# Analyze Creator Videos > Analyze a creator's complete public video portfolio and build an evidence-backed operating Dashboard. Use when Codex must collect or resume a creator inventory; verify downloaded media; study one creator or blogger across all visible posts; compare high, median, arithmetic-mean-near, and low performers; explain positioning, audiences, topics, formats, visual language, publishing patterns, success and failure mechanisms; select representative videos for deep reconstruction; turn findings into launch or account-growth decisions; or update the reusable List + Gallery creator intelligence Dashboard. Supports a creator profile URL, collected corpus JSON, or an existing local research directory. For Xiaohongshu acquisition, use ego-browser with the user's authenticated session; never substitute a Chrome/Google scraping route. - Skill: `cyl19970726/analyze-creator-videos-2` (Agent Skill, multi-file: 49 files) - Install (CLI): `npx skillmds@latest add cyl19970726/analyze-creator-videos-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/cyl19970726/analyze-creator-videos-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: cyl19970726 (https://skillmd.com/u/cyl19970726) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/cyl19970726/analyze-creator-videos-2 --- # Analyze Creator Videos Build a creator operating system, not a decorative long report. Separate corpus facts, video-internal evidence, author claims, system inferences, external verification, and unknowns throughout. Read [session-derived-infrastructure.md](references/session-derived-infrastructure.md) before creating a new run or changing the pipeline. Its gates come from the real build history that produced the reference Dashboard. ## Core model Run two connected layers: 1. **Portfolio layer:** inspect every visible post at metadata/annotation level to learn the account's baseline, positioning, content system, distribution, topics, formats, publishing rhythm, and outliers. 2. **Evidence layer:** deeply reconstruct a bounded representative set with `$video-content-reconstruction` before making claims about content mechanisms. Default deep set: high, median, arithmetic-mean-near, and low performance, 3 videos per group. Use the full corpus for quantitative context, a comparison set of 5–7 per group for pattern induction, and 3 per group for evidence-grade reconstruction. Median and mean are separate because breakout posts can pull the mean far above the typical post. If no post lies within 25% of the mean, label `mean_gap` and compare its nearest lower/upper neighbors instead of inventing an average tier. Do not reconstruct every video unless the user explicitly requests it and accepts the cost. ## Inputs Accept one or more: - creator profile URL; - normalized corpus JSON matching [creator-corpus.schema.json](schemas/creator-corpus.schema.json); - existing local creator research directory; - optional local videos, subtitles, covers, comments, and public metrics; - optional target account or business strategy to inform the final launch plan. Create a new run directory. Never overwrite an existing creator artifact unless the user explicitly asks to update it. Store authenticated URLs, session state, and login information outside public outputs. ## Workflow ### 1. Establish the analysis contract Write `analysis-contract.json` and `run-manifest.json` before collecting or interpreting data: - why the creator is being analyzed; - decisions the result must support; - available platforms and snapshot time; - allowed public metrics; - unavailable backend metrics; - default full-corpus, comparison-set, and deep-set sizes; - target account constraints, if any. `run-manifest.json` is the single control plane for artifact paths, source revisions, stage status, validation status, and downstream invalidation. Never maintain a second canonical Dashboard or duplicate specification tree for the same creator run. The Dashboard must answer why this creator is analyzed, what method was used, and which decisions can and cannot be made from the evidence. ### 2. Acquire or normalize the corpus For Xiaohongshu, read [acquisition-xhs.md](references/acquisition-xhs.md) and [collection-infrastructure.md](references/collection-infrastructure.md), then use `$ego-browser`. Reuse the authenticated `hhh-01` session when available. Respect handoff and user-control rules. Do not use Chrome, Google browser automation, or hidden credential extraction. When the user asks for broad platform discovery through Xiaohongshu's in-site AI, also read [xhs-site-ai-discovery.md](references/xhs-site-ai-discovery.md). Treat its answer as a candidate generator only; independently open and verify cited creators/posts before they enter the canonical corpus. Initialize and maintain the resumable collection control plane: ```bash node /scripts/init-collection.mjs \ --out /absolute/path/run/collection-inventory.json \ --creator-id \ --creator-name \ --profile-url \ --server hhh-01 ``` Ingest sanitized profile, crawl-round, and detail observations with the bundled ingestion scripts. Never call a timeout or extraction error “end of profile.” For local input, normalize it directly. Preserve raw snapshots separately from normalized data. ```bash node /scripts/normalize-corpus.mjs \ --input /absolute/path/raw-or-normalized.json \ --out /absolute/path/run/creator-corpus.json ``` Do not silently convert missing values to zero. Record capture failures and incomplete pagination in `collection-status.json`. Before portfolio analysis, build the collection gate: ```bash node /scripts/build-collection-status.mjs \ --inventory /absolute/path/run/collection-inventory.json \ --out /absolute/path/run/collection-status.json ``` Require `inventory_ready`. Before deep reconstruction, require selected media to pass `scripts/verify-media.mjs` and the status to become `deep_media_ready`. ### 3. Annotate every post at portfolio depth Read [data-contract.md](references/data-contract.md). For every post, add open-ended, multi-label annotations grounded in title, cover, post copy, and available video evidence: - topic and user problem; - content form and intent; - hook and promised payoff; - target audience and use context; - proof mode; - presenter, composition, aspect ratio, screen/UI role, text density, and result visibility; - content mechanism and likely audience action; - uncertainty and annotation evidence. Do not force posts into a closed taxonomy. Add new labels when the corpus demands them. Keep `unclassified` when evidence is insufficient. ### 4. Compute the full-corpus baseline ```bash node /scripts/analyze-corpus.mjs \ --input /absolute/path/run/creator-corpus.json \ --out /absolute/path/run/corpus-analysis.json ``` Calculate count, video count, mean, median, percentiles, maximum, distribution, weekday/daypart statistics, topic and format clusters, and candidate pools for high/median/low comparison. Prefer median and distribution over mean. Display maximum alongside median and mean. Treat likes, comments, collections, and shares as public engagement signals—not exposure, retention, conversion, or follower growth. ### 5. Build the comparison set and choose the deep set Read [selection-method.md](references/selection-method.md). - Compare 5–7 videos per high/median/average/low group when available. - Select 3 videos per group for deep reconstruction, reusing an overlapping median/average item only once. - Cover distinct mechanisms, content forms, durations, eras, and evidence modes. - Do not choose nine videos solely by ranking. - Record `selectionReason`, represented mechanism, alternatives considered, and known confounds for every selected item. If the corpus is small, shrink tiers and disclose the denominator. Never label a three-video sample “the creator's universal rule.” ### 6. Reconstruct selected videos Use `$video-content-reconstruction` for each deep-set video. Require its hard gates to pass before using a reconstruction as evidence. For every selected video preserve: - complete verbatim transcript; - cue ↔ representative frame ↔ all overlapping shots; - sparse and dense frame views; - content architecture and core knowledge units; - viewer cognitive change; - procedural or argument relationships; - claims, observations, inferences, and unknowns; - full readable article that restores the video's key information. Do not infer tool causality, product capabilities, commercial rights, performance, or success from montage adjacency. ### 7. Synthesize across videos Generate `creator-analysis.json` matching [creator-analysis.schema.json](schemas/creator-analysis.schema.json). Read [analysis-method.md](references/analysis-method.md). Separate observed portfolio facts, sample associations, mechanism hypotheses, optional external verification, and unknowns. Explain high vs median vs low through user value, content promise, proof strength, comprehension cost, information density, visual structure, novelty, audience fit, and CTA—not likes alone. ### 8. Produce the launch system Translate findings into the user's account: - positioning and audience wedge; - three content lanes serving save, share, and trust goals; - first 12 ideas; - reusable script skeletons; - proof checklist; - one-variable experiments; - dashboard metrics to add when backend data becomes available. Mark what is transferable, what depends on the original creator's identity or era, and what must be tested. ### 9. Build or update the Dashboard Read [dashboard-contract.md](references/dashboard-contract.md). Produce `dashboard-data.json` matching [dashboard-data.schema.json](schemas/dashboard-data.schema.json), then run: ```bash node /scripts/build-dashboard-data.mjs \ --corpus /absolute/path/run/creator-corpus.json \ --stats /absolute/path/run/corpus-analysis.json \ --analysis /absolute/path/run/creator-analysis.json \ --selection /absolute/path/run/selection.json \ --out /absolute/path/run/dashboard-data.json node /scripts/render-dashboard.mjs \ --data /absolute/path/run/dashboard-data.json \ --out /absolute/path/run/dashboard ``` Use the bundled template. When updating an existing Dashboard, preserve its URL, navigation, visual system, and working interactions; add new evidence into the existing page rather than creating a weaker parallel Dashboard. Treat `dashboard/` as generated output. Change source data, schemas, scripts, or the template; do not hand-patch a delivered Dashboard as the only source of truth. Required surfaces: 1. analysis purpose and account positioning; 2. full-corpus baseline and public-metric boundaries; 3. topic and format portfolios with count, median, mean, and maximum likes; 4. high/median/average/low comparison and success/failure mechanisms; 5. all-post List and Gallery views; 6. selected-video evidence with transcript, frames, content architecture, and unknowns; 7. publishing rhythm and video language; 8. launch plan, experiments, and methodology. ### 10. Validate ```bash python3 /scripts/validate-json.py \ --schema /schemas/analysis-contract.schema.json \ --data /absolute/path/run/analysis-contract.json python3 /scripts/validate-json.py \ --schema /schemas/run-manifest.schema.json \ --data /absolute/path/run/run-manifest.json python3 /scripts/validate-json.py \ --schema /schemas/collection-inventory.schema.json \ --data /absolute/path/run/collection-inventory.json python3 /scripts/validate-json.py \ --schema /schemas/collection-status.schema.json \ --data /absolute/path/run/collection-status.json python3 /scripts/validate-json.py \ --schema /schemas/creator-corpus.schema.json \ --data /absolute/path/run/creator-corpus.json python3 /scripts/validate-json.py \ --schema /schemas/selection.schema.json \ --data /absolute/path/run/selection.json python3 /scripts/validate-json.py \ --schema /schemas/creator-analysis.schema.json \ --data /absolute/path/run/creator-analysis.json python3 /scripts/validate-json.py \ --schema /schemas/dashboard-data.schema.json \ --data /absolute/path/run/dashboard-data.json node /scripts/validate-run.mjs --run /absolute/path/run ``` Browser-smoke the final Dashboard on desktop and mobile. Check console errors, navigation, List/Gallery switching, selected-video switching, image loading, transcript links, overflow, and evidence-boundary visibility. ## Completion gate Do not announce completion unless: - corpus coverage and collection failures are explicit; - full-corpus statistics are reproducible from `creator-corpus.json`; - all selected videos have selection reasons; - every mechanism claim points to comparison or reconstruction evidence; - no failed reconstruction is presented as validated; - unknown backend metrics remain unknown; - Dashboard List and Gallery both work; - validation and browser smoke pass. If any condition fails, report `NOT_READY` with the failed closure. Otherwise report `READY_FOR_CREATOR_ANALYSIS` and provide the canonical run path and Dashboard URL. ## Output contract ```text analysis-contract.json run-manifest.json raw/ # immutable collection snapshots collection-inventory.json crawl-ledger.jsonl creator-corpus.json collection-status.json corpus-analysis.json selection.json videos// # deep reconstructions and evidence media-verification/.json creator-analysis.json dashboard-data.json dashboard/index.html dashboard/styles.css dashboard/app.js validation-report.json ``` Do not publish raw media, private comments, authentication data, or signed URLs to a public repository.