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 frankxai/claude-skills-library --skill stream-chaingit clone --depth 1 https://github.com/frankxai/claude-skills-libraryWrote 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/frankxai/claude-skills-library/stream-chain)<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/stream-chain"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/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/frankxai/claude-skills-library/stream-chain"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/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 | $0.00036 | $0.01277 |
| Opus 5.5 | $0.00014 | $0.00511 |
| Sonnet 5.5 | $0.00007 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00128 |
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 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 — 252 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
Reference
The full detail lives in references/ and loads only when needed:
references/pipelines.md— custom chains, predefined pipelines, custom pipeline definitions & advanced use cases.
Best Practices
1. Clear and Specific Prompts
Good:
"Analyze authentication.js for SQL injection vulnerabilities"
Avoid:
"Check security"
2. Logical Progression
Order prompts to build on previous outputs:
1. "Identify the problem"
2. "Analyze root causes"
3. "Design solution"
4. "Implement solution"
5. "Verify implementation"
3. Appropriate Timeouts
- Simple tasks: 30 seconds (default)
- Analysis tasks: 45-60 seconds
- Implementation tasks: 60-90 seconds
- Complex workflows: 90-120 seconds
4. Verification Steps
Include validation in your chains:
claude-flow stream-chain run \
"Implement feature X" \
"Write tests for feature X" \
"Verify tests pass and cover edge cases"
5. Iterative Refinement
Use chains for iterative improvement:
claude-flow stream-chain run \
"Generate initial implementation" \
"Review and identify issues" \
"Refine based on issues found" \
"Final quality check"
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 · 252 lines · 36 tokens per session scan A 4e017509d684
stream-chain is a skill published in the GitHub repository frankxai/claude-skills-library (47 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 1,277 once invoked, about $0.0001 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 2026-09-03.
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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.