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 agentmods add skills/macpaw/composablepipelines/composable-pipelinesnpx skills add MacPaw/ComposablePipelines --skill composable-pipelinesgit clone --depth 1 https://github.com/MacPaw/ComposablePipelinesWrote 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/macpaw/composablepipelines/composable-pipelines)<a href="https://agentmods.dev/skills/macpaw/composablepipelines/composable-pipelines"><img src="https://agentmods.dev/badge/skills/macpaw/composablepipelines/composable-pipelines.svg" alt="Measured on agentmods" 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 | $0.00109 | $0.01586 |
| Opus 5 | $0.00055 | $0.00793 |
| Sonnet 5 | $0.00022 | $0.00317 |
| Haiku 4.5 | $0.00011 | $0.00159 |
Grade A, and why
composable-pipelines 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Composable Pipelines
A runtime-agnostic stack for AI pipelines in Swift: a declarative DSL lowers to a Codable
AST, a compiler emits an execution graph, and an observable walker runs it — while an
Executor you supply decides what "run a model" means.
Quick start
import ComposablePipelines
struct Summary: Pipeline {
typealias Output = String
let document: String
@State var keyPoints = ""
@State var summary = ""
var body: some Pipeline {
$keyPoints.set { // step 1 → slot
Model<String>().systemPrompt("Extract the 5 key points.").message(document)
}
$summary.set { // step 2 reads step 1
Model<String>().systemPrompt("Summarize from these points.").input { $keyPoints.get() }
}
$summary.get() // pipeline output
}
}
// Run it (linear flow): compile → walk.
let graph = PipelineCompiler().compile(pipeline.loweredGraph())
let result = try await PipelineWalker(executor: MyExecutor()).run(graph: graph) { event in print(event) }
let text = try JSONDecoder().decode(String.self, from: result) // ExecutionValue == Data (JSON)
Authoring cheatsheet
- Pipeline:
struct X: Pipeline { typealias Output = T; @State var … ; var body: some Pipeline { … } }. The graph is inferred from the@Stateslots each step reads/writes — never wired by hand. - State:
@State var s = ""→ write with$s.set { <pipeline> }or$s.set(value); read with$s.get(). Endbodywith the output slot's.get(). - Model (output-only generic):
Model<Output>()then chain.systemPrompt(_),.input { $slot.get() }or.message(staticValue), and optionally.tools([…]),.temperature(_),.maxTokens(_),.requirements(ModelSelectionRequirements(traits: […])). - Primitives:
Guardrail(input, rules:allowed:blocked:),While(condition:) { … },Group { … },ForEach(in: xs) { x in … },Summarize(text: $slot, maxTokens:),ClientTask(input: $slot) { value in … },From(provider, query: $slot),Self.return(value). - Control flow: use native
if/switchon produced state (e.g.if verdict == "trivial").
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 · 127 lines · 0 tokens per session scan A 1192937d6fbe
composable-pipelines is a skill published in the GitHub repository MacPaw/ComposablePipelines (24 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 109 tokens to every session and 1,586 once invoked, about $0.0005 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 2026-08-30.
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