nimbleway/agent-skills/launch-monitor

Launch Monitor

Monitors press, social, and community forums from launch day — tracking sentiment, flagging mischaracterizations, surfacing competitor responses, and recommending actions so you can respond fast.

Onboarding message

When this skill is triggered for the first time in a session, send this message. Send it exactly once per session, before asking any setup questions. Do not skip it even when the user's opening message already names a product — the onboarding sets expectations for what the skill collects and how it works.

👋 Launch Monitor is ready.

This skill monitors press, social media, developer communities, and competitor channels around a product launch — tracking sentiment, flagging mischaracterizations, surfacing competitor responses, and telling you exactly what to respond to and how.

To start, just say:

_"Monitor the launch of [product name]"_

Or try:

- "Track coverage since we announced [product] yesterday"

- "What's the reaction to our [product] launch?"

- "Flag any mischaracterizations in our [product] press coverage"

- "How are competitors responding to our [product] launch?"

Would you like me to save your preferences so I skip the questions next time?

Preflight

Follow the transport selection and standard preflight from references/nimble-playbook.md: pick CLI vs MCP at session start, then run the parallel preflight calls (date, profile, memory index) simultaneously. Tag every Nimble CLI call: nimble --client-source nimble-agent-skills <subcommand>.

From the profile (~/.nimble/business-profile.json): load product/brand context and last_runs.launch-monitor for date windowing. Pre-populate setup questions so the user confirms rather than re-enters. If no profile exists, follow the first-run onboarding flow in references/profile-and-onboarding.md and create a stub after the first run.

How to start

Before asking anything, do two quick research steps:

Step A — Resolve alternate names automatically: Search for the product the user named to discover all alternate names, codenames, version names, and related search terms. Do not ask the user for this. Use what you find to build a comprehensive search term list for Step 1.

• Search: "[product name]" official name OR "also known as" OR codename OR version
• For Apple products, always check for internal codename, model number, and marketing name variants
• For software: check GitHub repo names, API names, and SDK names
• Add all confirmed variants to your search queries silently — the user never needs to see this step

Step B — Confirm launch date automatically: Search for when the product launched before asking the user. Surface what you find and ask the user to confirm or correct it.

• Search: "[product name]" launch date OR announced OR "available now" OR "shipping today"
• If you find a clear date, present it to the user for confirmation
• If you find multiple conflicting dates (announcement vs GA), surface both and ask which window to use

Then ask in a single message:

"Before I run — just confirming a couple of things:

1. Launch date: I found that [product] launched on [DATE YOU FOUND] — is that the right date to monitor from, or a different one?

2. Window: How far back should I look beyond that? (default: from launch date to now)

3. Depth: Quick scan (faster) or deep sweep (more thorough, more sources)? (default: deep)

4. Competitors: I'll monitor competitor responses by default — any specific competitors to prioritize or exclude?"

Output is always the Response War Room rendered directly in Claude. Do not ask about format or output options.

Exceptions — skip asking entirely if:

• The user provided all the above in their initial message
• The user has run this skill before in the session (use prior config)

Defaults if user says "just run it":

• Window: launch date to now
• Depth: deep
• Competitors: on (identify automatically from context profiling)

Disambiguation: If the product name is ambiguous after research, confirm before proceeding:

"Just to confirm — by [product], do you mean [Option A] or [Option B]?"

Step 0 — Context profiling (run before any searches)

Before searching, profile the product to adapt all queries:

1. What category is this? (developer tool, consumer app, enterprise SaaS, hardware, API, etc.)
2. What ecosystem does it live in? (e.g. Claude/Anthropic, AWS, Salesforce, open source)
3. Who is the target audience? (developers, PMs, enterprise buyers, consumers)
4. What are the key claims made at launch? (extract from the user's description or a quick search of the announcement)
5. What would a mischaracterization look like? (wrong category, wrong pricing, wrong capability, wrong comparison)

Use this profile to:

• Target the right communities and press outlets
• Build precise search queries that find real coverage vs noise
• Know what "correct" positioning looks like so mischaracterizations can be flagged accurately
• Understand what competitor responses would look like

Step 1 — Signal sweep

Run all of the following in parallel. Use --search-depth lite for the discovery pass. Switch to --search-depth deep only when extracting full article or thread content. Tag every call: nimble --client-source nimble-agent-skills search ...

Press & editorial

• "[product name]" site:techcrunch.com
• "[product name]" site:theverge.com
• "[product name]" site:wired.com
• "[product name]" site:arstechnica.com
• "[product name]" site:venturebeat.com
• "[product name]" site:siliconangle.com
• "[product name]" site:theregister.com
• "[product name]" site:zdnet.com
• "[product name]" "[company name]" announcement OR launch OR release
• Category-specific press (e.g. for dev tools: InfoQ, SDTimes; for AI: The Information, Import AI)

Social & community

Run ALL of the following — social moves faster than press. Use focus:"social" on Nimble for broader platform reach. See references/sources.md Tier 2b for full query patterns per platform.

Reddit (run multiple subreddit-specific queries, not just the broad one):

• "[product name]" site:reddit.com — broad
• "[product name]" site:reddit.com/r/[category] — category subreddit
• "[product name]" site:reddit.com/r/[company] — brand subreddit
• "[product name]" site:reddit.com/r/technology and other relevant subs
• Nimble social focus: focus:"social" query "[product name]" reddit

X / Twitter:

• "[product name]" site:x.com
• "[product name]" wrong OR broken OR disappointed site:x.com — complaint hunt
• "[product name]" "actually" OR correcting site:x.com — correction chains
• Check quote-tweets of the official launch tweet

LinkedIn:

• "[product name]" site:linkedin.com
• "[product name]" launched OR "my take" site:linkedin.com

Instagram:

• Nimble social focus: focus:"social" query "[product name]" instagram
• Signal: influencer posts, brand account engagement, consumer reactions

TikTok:

• Nimble social focus: focus:"social" query "[product name]" tiktok review OR reaction
• Signal: viral reaction videos within 48h; comment sentiment

YouTube:

• "[product name]" review OR reaction OR "first impressions" site:youtube.com
• "[product name]" problems OR issues site:youtube.com

Facebook / Threads:

• Nimble social focus: focus:"social" query "[product name]" facebook OR threads

Hacker News:

• "[product name]" site:news.ycombinator.com
• Also search: hn.algolia.com/?q=[product+name]&dateRange=last24h

Dev communities:

• "[product name]" site:dev.to
• "[product name]" site:medium.com

Developer communities (if applicable)

• "[product name]" site:stackoverflow.com
• "[product name]" site:github.com — issues, discussions, reactions
• "[product name]" site:discord.com OR discord community
• "[product name]" site:hashnode.com

Competitor monitoring (if enabled)

• [competitor A] "[product name]" OR "[category]" — how are they reacting?
• [competitor A] announcement OR response OR "compared to" — any counter-announcements?
• Check competitor social accounts and blogs for positioning moves
• Search for "[competitor] vs [product name]" content published since launch date

Mischaracterization hunting

Run targeted queries designed to surface wrong information:

• "[product name]" "[wrong claim to watch for]"
• "[product name]" pricing OR price — check if pricing is being reported accurately
• "[product name]" vs "[wrong comparison]" — is it being compared to the wrong thing?
• "[product name]" "[capability it doesn't have]" — check for capability inflation or deflation

Step 2 — Signal triage

For every signal found, assign:

Urgency level:

• 🔴 Act now — mischaracterization going viral, high-reach negative coverage, competitor counter-announcement, crisis signal
• 🟡 Monitor — emerging negative theme, mid-reach inaccurate coverage, competitor positioning content
• 🟢 Good signal — accurate positive coverage, organic enthusiasm, developer adoption signals
• ⬜ Noise — irrelevant mentions, unrelated products with similar names, spam

Action badge:

• RESPOND — requires a direct public response (tweet, comment, press outreach)
• CORRECT — requires a correction or clarification (DM, comment, press note)
• AMPLIFY — worth sharing, retweeting, or building on
• ESCALATE — needs to go to comms, legal, or leadership
• WATCH — no action yet but track for escalation
• IGNORE — filtered noise

Signal type:

• Press coverage · Community discussion · Social mention · Competitor move · Mischaracterization · Churn signal · Influencer take · Developer reaction · Analyst comment

Source URL — required for every signal: Every signal must include the exact URL returned by Nimble for that article, thread, or post. This URL powers the clickable ↗ source link on each card. A homepage URL is useless to the user.

CORRECT — exact article/thread/post URLs:

• https://techcrunch.com/2026/06/11/nimble-mcp-connector-launch
• https://news.ycombinator.com/item?id=12345678
• https://reddit.com/r/MachineLearning/comments/abc123/is_nimble_just_another_scraper
• https://x.com/[username]/status/[POST_ID]

WRONG — never use these:

• https://techcrunch.com
• https://news.ycombinator.com
• https://reddit.com
• https://x.com

Use the URL exactly as Nimble returns it in the search result. Do not fabricate a URL.

Step 3 — Mischaracterization analysis

For every piece of coverage that gets something wrong, extract:

1. The claim — exactly what was said
2. The source — outlet, author, reach estimate, date
3. What's wrong — specific inaccuracy
4. The correct version — what the accurate statement is
5. Spread risk — is this being picked up by others? (search for secondary coverage citing the wrong claim)
6. Suggested response — one-sentence correction Claude recommends

Step 4 — Sentiment velocity

Track how sentiment is trending over time since launch:

• Break the window into intervals (e.g. hourly for first 24h, daily after that)
• For each interval: count positive, negative, neutral signals
• Identify the inflection point — when did sentiment peak? When did it shift?
• Flag any velocity spike — sudden surge in mentions (positive or negative)

Memory dedup (filter already-known signals)

Before generating output, run the dedup lifecycle from references/memory-and-distribution.md against prior launch-monitor reports in ~/.nimble/memory/reports/.