Borrowing it
Nothing to install: this file belongs to MacPaw/ComposablePipelines. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MacPaw/ComposablePipelines/main/.claude/skills/composable-pipelines/SKILL.mdgit 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/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/macpaw/composablepipelines/composable-pipelines"><img src="https://agentmods.dev/badge/skills/macpaw/composablepipelines/composable-pipelines.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 | $0.00000 | $0.01586 |
| Opus 5.5 | $0.00000 | $0.00634 |
| Sonnet 5.5 | $0.00000 | $0.00317 |
| Haiku 4.5 | $0.00000 | $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 2mo 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.
- 2mo ago First seen · 127 lines · 0 tokens per session scan A 1192937d6fbe
composable-pipelines is a skill published in the GitHub repository MacPaw/ComposablePipelines (26 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,586 tokens. 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.
composable-pipelines compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| composable-pipelinesMacPaw/ComposablePipelines | Skill | 26 | 1mo ago | Apache-2.0 | A | 0 | Claude Code |
| autorag-setupMarker-Inc-Korea/AutoRAG | Skill | 5.3k | yesterday | Apache-2.0 | A | 75 | Claude Code, Codex |
| batch-processing-clinical-textmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 161 | Claude Code |
| bridging-presidio-and-spacymaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 154 | Claude Code |
| exporting-to-fhirmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 163 | Claude Code |
| autoragMarker-Inc-Korea/AutoRAG | Skill | 5.3k | yesterday | Apache-2.0 | A | 68 | Claude Code, Codex |
| autorag-lite-setupMarker-Inc-Korea/AutoRAG | Skill | 5.3k | yesterday | Apache-2.0 | A | 65 | Claude Code, Codex |