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 tushaarmehtaa/tushar-skills --skill product-launchgit clone --depth 1 https://github.com/tushaarmehtaa/tushar-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/tushaarmehtaa/tushar-skills/product-launch)<a href="https://agentmods.dev/skills/tushaarmehtaa/tushar-skills/product-launch"><img src="https://agentmods.dev/badge/skills/tushaarmehtaa/tushar-skills/product-launch/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/tushaarmehtaa/tushar-skills/product-launch"><img src="https://agentmods.dev/badge/skills/tushaarmehtaa/tushar-skills/product-launch.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 | $0.00040 | $0.00742 |
| Opus 5 | $0.00020 | $0.00371 |
| Sonnet 5 | $0.00008 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
product-launch 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product launch
Build a launch around a specific audience, credible claim, working conversion path, and learning loop.
Choose a mode
- Strategy: positioning, audience access, channel choices, timeline, and risks.
- Implementation: build only the required site, waitlist, tracking, or launch assets.
- Audit/dry run: test readiness, claims, links, conversion, instrumentation, and operations.
- Postmortem: explain results, update positioning, and define follow-up experiments.
Scale the artifact to the launch. A small beta does not need a multiweek war room or a large GTM document.
Workflow
- Inspect product briefs, site/repository, analytics, waitlist, audience evidence, assets, platform accounts, and prior launches before asking questions.
- Establish stage, audience, problem, proof, target action, launch date/window, owner, budget, constraints, existing reach, and decision metrics. Ask only for missing inputs that change the plan.
- Research current alternatives, audience language, communities, channel rules, and relevant calendars from primary or first-party sources where possible. Date platform-sensitive findings. Do not claim knowledge of opaque ranking algorithms.
- Write plain-language positioning: for whom, what changes, mechanism, why now, and why credible. Remove unsupported claims.
- Choose a launch type and few channels based on audience access and product fit. For each, state format, owner, timing rationale, response plan, policy constraints, conversion path, and metric.
- Inventory the smallest asset set needed. Treat landing pages, demos, screenshots, support docs, share artifacts, tracking links, and product changes as dependencies only when the chosen plan requires them.
- Test the conversion path end to end. If demand capture is required, follow the safe waitlist guidance.
- Instrument source attribution, activation, conversion, follow-up, and failure states. Define what decision each metric supports and distinguish directional attribution from causal proof.
- Run a dry launch: links, forms, email delivery, mobile, accessibility, analytics, support, rollback, incident ownership, and platform-policy checks.
- Operate the launch, then review at an appropriate early window and after enough time for the target behavior to mature.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 60 lines · 40 tokens per session scan A 78da9f6222bf
product-launch is a skill published in the GitHub repository tushaarmehtaa/tushar-skills (11 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 742 once invoked, about $0.0002 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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