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 Abhillashjadhav/AI-PM-essential-skills --skill prd-firstgit clone --depth 1 https://github.com/Abhillashjadhav/AI-PM-essential-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/abhillashjadhav/ai-pm-essential-skills/prd-first)<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/prd-first"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/prd-first/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/abhillashjadhav/ai-pm-essential-skills/prd-first"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/prd-first.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 | $0.00199 | $0.01881 |
| Opus 5 | $0.00100 | $0.00941 |
| Sonnet 5 | $0.00040 | $0.00376 |
| Haiku 4.5 | $0.00020 | $0.00188 |
Grade A, and why
prd-first 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 11d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD-First Discipline
The user (Abhillash) vibe-codes 10-15 apps and loses track of what each app is actually doing because Claude generates code from high-level intent without a written contract. This skill forces a 10-minute thinking pass before any code generation. The PRD is the contract; the code must satisfy it; future-Abhillash can read the file three months from now and remember why.
The hard rule
No PRD, no code. When the trigger fires, refuse to generate code or vibe-code prompts until a PRD exists as a markdown file in the repo at /prds/YYYY-MM-DD-.md. This is non-negotiable except when the user explicitly overrides with "skip the PRD" — in which case flag the risk once, then proceed.
The 5-question protocol
Ask one question at a time. Wait for the answer. Do not batch. This matches the user's learning style (theory → quiz → build, one question at a time).
Each question has a quality bar. If the answer is vague, ask one follow-up. Then move on — don't gold-plate.
Question 1 — Problem:
What hurts today, and for whom?
Quality bar: a specific pain, not a feature wish. "I want a dashboard" is a feature; "I'm losing 30 minutes a day reconciling Stripe payouts against orders" is a problem. If they answer with a feature, ask: "What's the underlying pain that makes you want that?"
Question 2 — User:
Who specifically uses this, and what's their current alternative?
Quality bar: a real person (you, your team, a known segment) and a named alternative ("I do it in a spreadsheet now", "we use Notion but it doesn't sync"). If "everyone" — push back: "Pick the one user whose problem we're solving first."
Question 3 — Success:
One metric, one number, one timeframe — when do we know this worked?
Quality bar: measurable. "Faster reconciliation" fails. "Reconcile a day's payouts in under 3 minutes by end of week 2" passes. If they can't name a number, accept a binary: "Does the thing I described in Q1 still happen on Friday? Yes/No."
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.
- 11d ago First seen · 117 lines · 199 tokens per session scan A 7269381df03f
prd-first is a skill published in the GitHub repository Abhillashjadhav/AI-PM-essential-skills (3 stars, last pushed 10d ago), licensed MIT. It adds 199 tokens to every session and 1,881 once invoked, about $0.0010 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.
Other skills, from other repositories
github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination.
recipe-create-meet-space
Create a Google Meet meeting space and share the join link.
x-tweet-by-conversation
Collects every tweet in an X (Twitter) conversation thread given a conversation id (root tweet id) — the focal tweet plus all replies, sub-replies, and quote chains — and returns normalized per-tweet data with text, author, engagement counts, media, hashtags, mentions, inreplyto mapping, and cursor for pagination. Use…
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
atmos-config
Atmos root configuration: atmos.yaml discovery, precedence, deep merging, basepath, imports, minimal bootstrap, and routing to narrower Atmos skills.
detecting-privilege-escalation-in-kubernetes-pods
Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.