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 airmcp-com/mcp-standards --skill stream-chaingit clone --depth 1 https://github.com/airmcp-com/mcp-standardsWrote 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/airmcp-com/mcp-standards/stream-chain)<a href="https://agentmods.dev/skills/airmcp-com/mcp-standards/stream-chain"><img src="https://agentmods.dev/badge/skills/airmcp-com/mcp-standards/stream-chain/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/airmcp-com/mcp-standards/stream-chain"><img src="https://agentmods.dev/badge/skills/airmcp-com/mcp-standards/stream-chain.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 | $0.00018 | $0.02786 |
| Opus 5 | $0.00009 | $0.01393 |
| Sonnet 5 | $0.00004 | $0.00557 |
| Haiku 4.5 | $0.00002 | $0.00279 |
Grade A, and why
stream-chain 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 5d 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.
This is a copy
100% identical to stream-chain — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 564 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stream-Chain Skill
Execute sophisticated multi-step workflows where each agent's output flows into the next, enabling complex data transformations and sequential processing pipelines.
Overview
Stream-Chain provides two powerful modes for orchestrating multi-agent workflows:
- Custom Chains (
run): Execute custom prompt sequences with full control - Predefined Pipelines (
pipeline): Use battle-tested workflows for common tasks
Each step in a chain receives the complete output from the previous step, enabling sophisticated multi-agent coordination through streaming data flow.
Quick Start
Run a Custom Chain
claude-flow stream-chain run \
"Analyze codebase structure" \
"Identify improvement areas" \
"Generate action plan"
Execute a Pipeline
claude-flow stream-chain pipeline analysis
Custom Chains (run)
Execute custom stream chains with your own prompts for maximum flexibility.
Syntax
claude-flow stream-chain run <prompt1> <prompt2> [...] [options]
Requirements:
- Minimum 2 prompts required
- Each prompt becomes a step in the chain
- Output flows sequentially through all steps
Options
| Option | Description | Default |
|---|---|---|
--verbose |
Show detailed execution information | false |
--timeout <seconds> |
Timeout per step | 30 |
--debug |
Enable debug mode with full logging | false |
How Context Flows
Each step receives the previous output as context:
Step 1: "Write a sorting function"
Output: [function implementation]
Step 2 receives:
"Previous step output:
[function implementation]
Next task: Add comprehensive tests"
Step 3 receives:
"Previous steps output:
[function + tests]
Next task: Optimize performance"
Examples
Basic Development Chain
claude-flow stream-chain run \
"Write a user authentication function" \
"Add input validation and error handling" \
"Create unit tests with edge cases"
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.
- 5d ago First seen · 564 lines · 18 tokens per session scan A e99eba739057
stream-chain is a skill published in the GitHub repository airmcp-com/mcp-standards (3 stars, last pushed 9mo ago), licensed MIT. It adds 18 tokens to every session and 2,786 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to stream-chain, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows. Use when piping output between agents/tools as streaming JSON or building a sequential transform pipeline.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.