What is byted-contextsearch-operator?

An administration tool for Volcano Engine ContextSearch, a service for organizing data sources and configuring search-based applications. It manages settings in the service’s control panel and control system.

Use it to create search scenes, connect data sources, configure indexers and search settings, manage models and deployments, and administer networks, specifications, users, tools, variables, chats, and API keys.

It replaces scattered manual console work with documented commands for setting up and maintaining ContextSearch resources.

byted-contextsearch-operator — Skill for Claude Code and Codex by byte

About the project

bytedance/agentkit-samples is a collection of examples and tutorials for Volcengine AgentKit, an AI-agent development platform for building, deploying, and operating agent applications. Developers use the samples to learn agent creation, multi-agent collaboration, memory, retrieval, MCP integrations, media generation, customer service, and other workflows. The catalogue skills provide agent workflows based on these examples.

bytedance/agentkit-samples · 449 stars · on GitHub

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.

Any agent

npx skills add bytedance/agentkit-samples --skill byted-contextsearch-operator

Clone the repo

git clone --depth 1 https://github.com/bytedance/agentkit-samples

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-contextsearch-operator.svg)](https://agentmods.dev/skills/bytedance/agentkit-samples/byted-contextsearch-operator)

Your own site

<a href="https://agentmods.dev/skills/bytedance/agentkit-samples/byted-contextsearch-operator"><img src="https://agentmods.dev/badge/skills/bytedance/agentkit-samples/byted-contextsearch-operator.svg" alt="Measured on agentmods" height="20"></a>

Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 937 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00072	$0.00937
Opus 5	$0.00036	$0.00468
Sonnet 5	$0.00014	$0.00187
Haiku 4.5	$0.00007	$0.00094

Measured 6d ago against content hash 7cdd86d876a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

byted-contextsearch-operator 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 6d ago.

The scan reads SKILL.md. This mod also ships 10 executable files (scripts/common.py, scripts/contextsearch_cli.py, scripts/control.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/byted-contextsearch-operator/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Volcano Engine ContextSearch

Manage ContextSearch application-console and control-plane operations on Volcano Engine.

Quick Start

Use the bundled CLIs (always run via the skill venv):

{baseDir}/venv/bin/python {baseDir}/scripts/control.py <command>

If {baseDir}/venv does not exist:

python3 -m venv {baseDir}/venv
{baseDir}/venv/bin/pip install -r {baseDir}/requirements.txt

See references/control_plane.md for workflows and examples.

Low-level fallback (use only when goal-based commands do not cover the task):

{baseDir}/venv/bin/python {baseDir}/scripts/contextsearch_cli.py <namespace> <command>

See references/control_tools.md and references/control_coverage.md.

Available operations

Use goal-based workflows and console action commands to create AgenticSearch and manage scenes, data sources, indexers, search configs, Agentic tools/skills/variables/chats/users, models, deployments, PSR resources, network, specs, and API keys. → See references/control_plane.md.

Low-level tools: Use scripts/contextsearch_cli.py console or openapi only when control.py does not cover a specific console action. → See references/control_tools.md.

Out of scope

Operating non-Volcano-Engine ContextSearch deployments.
Runtime RAG, image, or video retrieval and chat against published context endpoints.
Generic web search, research, or best-practice lookup; use an available web-search capability instead.
Designing full application-level RAG/AgenticSearch architecture beyond the fields needed to run the provided commands.
Guessing unknown request bodies, IDs, or credentials. If a body shape is not clear, inspect the relevant console request shape or ask the user for required fields.

Rules

Common

Execution environment: Always use {baseDir}/venv/bin/python to run scripts.
Control-plane authentication: Console operations require VOLCENGINE_AK and VOLCENGINE_SK. Use VOLCENGINE_REGION only when the default cn-beijing is not correct.
Script usage: Use scripts/control.py for console operations. Use scripts/contextsearch_cli.py only as a last resort when control.py does not cover the console action.
Console parity: For AgenticSearch creation, use control.py create-agentic-search; it follows the console chain ListAgenticSceneTemplate -> CheckBuiltinDeployment -> DeployBuiltinDeployment -> CreateAgenticScene.
Output handling: Commands return JSON. Make decisions from status, goal, data, and steps_completed.
Language (strict): Always reply in the user's language. If the user's message contains Chinese characters, reply in Chinese. Keep commands/flags/code in English.

Read the full file on GitHub · 80 lines

Files

What ships with it

22 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.

6d ago First seen · 80 lines · 72 tokens per session scan A 7cdd86d876a5

Subscribe to this mod's changes

byted-contextsearch-operator is a skill published in the GitHub repository bytedance/agentkit-samples (449 stars, last pushed 2d ago), licensed Apache-2.0. It adds 72 tokens to every session and 937 once invoked, about $0.0004 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 2026-08-31.

Backend and APIs agent ai samples volcengine

langbot-dev

Develop, build, and debug the LangBot core backend and web frontend. Use when working inside the LangBot repository — backend (Python/Quart, src/langbot/pkg), the Vite/React web UI, HTTP API controllers/services, Alembic migrations, or the MCP server. Covers the dev environment (uv, pnpm), repo layout, the API auth…

langbot-app/LangBot · 136 tokens

blind-sqli

Blind SQL injection under hostile WAF — manual bypass playbook for when sqlmap fails because common tokens (SUBSTRING, IF, AND, WHERE, single quotes) are filtered. Covers token-fingerprinting probe loops, arithmetic-multiplication boolean evaluation, hex-encoded literals, and exponential-probe binary search. Loaded on…

PurpleAILAB/Decepticon · 88 tokens

graphql

GraphQL exploitation — introspection, injection, authorization bypass, and data exfiltration through GraphQL APIs.

PurpleAILAB/Decepticon · 24 tokens

groq-inference

Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok/s). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.

synthetic-sciences/openscience · 77 tokens

golang-graphql

Implements GraphQL APIs in Golang using gqlgen or graphql-go. Apply when building GraphQL servers, designing schemas, writing resolvers, handling subscriptions, or integrating GraphQL with existing Go HTTP services. Also apply when the codebase imports github.com/99designs/gqlgen or github.com/graph-gophers/graphql-go.

samber/cc-skills-golang · 77 tokens

golang-grpc

Provides gRPC usage guidelines, protobuf organization, and production-ready patterns for Golang microservices. Use when implementing, reviewing, or debugging gRPC servers/clients, writing proto files, setting up interceptors, handling gRPC errors with status codes, configuring TLS/mTLS, testing with bufconn, or…

samber/cc-skills-golang · 72 tokens