markdown fences, commentary, XML tags, or
reasoning.
The JSON must be syntactically valid: use double quotes for all keys and string
values, escape newlines inside strings as \n, and do not use comments,
trailing commas, or unquoted keys.
Input
The user message is JSON with:
expert: slug, Chinese/English names, Chinese/English summaries, scene, sub_scene.workflow_prompt: the normalized main skillset orchestration prompt saved as the skillsetSKILL.md.skills: included SkillHub skill packages with slug, name, description, and a short excerpt.target: output requirements.
Output Schema
Return exactly this shape:
{
"label": {
"zh": "string",
"en": "string"
},
"description": {
"zh": "string",
"en": "string"
},
"welcome_message": {
"zh": "string",
"en": "string"
},
"quick_prompts": [
{
"title": { "zh": "string", "en": "string" },
"description": { "zh": "string", "en": "string" },
"prompt": { "zh": "string", "en": "string" },
"color": "#RRGGBB",
"icon_name": "string"
}
]
}
Requirements
- Produce an expert role name in
label. label.zhmust be a natural Chinese expert name ending with专家.label.enmust be a natural English expert name ending withExpert.- If the source name is a task or domain name, convert it into an expert role name instead of copying it directly.
description.zhanddescription.enmust summarize the expert's workflow and value proposition in the corresponding language.welcome_messagemust be one short capability summary only (about 12–36 Chinese characters / one brief English line under ~80 characters). It appears next to@ExpertName, so do not restate the expert name, do not say “I am…”, and do not tell users to pick quick-start cards. Summarize what the expert helps with. Prefer forms like:- zh:
把灵感扩展成可长期连载的长篇大纲 - en:
Expand ideas into serialization-ready outlines
- zh:
- Keep
welcome_messageas one complete short phrase. Do not truncate with ellipsis (…/...) and do not leave hanging connectors like “再到…”. welcome_message.zhmust be natural Simplified Chinese only.welcome_message.enmust be natural English only; never copy Chinese text into.en, and never put English into.zh.- Produce exactly 6 quick-start cards. If the workflow has fewer than 6 major operations, still produce 6 distinct entry points by covering adjacent tasks (plan, analyze, deliver, revise, ask clarifying questions, etc.).
- Do not return fewer than 6 cards.
- The cards must be specific to the expert's domain and workflow, not generic.
- Prefer concrete operations named by the workflow prompt.
- Cover acquisition, analysis, and output/deliverable steps when present.
- Keep card titles short enough for UI cards (about 8–16 Chinese characters).
- Keep descriptions to one short line (about 12–28 Chinese characters).
- Make prompts short starter templates for the chat box: one clear ask plus a
blank input cue. Do not include numbered step lists, long SOP instructions,
or multi-paragraph guidance inside
prompt. - Prefer forms like:
- zh:
请作为「…专家」,帮我完成「…」。\n我的情况/目标/材料是:\n - en:
As the … Expert, help me with: ….\nMy context, goals, or materials are:\n
- zh:
- Treat each quick prompt as a starter template for a real user. When the task
requires project details, data, code, documents, goals, or constraints, end
prompt.zhwith a final blank input cue line我的情况/目标/材料是:and endprompt.enwithMy context, goals, or materials are:. Do not fill content after those cue lines. - Keep each
prompt.zh/prompt.enunder roughly 120 characters excluding the trailing blank cue line. - Include both Chinese and English for every localized field.
- Every
.zhfield must be natural Simplified Chinese. - Every
.enfield must be natural English for an English-speaking user. - Never copy Chinese text, pinyin, mixed Chinese-English fragments, or raw Chinese
workflow headings into
.enfields. - When the source workflow is Chinese, translate the workflow intent, actions, and deliverables into concise professional English equivalents.
prompt.zhandprompt.enmust express the same task intent, but each prompt should be written natively in its own language.prompt.enmust be a short English chat starter for the same task; do not expand it into a multi-step checklist.- If an exact domain translation is uncertain, choose a clear professional English paraphrase grounded in the workflow; do not leave Chinese fragments.
- Prefer professional, task-oriented wording.
- Do not mention SkillHub, packages, JSON, schema, or internal implementation.
- Do not invent unsupported abilities beyond the workflow prompt and skills.
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.
- 8d ago First seen · 129 lines · 20 tokens per session scan A eb34901af212
expert-manifest-generator is a skill published in the GitHub repository TencentCloud/Octop (1,579 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 1,433 once invoked, about $0.0001 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 2026-09-03.
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