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/apiliumcode/mayros/prosenpx skills add ApiliumCode/mayros --skill prosegit clone --depth 1 https://github.com/ApiliumCode/mayrosWhat 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 | $0.00036 | $0.03554 |
| Opus 5 | $0.00018 | $0.01777 |
| Sonnet 5 | $0.00007 | $0.00711 |
| Haiku 4.5 | $0.00004 | $0.00355 |
Grade A, and why
prose scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Remote fetch** == Mayros `web_fetch` (or `exec` with curl when POST is required) This is a copy
95% identical to prose — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenProse Skill
OpenProse is a programming language for AI sessions. LLMs are simulators—when given a detailed system description, they don't just describe it, they simulate it. The prose.md specification describes a virtual machine with enough fidelity that a Prose Complete system reading it becomes that VM. Simulation with sufficient fidelity is implementation. You are the Prose Complete system.
Mayros Runtime Mapping
- Task tool in the upstream spec == Mayros
sessions_spawn - File I/O == Mayros
read/write - Remote fetch == Mayros
web_fetch(orexecwith curl when POST is required)
When to Activate
Activate this skill when the user:
- Uses ANY
prosecommand (e.g.,prose boot,prose run,prose compile,prose update,prose help, etc.) - Asks to run a
.prosefile - Mentions "OpenProse" or "prose program"
- Wants to orchestrate multiple AI agents from a script
- Has a file with
session "..."oragent name:syntax - Wants to create a reusable workflow
Command Routing
When a user invokes prose <command>, intelligently route based on intent:
| Command | Action |
|---|---|
prose help |
Load help.md, guide user to what they need |
prose run <file> |
Load VM (prose.md + state backend), execute the program |
prose run handle/slug |
Fetch from registry, then execute (see Remote Programs below) |
prose compile <file> |
Load compiler.md, validate the program |
prose update |
Run migration (see Migration section below) |
prose examples |
Show or run example programs from examples/ |
| Other | Intelligently interpret based on context |
Important: Single Skill
There is only ONE skill: open-prose. There are NO separate skills like prose-run, prose-compile, or prose-boot. All prose commands route through this single skill.
What ships with it
60 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.
- alt-borges.md 7.0 KB
- alts/arabian-nights.md 9.9 KB
- alts/borges.md 11 KB
- alts/folk.md 9.9 KB
- alts/homer.md 9.4 KB
- alts/kafka.md 11 KB
- compiler.md 81 KB
- examples/01-hello-world.prose 118 B
- examples/02-research-and-summarize.prose 355 B
- examples/03-code-review.prose 821 B
- examples/04-write-and-refine.prose 643 B
- examples/05-debug-issue.prose 738 B
- examples/06-explain-codebase.prose 786 B
- examples/07-refactor.prose 830 B
- examples/08-blog-post.prose 840 B
- examples/09-research-with-agents.prose 890 B
- examples/10-code-review-agents.prose 1.0 KB
- examples/11-skills-and-imports.prose 888 B
- examples/12-secure-agent-permissions.prose 1.2 KB
- examples/13-variables-and-context.prose 1.9 KB
- examples/14-composition-blocks.prose 1.1 KB
- examples/15-inline-sequences.prose 647 B
- examples/16-parallel-reviews.prose 536 B
- examples/17-parallel-research.prose 565 B
- examples/18-mixed-parallel-sequential.prose 774 B
- examples/19-advanced-parallel.prose 2.3 KB
- examples/20-fixed-loops.prose 670 B
- examples/21-pipeline-operations.prose 1.2 KB
- examples/22-error-handling.prose 1.3 KB
- examples/23-retry-with-backoff.prose 1.5 KB
- examples/24-choice-blocks.prose 2.9 KB
- examples/25-conditionals.prose 3.0 KB
- examples/26-parameterized-blocks.prose 3.1 KB
- examples/27-string-interpolation.prose 3.2 KB
- examples/28-automated-pr-review.prose 1.4 KB
- examples/28-gas-town.prose 48 KB
- examples/29-captains-chair.prose 7.7 KB
- examples/30-captains-chair-simple.prose 1.3 KB
- examples/31-captains-chair-with-memory.prose 4.9 KB
- examples/33-pr-review-autofix.prose 4.4 KB
- examples/34-content-pipeline.prose 5.2 KB
- examples/35-feature-factory.prose 7.7 KB
- examples/36-bug-hunter.prose 6.2 KB
- examples/37-the-forge.prose 37 KB
- examples/38-skill-scan.prose 16 KB
- examples/39-architect-by-simulation.prose 9.3 KB
- examples/40-rlm-self-refine.prose 752 B
- examples/41-rlm-divide-conquer.prose 917 B
- examples/42-rlm-filter-recurse.prose 1.2 KB
- examples/43-rlm-pairwise.prose 1.2 KB
- examples/44-run-endpoint-ux-test.prose 8.2 KB
- examples/45-plugin-release.prose 4.5 KB
- examples/45-run-endpoint-ux-test-with-remediation.prose 19 KB
- examples/46-run-endpoint-ux-test-fast.prose 4.9 KB
- examples/46-workflow-crystallizer.prose 7.9 KB
- examples/47-language-self-improvement.prose 11 KB
- examples/48-habit-miner.prose 13 KB
- examples/49-prose-run-retrospective.prose 6.5 KB
- examples/README.md 14 KB
- examples/roadmap/iterative-refinement.prose 464 B
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.
- 2d ago First seen · 324 lines · 36 tokens per session scan A 50e217f62b26
prose is a skill published in the GitHub repository ApiliumCode/mayros (12 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 3,554 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to prose, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
campaign
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dev-loop
Pull the next task from a platform Ralph Loop queue (via the devloop MCP bridge) and drive it to a verified, committed, reported outcome. One task per invocation — this is a Ralph-pattern loop body designed to be driven repeatedly by /loop /dev-loop.
improve
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gen-tests
Generate RSpec request specs for untested controllers and service specs for untested services.
verify
Run targeted verification based on what changed since last commit.
audit
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