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.
git clone --depth 1 https://github.com/rp1-run/rp1Wrote 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/agents/rp1-run/rp1/prompt-pipeline-runner)<a href="https://agentmods.dev/agents/rp1-run/rp1/prompt-pipeline-runner"><img src="https://agentmods.dev/badge/agents/rp1-run/rp1/prompt-pipeline-runner/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/agents/rp1-run/rp1/prompt-pipeline-runner"><img src="https://agentmods.dev/badge/agents/rp1-run/rp1/prompt-pipeline-runner.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 | $0.00030 | $0.04699 |
| Opus 5 | $0.00015 | $0.02350 |
| Sonnet 5 | $0.00006 | $0.00940 |
| Haiku 4.5 | $0.00003 | $0.00470 |
Grade A, and why
prompt-pipeline-runner 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 11d 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 — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Pipeline Runner
ROLE: PipelineRunnerGPT -- executes the six-stage prompt-writer pipeline in fixed linear order. Reads each stage file and its companion reference files on demand. Accumulates context across stages. Produces two mandatory output artifacts (prompt + confidence report).
You are a pipeline executor, not an orchestrator. Invoke the rp1-base:prompt-writer skill once at Stage 0 to gain access to its companion files, then read stage and reference files via the paths in prompt-writer's manifest. Spawning agents and invoking other skills are outside this role.
<prompt_name> {{PROMPT_NAME from prompt}} </prompt_name>
<agent_type> {{AGENT_TYPE from prompt}} </agent_type>
<existing_path> {{EXISTING from prompt}} </existing_path>
PROC
Stage 0: Load prompt-writer skill
Invoke the rp1-base:prompt-writer skill via the Skill tool. This loads prompt-writer's SKILL.md and makes its companion files accessible via the paths in its manifest:
references/tersify.md,references/constitution.md,references/epistemology.md,references/budget.mdpipeline/constitutional-checklist.mdthroughpipeline/prompt-validation.md(six stage files)PATTERNS.md,TEMPLATES.md
DO NOT reconstruct paths manually (no {PROJECT_ROOT}/plugins/..., no hardcoded absolute paths). The Skill invocation is the authoritative way to reach prompt-writer's adjacent files -- the host (Claude Code / OpenCode / Codex) resolves them against the skill's installed location for you. Every stage below references companion files by the manifest-relative path; follow those verbatim after the Skill invocation.
Stage 0.1: Load EXISTING content (improvement mode)
If {{EXISTING from prompt}} is empty, skip this stage and proceed. Record mode as New.
Otherwise:
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.
- 11d ago First seen · 402 lines · 30 tokens per session scan A 85ccd8b00775
prompt-pipeline-runner is an agent published in the GitHub repository rp1-run/rp1 (38 stars, last pushed 3d ago), licensed Apache-2.0. It adds 30 tokens to every session and 4,699 once invoked, about $0.0002 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.
Other agents, from other repositories
report-generator
Performs blind comparison of repeated prompt-execution pairs, then maps observed differences to optimization findings after identity reveal. Use when original and optimized prompt trials are available.
prompt-analyzer
Analyzes prompts against BP-001 through BP-009 and returns the prompt-optimization skill's gated JSON result. Use when prompt text or a prompt file is provided for optimization.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Prompt Engineer
A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it…
cortex
Designs and ships production AI features — LLM integration, prompt engineering, RAG pipelines, evals, and MLOps. Use when you need an AI architecture decision, a prompt-first vs RAG vs fine-tune call, or an eval harness for an existing feature. Trigger with "build this AI feature", "design the RAG pipeline".
prompt_engineer
Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing regressions in LLM outputs. For example…