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 vfarcic/dot-ai --skill dot-ai-prd-nextgit clone --depth 1 https://github.com/vfarcic/dot-aiWrote 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/vfarcic/dot-ai/dot-ai-prd-next)<a href="https://agentmods.dev/skills/vfarcic/dot-ai/dot-ai-prd-next"><img src="https://agentmods.dev/badge/skills/vfarcic/dot-ai/dot-ai-prd-next/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/vfarcic/dot-ai/dot-ai-prd-next"><img src="https://agentmods.dev/badge/skills/vfarcic/dot-ai/dot-ai-prd-next.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 188 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00024 | $0.02496 |
| Opus 5 | $0.00012 | $0.01248 |
| Sonnet 5 | $0.00005 | $0.00499 |
| Haiku 4.5 | $0.00002 | $0.00250 |
Grade A, and why
dot-ai-prd-next 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 9d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Next - Work On the Next Task
Instructions
You are helping analyze an existing Product Requirements Document (PRD) to suggest the single highest-priority task to work on next, then discuss its design if the user confirms they want to work on it.
Process Overview
- Check Context Clarity - Determine if PRD is obvious from recent conversation
- Auto-Detect Target PRD - If context unclear, intelligently determine which PRD to analyze
- Analyze Current Implementation - Understand what's implemented vs what's missing (skip if recent context available)
- Identify the Single Best Next Task - Find the one task that should be worked on next
- Present Recommendation - Give clear rationale and wait for confirmation
- Design Discussion - If confirmed, dive into implementation design details
- Implementation - User implements the task
- Update Progress - Prompt user to run /prd-update-progress
Step 0: Context Awareness Check
FIRST: Check if PRD context is already clear from recent conversation:
Skip detection/analysis if recent conversation shows:
- Recent PRD work discussed - "We just worked on PRD 29", "Just completed PRD update", etc.
- Specific PRD mentioned - "PRD #X", "MCP Prompts PRD", etc.
- PRD-specific commands used - Recent use of
/prd-update-progress,/prd-startwith specific PRD - Clear work context - Discussion of specific features, tasks, or requirements for a known PRD
If context is clear:
- Skip to Step 6 (Single Task Recommendation) using the known PRD
- Use conversation history to understand current state and recent progress
- Proceed directly with task recommendation based on known PRD status
If context is unclear:
- Continue to Step 1 (PRD Detection) for full analysis
Step 1: Smart PRD Detection (Only if Context Unclear)
Auto-detect the target PRD using these context clues (in priority order):
- Git Branch Analysis - Check current branch name for PRD patterns:
feature/prd-12-*→ PRD 12prd-13-*→ PRD 13feature/prd-*→ Extract PRD number
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.
- 9d ago First seen · 265 lines · 24 tokens per session scan A c6264140973c
dot-ai-prd-next is a skill published in the GitHub repository vfarcic/dot-ai (336 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 2,496 once invoked, about $0.0001 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…