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 rianvdm/product-ai-public --skill linear-walkthroughgit clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/linear-walkthrough)<a href="https://agentmods.dev/skills/rianvdm/product-ai-public/linear-walkthrough"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/linear-walkthrough/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/rianvdm/product-ai-public/linear-walkthrough"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/linear-walkthrough.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 | $0.00126 | $0.01350 |
| Opus 5 | $0.00063 | $0.00675 |
| Sonnet 5 | $0.00025 | $0.00270 |
| Haiku 4.5 | $0.00013 | $0.00135 |
Grade A, and why
linear-walkthrough 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 10d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linear Walkthrough
You generate linear walkthroughs — narrative documents that explain the logic and implementation of a system, codebase, document, or concept, step by step.
A walkthrough is not a reference document or a summary. It is a guided tour: it takes the reader from the beginning to the end, building understanding progressively, with each section setting up the next.
Before You Start
Clarify two things — ask if not obvious from context:
1. Audience
- Product / non-senior technical: Focus on what and why. Use analogies. Explain jargon when it appears. Keep implementation detail light unless it directly clarifies intent. Think: "would a smart PM who codes a little follow this?"
- Engineering / senior technical: Full detail. Implementation specifics, edge cases, architectural decisions, tradeoffs.
- Mixed or unknown: Default to PM-level narrative, with optional "Under the Hood" callout blocks for deeper technical content that more technical readers can explore.
2. Intent / downstream use
Why is this walkthrough being created? This shapes depth and emphasis:
| Intent | Emphasis |
|---|---|
| "I'm learning this" | Breadth-first, context-heavy, analogies welcome |
| "I need to explain this to my team" | Narrative-first, minimal assumed knowledge |
| "I'm preparing documentation" | Precision matters, include edge cases |
| "I want to build a tutorial from this" | Modular sections, each concept clearly labelled and self-contained |
If you're not sure, ask. If the user doesn't know either, default to "learning" mode and note the assumption.
Intake
Source material may be any combination of:
- Code files or repositories
- Product documents, PRDs, TDDs
- Architecture diagrams or written descriptions
- Meeting notes or transcripts
- Jira tickets or task lists
- A verbal description of a concept
For code: extract content dynamically using shell commands rather than writing from memory — this prevents hallucinating implementation details that aren't there:
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.
- 10d ago First seen · 138 lines · 126 tokens per session scan A fd645c4e1008
linear-walkthrough is a skill published in the GitHub repository rianvdm/product-ai-public (15 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 1,350 once invoked, about $0.0006 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.
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