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 rp1-run/rp1 --skill prompt-writergit 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/skills/rp1-run/rp1/prompt-writer)<a href="https://agentmods.dev/skills/rp1-run/rp1/prompt-writer"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/prompt-writer/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/rp1-run/rp1/prompt-writer"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/prompt-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.01401 |
| Opus 5 | $0.00015 | $0.00700 |
| Sonnet 5 | $0.00006 | $0.00280 |
| Haiku 4.5 | $0.00003 | $0.00140 |
Grade A, and why
prompt-writer 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 12d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Writer
Guide for writing maximally terse agent prompts with built-in constitutional governance and epistemic stance. Combines compression-by-default authoring (tersify), governance primitives (constitution), and epistemological foundations (epistemology) into a single progressive-disclosure skill.
When to Use
- Creating new agent specifications or skill prompts
- Writing command/skill prompts with governance built in
- Drafting instruction sets for AI agents
- Applying constitutional or epistemic review to existing prompts
- Running the six-stage prompt pipeline via
/build-prompt
Core Principles
Structure-First: Start with skeleton, not prose. Choose section pattern, fill minimally.
Compression-by-Default: Every word must earn its place. Remove before adding.
Preserve Exactness: Normative language (MUST/SHOULD/MAY), literals, constraints stay verbatim.
Governed-by-Design: Constitutional primitives and epistemic stance are selected upfront, not bolted on.
Skill Files
Reference Layers
| File | Purpose | When to Load |
|---|---|---|
references/tersify.md |
Compression discipline, section patterns, abbreviation policy, style rules, validation checklist | Direct /prompt-writer invocation; prompt-validation pipeline stage |
references/constitution.md |
10 governance primitives (anti-loop, output discipline, role, scope limits, orchestrator purity, error degradation, exploration bounds, anti-bias, truth constraints, transition guards) + four agent-type profiles | Constitutional-checklist pipeline stage; standalone constitutional review |
references/epistemology.md |
Six epistemic stances (Fallibilist Empirical, Interpretivism, Phenomenology, Constructivism, Pragmatism, Compare-Mode) with composable contracts | Epistemic-stance pipeline stage; standalone epistemic review |
references/budget.md |
15% aggregate governance cap, per-stage budget allocations, line classification rules, enforcement protocol | Pipeline execution (Stage 6 budget enforcement); standalone budget review |
What ships with it
12 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.
- PATTERNS.md 5.3 KB
- pipeline/confidence-schema.md 5.0 KB
- pipeline/constitutional-checklist.md 3.4 KB
- pipeline/epistemic-stance.md 3.8 KB
- pipeline/fallibilist-overlay.md 5.0 KB
- pipeline/popper-patterns.md 11 KB
- pipeline/prompt-validation.md 18 KB
- references/budget.md 1.7 KB
- references/constitution.md 13 KB
- references/epistemology.md 7.7 KB
- references/tersify.md 6.3 KB
- TEMPLATES.md 5.2 KB
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.
- 12d ago First seen · 110 lines · 29 tokens per session scan A 08062f11e02b
prompt-writer is a skill published in the GitHub repository rp1-run/rp1 (38 stars, last pushed 4d ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,401 once invoked, about $0.0001 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 skills, from other repositories
refine
Transform a brief or prompt into a structured, production-ready prompt via prompt-optimizer. File or text mode.
prompt-optimization
Improves LLM-facing context while preserving intent, execution boundaries, and proportional work. Use when creating or reviewing prompts, agent definitions, skill definitions, or other instructions for an LLM.
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.
pi-prompting
Internal guidance for composing prompts that Pi runs (DeepSeek by default) handle reliably for coding, review, diagnosis, and research tasks.
analyze-prompt
Use when the user wants to review, score, or improve how they write prompts; when coaching prompt quality; or when tracking prompting mistakes over a session. Keywords: prompt analysis, prompt coaching, how should I ask, rate my prompt, am I prompting well, prompt statistics, prompt score.
prompt-report
Use when the user wants an end-of-session summary of their prompting: overall stats, best and worst prompts, biggest recurring mistake, improvement curve, and personalized recommendations. Keywords: prompt report, session summary, how did I do, my prompting stats, prompt scorecard, prompting progress.