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 peteromallet/Arnold --skill _templategit clone --depth 1 https://github.com/peteromallet/ArnoldWrote 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/peteromallet/arnold/_template)<a href="https://agentmods.dev/skills/peteromallet/arnold/_template"><img src="https://agentmods.dev/badge/skills/peteromallet/arnold/_template/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/peteromallet/arnold/_template"><img src="https://agentmods.dev/badge/skills/peteromallet/arnold/_template.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 | $0.00023 | $0.00748 |
| Opus 5.5 | $0.00009 | $0.00299 |
| Sonnet 5.5 | $0.00005 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
new-arnold-pipeline-template 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
New Arnold Pipeline Template
Copy arnold_pipelines/_template/ to a new package under arnold_pipelines/,
rename it (remove the leading underscore), and replace the skeleton native
declarations with real pipeline logic.
This template is native-first. Every package built from it uses
@pipeline, @phase, @decision, parallel, compile_pipeline, and
project_graph to declare a native program and project it into a
Pipeline shell that the runtime executes directly.
Contract
build_pipeline() -> arnold.pipeline.Pipelinereturning a projected shell with a non-nullnative_program.- Module-level metadata:
name,description,driver,entrypoint,arnold_api_version,capabilities. drivermust be("native", "<kind>")(e.g.("native", "project+validate")).supported_modesmust include"native"(e.g.("native",)).- Optional:
default_profile,recommended_profiles.
New packages must be native-first. Do not add _legacy.py, graph
fallback builders, compatibility namespaces, shim packages, or temporary
wrapper modules for new work — those patterns are explicitly disallowed.
Dispatch Substrate, Not Final Composition
The native_program attached to the projected Pipeline is a dispatch
substrate. It describes how the runtime lowers and executes pipeline
topology, but it does not define the final visible compositional
semantics (panel synthesis, join delegation, parallel merge strategy,
subpipeline ownership, or Capsule projection). Those compositional
concerns are deferred to later Megaplan layers above the dispatch
boundary.
Package authors should treat native_program as the execution-level
contract: a non-null program proves the package is native-runnable, but
the exact shape of how composition is rendered for end users, agents, or
Capsules is not finalised at this layer.
Example
from arnold.pipeline import Pipeline, StepResult, compile_pipeline, phase, pipeline, project_graph
@phase(name="draft")
def draft(ctx: object) -> StepResult:
return StepResult(outputs={"draft": "TODO"}, next="publish")
@phase(name="publish")
def publish(ctx: object) -> StepResult:
return StepResult(outputs={"final_artifact": "TODO"}, next="halt")
@pipeline(name="my-pipeline", description="draft → publish")
def my_pipeline_native(ctx: object) -> Any:
state = yield draft(ctx)
state = yield publish(ctx)
return state
def build_pipeline() -> Pipeline:
native = compile_pipeline(my_pipeline_native)
return project_graph(native, key_mode="phase")
What ships with it
4 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.
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 · 91 lines · 23 tokens per session scan A 58814bc1ed99
new-arnold-pipeline-template is a skill published in the GitHub repository peteromallet/Arnold (98 stars, last pushed yesterday), licensed Apache-2.0. It adds 23 tokens to every session and 748 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 2026-09-04.
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