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 gmaxxxie/ai-native-product-agent-skills --skill p10a-value-discovery-loopgit clone --depth 1 https://github.com/gmaxxxie/ai-native-product-agent-skillsWrote 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/gmaxxxie/ai-native-product-agent-skills/p10a-value-discovery-loop)<a href="https://agentmods.dev/skills/gmaxxxie/ai-native-product-agent-skills/p10a-value-discovery-loop"><img src="https://agentmods.dev/badge/skills/gmaxxxie/ai-native-product-agent-skills/p10a-value-discovery-loop/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/gmaxxxie/ai-native-product-agent-skills/p10a-value-discovery-loop"><img src="https://agentmods.dev/badge/skills/gmaxxxie/ai-native-product-agent-skills/p10a-value-discovery-loop.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 | $0.00094 | $0.02202 |
| Opus 5 | $0.00047 | $0.01101 |
| Sonnet 5 | $0.00019 | $0.00440 |
| Haiku 4.5 | $0.00009 | $0.00220 |
Grade A, and why
p10a-value-discovery-loop 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 4d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 4d ago Changed · -251 lines c2bd87d2ed77
- 12d ago First seen · 467 lines · 94 tokens per session scan A a58c0b0775e6
p10a-value-discovery-loop is a skill published in the GitHub repository gmaxxxie/ai-native-product-agent-skills (46 stars, last pushed 2d ago), with no licence file. It adds 94 tokens to every session and 2,202 once invoked, about $0.0005 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
ai-md
AI.MD — Convert any human-written CLAUDE.md into AI-native structured format. Your CLAUDE.md is read by AI every single turn, not by you — so write it in AI's language. Battle-tested: 5 rounds, 4 models (GPT-5.3, Gemini 2.5 Pro, Grok-4, Claude Opus 4.6). Structured-label format raised Codex compliance from 6/8 → 8/8…
ai-md
AI.MD — Convert any human-written CLAUDE.md into AI-native structured format. Your CLAUDE.md is read by AI every single turn, not by you — so write it in AI's language. Battle-tested: 5 rounds, 4 models (GPT-5.3, Gemini 2.5 Pro, Grok-4, Claude Opus 4.6). Structured-label format raised Codex compliance from 6/8 → 8/8…
tinker-training-cost
Calculates training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
outcome-roadmap
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as outcomes.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.