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/sleeyax/promptfiles/refinenpx skills add sleeyax/promptfiles --skill refinegit clone --depth 1 https://github.com/sleeyax/promptfilesWrote 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/sleeyax/promptfiles/refine)<a href="https://agentmods.dev/skills/sleeyax/promptfiles/refine"><img src="https://agentmods.dev/badge/skills/sleeyax/promptfiles/refine.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.00394 |
| Opus 5 | $0.00023 | $0.00197 |
| Sonnet 5 | $0.00009 | $0.00079 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
refine 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 5d 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
Subject: $ARGUMENTS
Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.
$ARGUMENTS names what to refine. Empty means the plan or design already under discussion in this conversation.
Ask the questions one at a time.
If a question can be answered by exploring the codebase, explore the codebase instead.
Domain modeling
Before starting the interview, check whether a CONTEXT.md or CONTEXT-MAP.md exists at the repository root. If one exists, domain modeling is set up for this repo: use the domain-modeling skill throughout the interview — challenge terms against the glossary, sharpen fuzzy language, and record resolved terms and decisions as they crystallise. If neither file exists, skip this section and run the interview exactly as described here.
Asking questions
Ask exactly one question per turn, then halt and wait for the user's response before continuing. Never batch questions, never proceed on assumed answers, never self-answer and move on. The user must remain in control of the pace.
If a structured question tool is available in the current harness (e.g. AskUserQuestion in Claude Code), use it: provide your recommended answer plus alternatives as choices, and always include a final open-ended option for free-form input.
If no such tool is available, fall back to plain text: state the question, list the options including your recommendation, and explicitly stop output to wait for the user.
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.
- 5d ago First seen · 28 lines · 46 tokens per session scan A b44b2a542784
refine is a skill published in the GitHub repository sleeyax/promptfiles (2 stars, last pushed 6d ago), licensed MIT. It adds 46 tokens to every session and 394 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 2026-08-31.
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