# Socialseal Opportunity Analysis > Use this skill when answering questions such as "what should we make content about?", "what are people searching for or trying to answer?", "which queries or topics should we prioritize?", and "where are the content gaps?" Turn SocialSeal journeys, ranked results, tracking exports, or group evidence into keyword priorities, audience jobs, discoverability gaps, and actionable opportunities. - Skill: `opensealai/socialseal-opportunity-analysis` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add opensealai/socialseal-opportunity-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/opensealai/socialseal-opportunity-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: OpenSealAI (https://skillmd.com/u/opensealai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/opensealai/socialseal-opportunity-analysis --- # SocialSeal Opportunity Analysis ## Overview SocialSeal opportunity analysis answers: where should the brand create or improve content because people are searching and the current surfaced content leaves a gap? This skill replaces a vague prompt like “analyze this SocialSeal data.” It tells the agent what to inspect, how to define an opportunity, what metrics are safe to use, and what a good deliverable looks like. ## Inputs Required: - SocialSeal export files or SocialSeal MCP/CLI access - workspace ID and group ID(s), or exported CSV/JSON files - brand/entity definition and owned handles - platform, market, language, keyword/topic scope, and date range Good to have: - competitor list - current content pillars - business priority or target audience - previous-period export for comparison If no relevant tracking group or export exists, do not invent an opportunity matrix. Route through `socialseal-workspace-setup` for either a focused one-off search journey or reusable group setup, then resume with the returned journey/ranked evidence. ## Data Access Use enriched ranked search rows for keyword-level analysis: ```bash npx -y @socialseal/cli data export-search-results \ --group-ids , \ --workspace-id \ --out ./exports/search-results.csv \ --timeout 120000 ``` Use group evidence when you need a safer unified export across social and Google AI groups: ```bash npx -y @socialseal/cli data export-group-evidence \ --group-id \ --workspace-id \ --out ./exports/group-evidence.csv \ --timeout 120000 ``` MCP-first: there is no `export-group-evidence`/`export-search-results` MCP tool. Reach enriched ranked rows via `socialseal_export_report` (`reportType: "search_results_enriched"`, `payload: { "groupIds": [] }`) or `socialseal_export_tracking_data`. See `references/mcp-and-cli-usage.md`. Attribution: cite surfaced results in human-readable terms, the `"keyword" [market, platform]`, the video title or `video_url`, and `@author_handle`, with where it ranked. Keep `video_uid`/`search_result_id` only as an internal traceability note, and respect the timestamp rules (no platform-age claims when `published_at` is blank). See `references/socialseal-data-contract.md`. Evidence tiers: discoverability, coverage, and share of voice are hard observations, not estimates; report them plainly. But every figure is computed over a biased sample (only high-ranking videos for the tracked queries), so it describes "what surfaces for these queries," not total market demand. See `references/evidence-and-confidence.md`. ## Workflow 1. **Inspect the export.** Identify columns, grain, platform, market, date range, keyword field, URL/media ID, account/entity fields, metrics, and analysis fields. 2. **Define the denominator.** For each group, count qualified keywords/searches and qualified surfaced results. Exclude irrelevant rows before calculating. 3. **Separate entities.** Tag owned brand, competitors, creators/media/partners, and irrelevant/noise. If owned handles are unknown, ask or create a conservative placeholder column. 4. **Calculate safe metrics.** Use keyword coverage, discoverability by topic, surfaced attention/share where qualified metrics exist, and owned-vs-creator split. Do not lead with raw row counts. 5. **Find gaps.** A strong opportunity usually has search intent, relevant surfaced content, weak/absent owned presence, and a content job the brand can answer. 6. **Classify content jobs.** Use practical tags such as teach, walkthrough, compare, plan, reassure, show mood, show detail, or answer first-timer questions. 7. **Prioritize.** Rank by relevance, search intent strength, competitive/creator activity, expected business usefulness, and production feasibility. 8. **Select evidence.** For each recommendation, include metrics plus 1-3 examples that show what currently surfaces, cited by video title/URL and `@handle`. Label each recommendation's confidence basis (measured / scoped statistic / indicative pattern). 9. **Run the demand decision gate.** Present 3-5 prioritized query/topic options with the audience job, evidence, brand fit, and production feasibility. Ask the user to select or adjust the production priority before concepting; do not silently choose a whole content plan from one keyword. ## Output Create an opportunity table with: - priority - platform / market / keyword or topic - current brand presence - who/what surfaces now - content job - why this matters - recommended content direction - evidence examples (video title/URL, `@handle`, `"keyword" [market, platform]`) - caveat/confidence (measured / scoped statistic / indicative pattern) Also include a short action summary: what to brief, what to track, what to inspect further, and what not to pursue. For production-bound work, include the selected/awaiting-selection status so the next skill does not mistake every opportunity for an approved brief. ## Do / Don't Do: - use percentages and denominators - distinguish “no brand presence” from “no meaningful search demand” - cite examples by video title/URL, `@handle`, and `"keyword" [market, platform]` - keep recommendations shootable or operationally actionable Don't: - call every zero a whitespace opportunity - recommend topics outside the brand’s plausible scope - claim total market demand from sampled social-search results (selection bias) - make the user read internal IDs or raw exports as the deliverable ## Troubleshooting - If exports lack video/source identifiers, run `export-group-evidence` or refresh the export before video-level analysis. - If brand tagging is ambiguous, tag conservatively and add a review-needed column. - If keyword language is mixed, split analysis by language/market before ranking opportunities. - If the export is stale, refresh before making recommendations. ## Verification Checklist - [ ] Export grain and scope are stated. - [ ] Denominators are visible. - [ ] Owned, competitor, creator, and irrelevant rows are separated. - [ ] Opportunities include evidence and a content job. - [ ] Recommendations are actionable and within brand scope.