What does this skill do, and when should you use it?
Launch Window Planner supports timing decisions during product-launch research. It compares at least two candidate windows using industry events, competitor activity, community rhythm, review-latency padding, and operational constraints. It also recommends a launch-week or rolling-release format and defines an embargo lift moment with a timezone and committed parties. The skill proposes candidates; it does not directly create the authoritative launch record.
It reads user-provided launch goals, stage, team availability, store-review submissions, partner commitments, and press constraints. When available, it calls the Product Hunt, Hacker News, and GDELT connectors for competitor history, community rhythm, and news pulse. It scans event and conference cycles, builds a candidate-window table with conflicts, tailwinds, and risks, adds an Estimated store-review buffer, recommends launch-week versus rolling release, defines the exact embargo lift time and timezone, and submits the recommendation as a proposal for launch-registry to formalize.
- A product team has several quarterly dates and needs to avoid major industry events, dead zones, or crowded competitor moments.
- An app launch needs submission padding for store review without treating folklore or estimates as platform guarantees.
- Press, partners, and community contributors need one authoritative embargo lift moment and timezone.
- A launch owner must choose between a concentrated launch week and a staged rollout with explicit attention and execution tradeoffs.
- A team wants to submit a recommended window to launch-registry while keeping the research skill separate from the canonical record.
How do you install this skill?
- The workflow depends on external Product Hunt, GDELT, and Hacker News services; mainland-China reachability, rate limits, and data completeness are not established here, so failures should fall back to auditable user-supplied data.
- The review buffer is labeled Estimated but could still be mistaken for a general rule; the 2–3 day margin must not be treated as a store guarantee.
- The skill submits registry proposals and may promote findings to persistent memory; confirm target, content, authorization, and recoverability before any external or durable write.
- The skill references many repository protocols, scripts, and connectors, but this review did not execute or independently validate those dependencies.
- Shell / CLI
- Network access
- Local filesystem
Python standard library
Install the collection in Claude Code with "/plugin marketplace add aaron-he-zhu/aaron-marketing-skills" followed by "/plugin install aaron-marketing@aaron". For compatible Agent Skills hosts, run "npx skills add aaron-he-zhu/aaron-marketing-skills". You can also run "git clone https://github.com/aaron-he-zhu/aaron-marketing-skills". To select this skill individually, use the documented selector pattern: "npx skills add aaron-he-zhu/aaron-marketing-skills -s aaron-launch-window-planner".
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/launch/research/launch-window-planner" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Pick a launch window for [product] in [quarter]. Constraints: [team availability / store-review submission / partner commitments].
- Map the competitor launch calendar and industry events around [candidate date] — should we move?
Provide a product, launch stage, candidate dates or quarter, and constraints. Example: "Pick a launch window for [product] in [quarter]. Constraints: [team availability / store-review submission / partner commitments]." You can also ask: "Map the competitor launch calendar and industry events around [candidate date] — should we move?" If connectors are unavailable, paste the competitor dates and event calendar. The skill returns a comparison and registry proposal rather than directly establishing the final date.
What are this skill's strengths and limitations?
- Produces a concrete decision package covering window comparison, release format, review padding, and embargo definition.
- Requires timing inputs to be labeled Measured, User-provided, or Estimated.
- Clearly separates a research proposal from the authoritative launch-registry record.
- Works with pasted data and keyless Tier-1 data paths.
- The final date, stage, and embargo facts must be formalized by launch-registry; this skill does not write the canonical registry directly.
- Industry-event data is user-provided by default, so results depend on the supplied calendar when connectors are unavailable.
- The Product Hunt connector has non-commercial API terms; business use requires Product Hunt approval and attribution.
- The suggested two-to-three-day store-review margin is explicitly Estimated and is not a review-time guarantee.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Launch Window Planner this page | 57 · Use with care | ★ 3k | 12d ago | Apache-2.0 |
| Launch Tier Planner | 63 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Launch Momentum Planner | 60 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| RAMP Launch Readiness Auditor | 61 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Early Access Designer | 57 · Use with care | ★ 3k | 12d ago | Apache-2.0 |
Unlike trend-spotter, this skill chooses a launch window rather than judging whether a cultural moment is worth pursuing. Unlike launch-day-conductor, it plans the timing window instead of running the hour-by-hour launch-day playbook. It hands its proposal to launch-registry, which owns the canonical date, stage, and embargo record.
How did FollowSkills review this skill?
The skill treats connector and pasted content as untrusted data, favors read-only/keyless sources, and routes registry changes through an authorized propose flow; it also asks before writing memory and references injection defenses in SECURITY.md. Deductions apply because per-submission user confirmation, rollback, and sensitive-data minimization are not fully specified.
The instructions, input/output contract, pasted-data fallback, Measured/User-provided/Estimated labeling, and termination conditions are largely consistent. Static evidence does not show skill-specific tests, reproduced connector paths, or detailed diagnosable error messages for abnormal inputs, so the score remains below the static ceiling of 10.
Triggers, Quick Start prompts, arguments, expected outputs, non-fit boundaries, and next-skill routing are clearly described; pasted data provides a fallback when connectors are unavailable. Deductions apply because geo-relevance is marked low, mainland-China reachability of Product Hunt/GDELT is unverified, and semantic trigger precision is not demonstrated.
The skill provides structured frontmatter, versioning, Apache-2.0 licensing, a shared contract, examples, references, dependency notes, provenance labels, and limitation disclosures. Deductions apply because ownership, changelog practice, and a concrete maintenance/update path are not clear for this skill; referenced shared files and scripts were not independently checked here.
The contract specifies a directly usable candidate-window table, launch-format decision, review buffer, embargo definition, and handoff summary, with clear scope boundaries. Deductions apply because output quality depends heavily on supplied calendars and connector data, canonicalization requires a later skill, and no representative output or execution evidence is included; static review therefore stays at or below 7.
The skill requires source labels for timing inputs, official store documentation, and separation of measured facts from estimates; repository CI and tests provide some auditability. However, they do not demonstrate coverage of this skill's key paths, and no independently reproducible result sample is provided, warranting only limited credit.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →