Skill detail
short-form-research
Useful TikTok trend research, but it is a cross-platform short-form research workflow.
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SKILL.md
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---
name: short-form-research
description: "Discovers what's working right now on short-form video platforms (TikTok, Instagram Reels, YouTube Shorts; X video and LinkedIn video by opt-in) for a given topic and market. Produces a per-platform best-practice catalog at .agents/skill-artifacts/research/short-form-research/[slug].md that short-form-brief consumes. Not for long-form video (parked) or static visual (use design-brief). For audience research, see icp-research; for campaign planning, see campaign-plan."
argument-hint: "[topic or angle]"
allowed-tools: Read Grep Glob Bash WebSearch WebFetch Write
license: MIT
metadata:
author: hungv47
version: "1.0.0"
budget: deep
estimated-cost: "$3-6 (default 3 platforms) / $5-10 (--all)"
promptSignals:
phrases:
- "what's working on tiktok"
- "short-form trends"
- "reels research"
- "shorts research"
- "tiktok patterns"
- "viral hook research"
allOf:
- [short-form, research]
- [tiktok, hook]
- [reels, pattern]
anyOf:
- "trending sound"
- "hook archetype"
- "platform research"
- "what's hitting"
noneOf:
- "long-form"
- "youtube long"
- "podcast"
- "blog post"
minScore: 6
routing:
intent-tags:
- short-form-research
- platform-pattern-mining
- hook-research
- content-research
position: pipeline
lifecycle: pipeline
produces:
- .agents/skill-artifacts/research/short-form-research/[slug].md
consumes:
- product-context.md
- icp-research.md
requires: []
defers-to:
- skill: icp-research
when: "no audience context exists yet — research without ICP underperforms"
- skill: market-research
when: "user wants competitive landscape, not platform pattern mining"
parallel-with:
- market-research
interactive: false
estimated-complexity: heavy
---
# Short-Form Research — Orchestrator
*Pipeline skill — produces the per-platform best-practice catalog that `short-form-brief` consumes per asset.*
**Core Question:** "What's working right now on short-form for our topic and market — and which patterns should the next 30 days of briefs bet on?"
---
## Critical Gates — Read First
Non-negotiable constraints before dispatching any agent:
1. **No fabricated data.** Every claim, number, and pattern must trace to a source URL, video ID, or cited platform doc. Orphan claims fail critic rubric #1.
2. **Single market per artifact.** Multi-market campaigns re-run research per market — never mix VN and US findings in one artifact. Cultural patterns are not averageable.
3. **Hard cap on platforms.** Default 3 (TikTok + Reels + Shorts). X video and LinkedIn video are explicit opt-in via `--all` or `--platforms`. Maximum ever is 5. Cost discipline.
4. **Sample-size honesty.** Every per-platform section declares OK (n≥8), LOW_SAMPLE (n=3-7), or INSUFFICIENT_DATA (n<3). LOW_SAMPLE flags carry through to brief skill warnings. INSUFFICIENT_DATA means no pattern claims at all — only observed examples.
5. **Two freshness windows.** Trend signals refresh every 14d, warn at 30d. Platform mechanics refresh every 90d, warn at 180d. Frontmatter records `mechanics_sources_verified[]` — the actual doc URLs and their last-updated dates, not just the run timestamp.
---
## Philosophy
Research yields a **catalog of bets**, not a survey. The brief skill consumes specific recommendations ("TikTok hook should be credential-flash archetype in 0–1.5s — 8/12 in this sample") not generic advice ("strong hooks matter"). Specificity is the contract.
Trend signals decay fast — 14-day windows are deliberate. Platform mechanics change quarterly — 90-day windows match algorithm-update cadence. The two-window split prevents the "fresh date, stale truth" failure mode where one timestamp masks the other.
## Inputs / Output
**Inputs:** Topic (required); target platforms (default 3); market (single per artifact); audience hint or `research/icp-research.md` (warm-start); optional competitor hRead the full source on GitHub (opens external page)