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 T4LEL/Claude-Arsenal --skill launchgit clone --depth 1 https://github.com/T4LEL/Claude-ArsenalWrote 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/t4lel/claude-arsenal/launch)<a href="https://agentmods.dev/skills/t4lel/claude-arsenal/launch"><img src="https://agentmods.dev/badge/skills/t4lel/claude-arsenal/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/t4lel/claude-arsenal/launch"><img src="https://agentmods.dev/badge/skills/t4lel/claude-arsenal/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 | $0.00046 | $0.00743 |
| Opus 5 | $0.00023 | $0.00371 |
| Sonnet 5 | $0.00009 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00074 |
Grade A, and why
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 8d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch
Get humans to show up — everything gets drafted, the user posts it; never auto-publish anywhere.
Copy this checklist and check off items as you complete them:
Launch Progress:
- [ ] Step 1: Gate — production live, signup verified
- [ ] Step 2: Assets — copy, legal, analytics, OG tags
- [ ] Step 3: Channel plan
- [ ] Step 4: Posts drafted
- [ ] Step 5: Day-of runbook
- [ ] Report
Step 1 — Gate (paste real output)
Request the production URL and confirm it loads with real content. Run signup end to end and confirm it actually completes — not "looks right," an observed result. If it's not live or signup is broken, stop here and run /ship-it first; launching an unshipped product wastes the channel goodwill.
Step 2 — Assets
- Delegate to the copywriter agent with the product and audience: a landing copy pass where the headline states the customer outcome and there is exactly one CTA.
- Delegate to the legal-advisor agent with what tracking is actually implemented: privacy policy and any other legal pages, written to match the real tracking in place — not boilerplate claiming data collection that doesn't exist.
- Delegate to the data-analyst agent with the funnel: minimal events for visit, signup, activation, instrumented and confirmed firing (real event in a real dashboard, not "should work"). Any analytics or tracking keys stay in env vars, never printed in chat, code, or docs.
- Check OG/meta tags render correctly when the link is shared — paste a real preview (link debugger or equivalent), not an assumption. Fetch current docs via the context7 MCP tools for any tracking or meta-tag SDK involved.
Step 3 — Channel plan
Delegate to the growth-marketer agent with the product, audience, and Step 2 assets: channels ranked by fit and effort, the first 3 concrete actions for each, and a launch-day sequence with rough timing. Any market or conversion benchmarks the agent cites from general knowledge rather than this product's real data get labeled unverified assumptions in the report.
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.
- 8d ago First seen · 52 lines · 46 tokens per session scan A 9a3c85fb147b
launch is a skill published in the GitHub repository T4LEL/Claude-Arsenal (1 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 743 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 2026-08-31.
Other skills, from other repositories
workers-best-practices
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
find-journalists
Build, refine, dedupe, and enrich small fit-checked journalist lists for newsjack campaigns. Uses the newsjack CLI (preferred) or the medialyst MCP for news search and journalist enrichment, and falls back to a best-effort local mode with no verified contacts; the agent owns how returned data is organized.
story-origin-check
Recover the first public timestamp and canonical major coverage for a newsjacking signal, then decide whether newer coverage is the same story, a different story, or a materially new development.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
annotating-task-lineage
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
checking-freshness
Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.