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 unifapi-agent/agents --skill competitor-launch-monitorgit clone --depth 1 https://github.com/unifapi-agent/agentsWrote 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/unifapi-agent/agents/competitor-launch-monitor)<a href="https://agentmods.dev/skills/unifapi-agent/agents/competitor-launch-monitor"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/competitor-launch-monitor/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/unifapi-agent/agents/competitor-launch-monitor"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/competitor-launch-monitor.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 | $0.00124 | $0.02131 |
| Opus 5 | $0.00062 | $0.01066 |
| Sonnet 5 | $0.00025 | $0.00426 |
| Haiku 4.5 | $0.00012 | $0.00213 |
Grade A, and why
competitor-launch-monitor 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 12d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Launch Monitor
You are a competitive analyst who reads a competitor's launch against its actual public reception, not its press release.
Turn a named competitor's launch or announcement into an evidence-backed brief: what they shipped, how they're positioning it, which channels they're pushing, how customers and the market are reacting, and where it's vulnerable. Then leave behind a re-runnable watchlist so the next move gets caught early. This is a read on a moment in time, not a permanent profile.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
The signal is the overlap — what the competitor claims vs. how the market actually responds. Reaction volume and sentiment are only credible when measured directly from public engagement counts across surfaces, not eyeballed from one viral thread. Use the unifapi skill to connect (OAuth MCP), then call:
- The announcement —
x/users/{id}/tweets— the launch posts from the company/founder/exec accounts; the positioning in their own words. - Chatter —
x/tweets/search/recent— the wider conversation about the product/feature beyond the announcement thread. - Reaction volume + sentiment —
x/tweets/{id}/quote_tweets,x/tweets/{id}/retweeted_by,x/tweets/{id}/liking_users— quote-tweets carry the opinion (the differentiation doubts, the praise), reposts/likes carry the spread; together they size whether it landed against the account's norm. - B2B framing + next bet —
linkedin/companies/{slug}/posts(official buyer-facing announcement and employee amplification) andlinkedin/companies/{slug}/jobs(build-up hiring that hints where they invest next). - Demo reception —
youtube/search(find the launch/demo/reaction videos) andyoutube/videos/{video_id}(views, likes, comment count vs. their other videos = demand signal; the gap the marketing skipped shows in what prospects ask). - Unfiltered reaction —
reddit/posts/{id}/comments— open the relevant community thread and mine upvoted praise, complaints, and direct comparisons to alternatives. - Hacker News reception —
hacker-news/stories/{feed}/items(did the launch reach theshoworfrontfeed?) andhacker-news/items/{id}(the Show HN / launch thread — points, comment count, and the candid technical critique that often decides a dev-tool or infra launch). - Coverage —
news/search— press coverage and the angles outlets chose, to separate paid/PR framing from independent assessment.
What ships with it
1 file 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.
- 12d ago First seen · 92 lines · 124 tokens per session scan A d61cb3f50be9
competitor-launch-monitor is a skill published in the GitHub repository unifapi-agent/agents (566 stars, last pushed 7d ago), licensed MIT. It adds 124 tokens to every session and 2,131 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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