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 HK-hub/AgentSkills --skill pi-promptinggit clone --depth 1 https://github.com/HK-hub/AgentSkillsWrote 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/hk-hub/agentskills/pi-prompting)<a href="https://agentmods.dev/skills/hk-hub/agentskills/pi-prompting"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/pi-prompting/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/hk-hub/agentskills/pi-prompting"><img src="https://agentmods.dev/badge/skills/hk-hub/agentskills/pi-prompting.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 | $0.00031 | $0.00703 |
| Opus 5 | $0.00015 | $0.00351 |
| Sonnet 5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
pi-prompting 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 7d 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.
What it actually says
Pi Prompting
Use this skill when pi:pi-rescue needs to ask Pi for help on a non-trivial coding or investigation task.
Pi runs whatever model the user has configured. By default this plugin targets DeepSeek V4 (Pro for review, Flash for everyday tasks). The guidance below assumes the prompt may run on either a non-reasoning model (Flash) or a reasoning model (Pro). Bias toward concrete, numbered checklists rather than abstract instructions — both model classes follow those reliably.
Core rules:
- Prefer one clear task per Pi run. Split unrelated asks into separate runs.
- Tell Pi what done looks like. Do not assume it will infer the desired end state.
- State explicit grounding rules whenever an unsupported guess would degrade quality.
- Use stable XML-style tag blocks so the prompt has obvious internal structure.
- Keep prose short. Prefer numbered checklists over paragraphs for procedural work.
Default prompt recipe:
<task>: the concrete job and the relevant repository or failure context. Include file paths.<output_contract>(or<structured_output_contract>for JSON): exact shape, ordering, and brevity requirements.<grounding_rules>: required for review, research, or anything that could drift into unsupported claims.<action_safety>: required for write-capable tasks — name the directories and file kinds Pi may touch.
When to add extra blocks:
- Coding or debugging: add
<completeness_contract>listing what counts as "done" (tests pass, lint clean, files touched). - Review or adversarial review: rely on the shipped
prompts/adversarial-review.mdandprompts/review.md. Do not hand-craft another review prompt. - Research or recommendation tasks: add a short
<sources>block listing what Pi is allowed to consult.
How to choose prompt shape:
- Use the built-in
/pi:reviewor/pi:adversarial-reviewcommands when the job is reviewing local git changes. Those prompts already carry the review contract. - Use
taskwhen the job is diagnosis, planning, research, or implementation and you need to control the prompt more directly. - Use
task --resume-lastfor follow-up instructions on the same Pi session. Send only the delta instruction instead of restating the whole prompt unless the direction changed materially.
Working rules:
- Prefer explicit prompt contracts over vague nudges.
- Do not raise reasoning (
--effort high) by default — DeepSeek Flash will not benefit, and DeepSeek Pro takes longer. Tighten the prompt before escalating effort. - For tool-heavy or long-running tasks, ask Pi for brief outcome-based progress updates inside its working notes.
- Keep claims anchored to observed evidence. If something is a hypothesis, mark it as such.
Prompt assembly checklist:
- Define the exact task and scope in
<task>. - State the output contract — JSON, bullet list, plain prose — and the maximum length.
- Decide whether Pi should keep going on its own judgment or stop for missing high-risk details.
- Add
<grounding_rules>and<action_safety>only where the task needs them. - Remove redundant instructions before sending the prompt.
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.
- 7d ago First seen · 48 lines · 31 tokens per session scan A e68d16864761
pi-prompting is a skill published in the GitHub repository HK-hub/AgentSkills (6 stars, last pushed 23d ago), licensed MIT. It adds 31 tokens to every session and 703 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 2026-09-03.
Other skills, from other repositories
prompt-writer
Write maximally terse agent prompts from scratch. Use when creating agent specs, command prompts, or instruction sets with constitutional governance.
tersify-prompt
Rewrites agent-instruction prompts to be maximally terse while preserving full intent.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.
outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…