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 sohaibt/product-mode --skill preflightgit clone --depth 1 https://github.com/sohaibt/product-modeWrote 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/sohaibt/product-mode/preflight)<a href="https://agentmods.dev/skills/sohaibt/product-mode/preflight"><img src="https://agentmods.dev/badge/skills/sohaibt/product-mode/preflight/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/sohaibt/product-mode/preflight"><img src="https://agentmods.dev/badge/skills/sohaibt/product-mode/preflight.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 | $0.00034 | $0.00466 |
| Opus 5.5 | $0.00014 | $0.00186 |
| Sonnet 5.5 | $0.00007 | $0.00093 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
preflight 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 today.
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
Pre-flight checklist
Fill in the five pre-flight boxes for the work the user describes. Use the text after the command as the work description. If there is no text, use the current conversation.
- Check
.product-mode/checklist/for a checklist on the same topic. If one exists, build on it and tell the user its path. - Draft each box from the conversation, the code, and
.product-mode/decisions/:- Problem: Whose pain are we solving, in one sentence?
- Why now: What changed? (evidence, trigger, cost of waiting)
- Scope: Smallest change that tests the hypothesis
- Primary metric: The one number we expect to move
- Reversibility: One-way or two-way door?
- Mark each drafted answer validated (the user said it or data shows it) or assumed (you inferred it). Never invent evidence, data, or a baseline.
- Show the draft. Ask about empty or assumed boxes in one short batch. Ask only questions whose answers change the work.
- When the user confirms, save the file as
.product-mode/checklist/<UTC timestamp>-<work-slug>.md. Use the timestamp format2026-01-31T09-05-00-000Zand a lowercase, hyphenated slug. Use this content:
# Product-Mode Pre-Flight Checklist
**Work:** <work description>
**Date:** <YYYY-MM-DD>
## Answers
- **Problem**: <answer>
- **Why now**: <answer>
- **Scope**: <answer>
- **Primary metric**: <answer>
- **Reversibility**: <answer>
---
*Generated by product-mode /preflight*
- Tell the user the saved path. If a box stays empty, write
Unknown: <how we will find out>and say that the work proceeds without it.
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.
- today First seen · 41 lines · 34 tokens per session scan A bd4c18d15f44
preflight is a skill published in the GitHub repository sohaibt/product-mode (176 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 466 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-11-08.
preflight compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| preflightsohaibt/product-mode | Skill | 176 | yesterday | MIT | A | 34 | Claude Code |
| dependency-auditornariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 7 | Claude Code, Codex |
| pr-review-expertnariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 8 | Claude Code, Codex |
| skill-security-auditornariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 161 | Claude Code, Codex |
| performance-profilernariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 7 | Claude Code, Codex |
| ci-cd-pipeline-buildernariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 11 | Claude Code, Codex |
| tech-debt-trackernariatrip191/my-claude-skills | Skill | 1 | 28d ago | none | A | 63 | Claude Code, Codex |