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VideoWestern AI Influencer Ads
Generate AI virtual influencer ad assets for TikTok Shop in Western markets: in three steps, create a 9:16 influencer face, a vertical ad photo of the influencer holding the product, and a vertical product-introduction short video. No real person, model, or shoot is needed, while the character and product remain consistent throughout. Ready to publish. ⚠️: Creating a finished 15-second video from an image uses approximately 3,000 YouMind credits;
ResearchSpark | YouTube Researcher
Planning a video but unsure how others have already covered the topic? “Spark | YouTube Researcher” conducts real research on YouTube: finding relevant videos, reading transcripts, breaking down titles and the first 30 seconds, reconstructing content structures, and identifying recurring approaches, clear differences, and gaps that no one has addressed well across multiple examples. You can use it to: Research which approaches are repeatedly used for a topic Deeply analyze how a video is presented Scan a channel’s topics, titles, and content patterns Break down the first 30-second hook and the full video structure Find new creative angles in existing videos Research how to repackage your own finished video It won’t jump to the conclusion that a title must work simply because one video has high views. When transcripts, timestamps, or performance data are unavailable, it will also clearly tell you what evidence is missing. What you get is not a pile of video summaries, but a YouTube research brief you can hand directly to the next step of topic development, hook creation, or scriptwriting. Ideal for YouTube creators, video planners, social media operators, and anyone who wants to see how far others have taken a topic before starting to film.
ImageTimeline Infographic
Organize years, major events, and authentic visual archives into a circular yearbook with a distinctive central focus.
WriteHuman-Voice WeChat Writing
Turn a trending event, social phenomenon, workplace or relationship topic, personal experience, industry observation, product material, or existing draft into a WeChat Official Account article with a clear position, genuine emotion, and strong shareability. It doesn’t stop at the safe, even-handed “everyone has a point.” Instead, it helps you find an angle that fits your target readers, identify the real conflicts and pain points, and use specific scenes, everyday details, and memorable judgments to make the article sound like it was written by a real person with experience and preferences—someone who also acknowledges the limits of their understanding. Along with a publish-ready draft, it also organizes the creative decisions, title directions, and article’s core points. It prepares authorial responses for real-world comment scenarios, including challenges to the facts, opposing views, personal experience sharing, and quotes taken out of context, making it easier to handle discussion after publication. For trending topics, controversial events, and high-risk subjects such as law, medicine, and finance, it distinguishes known facts, reasonable inferences, and the author’s judgments, avoids fabricated information, and flags areas that still need sources or verification. It’s suited to WeChat Official Account writers, independent media editors, and content teams creating social commentary, workplace perspectives, relationship and emotional opinions, personal growth topics, and industry commentary. It’s especially useful when you want to move beyond detached “both-sides” writing and AI-sounding prose without relying on personal attacks, exaggerating facts, or creating needless conflict to gain attention.
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Entropy-TOPSIS-ML + Reviewer Objection
Produces a complete Methods + Results report from your entropy weighting + TOPSIS ranking + ML classifier (XGBoost, Random Forest, Logistic Regression, etc.) outputs. Correctly classifies AUC interpretation (Hosmer & Lemeshow criteria) and, if the train/test performance gap is large, states the overfitting risk explicitly. It also pre-identifies at least 4 objections journal reviewers frequently raise (e.g., "how was class imbalance handled," "no external validation set was used") and suggests a defense sentence for each. Suitable for: researchers working in health informatics, clinical risk prediction, or hybrid MCDM-ML methodology who want methodological robustness before journal submission.
TOPSIS Decision Analysis Writer
Produces a complete manuscript-ready Methods + Results text from your TOPSIS analysis outputs (normalized decision matrix, distances to positive/negative ideal solutions, closeness coefficients). Provides a ranking table and interprets which criteria the best and worst alternatives excelled or lagged in. Suitable for: researchers working on multi-criteria ranking problems such as supplier selection, product/service comparison, or clinical decision support.
AHP Criteria Weighting Writer
Converts the user's AHP (Analytic Hierarchy Process) pairwise comparison matrix outputs (criteria weights, consistency ratio) into an academic "Criteria Weighting" section ready to drop straight into a manuscript. If the consistency ratio (CR) exceeds 0.10, it states this openly rather than hiding it. Suitable for: researchers who use MCDM methods such as TOPSIS/VIKOR and determine criteria weights with AHP, and graduate students.
Entropy-TOPSIS-ML + Rebuttal
Generates a complete Methods + Results report from your entropy weighting, TOPSIS ranking, and ML classifier outputs (XGBoost, Random Forest, Logistic Regression, etc.). It correctly interprets AUC using Hosmer & Lemeshow criteria and clearly flags the risk of overfitting when the training and test performance gap is large. It also anticipates at least four objections commonly raised by journal reviewers (e.g., “How was class imbalance addressed?” and “Why was no external validation set used?”) and provides a defense statement for each. Suitable for researchers working in health informatics, clinical risk prediction, or hybrid MCDA-ML methodologies, and for those seeking methodological rigor before journal submission.
TOPSIS Decision Analysis Report Writer
Generates a complete, publication-ready Methods + Results section from your TOPSIS analysis outputs (normalized decision matrix, distances to positive/negative ideal solutions, and closeness coefficients). Presents a ranking table and interprets which criteria distinguish the best and worst alternatives. Suitable for researchers working on multicriteria ranking problems such as supplier selection, product/service comparisons, and clinical decision support.
AHP Criterion Weighting Writer
Transforms the user's AHP (Analytic Hierarchy Process) pairwise comparison matrix outputs (criterion weights and consistency ratio) into an academic “Criterion Weighting” section that can be added directly to a paper. If the consistency ratio (CR) exceeds 0.10, it states this clearly without hiding it. Who it's for: Researchers and graduate students who use MCDM methods such as TOPSIS/VIKOR and determine criterion weights with AHP.
PLS-SEM Report + Objection Engine
Produces a complete Measurement Model + Structural Model report from your PLS-SEM analysis outputs (outer loadings, CR/AVE, HTMT discriminant validity, path coefficients, R²/f²/Q², SRMR). Correctly applies the HTMT threshold and R² interpretation (Cohen/Chin criteria) and avoids causal language. It also pre-identifies at least 4 objections journal reviewers frequently raise (e.g., "why PLS over CB-SEM," "no CMB control reported") and suggests a defense sentence for each. Suitable for: business/social science researchers using PLS-SEM for structural equation modeling who want methodological robustness before journal submission.
Validity-Reliability Report Writer
Produces a complete manuscript-ready Methods + Results text from your EFA or CFA analysis outputs (KMO, Bartlett's test, factor loadings, fit indices, Cronbach alpha/CR/AVE). Correctly classifies fit index thresholds (CFI/TLI≥.90, RMSEA≤.08, etc.) and reports validity issues like AVE<0.50 without hiding them. Suitable for: academics and graduate students conducting scale development or adaptation studies.
Cronbach Alpha Reliability Writer
Converts the reliability statistics you enter (Cronbach alpha, item count, subscales) into an academic "Reliability Analysis" paragraph ready to drop straight into your manuscript. Provides an acceptability table by subscale and, if it detects a low alpha (<.60), states this openly rather than hiding it. Suitable for: graduate students and researchers using scales who want to write the method section quickly and correctly.
PLS-SEM Report & Rebuttal
Generates a complete Measurement Model + Structural Model report from your PLS-SEM analysis outputs (outer loadings, CR/AVE, HTMT discriminant validity, path coefficients, R²/f²/Q², and SRMR). It correctly applies the HTMT threshold and interprets R² using the Cohen/Chin criteria, while avoiding causal language. It also anticipates at least 4 common journal reviewer objections (e.g., “Why was PLS chosen instead of CB-SEM?” and “No CMB test was conducted”) and provides a defense statement for each. Suitable for: business and social science researchers conducting structural equation modeling with PLS-SEM and seeking methodological rigor before journal submission.
Validity & Reliability Writer
Generates a complete, publication-ready Methods + Results section from your EFA or CFA analysis outputs (KMO, Bartlett’s test, factor loadings, fit indices, Cronbach’s alpha/CR/AVE). Correctly classifies fit-index thresholds (CFI/TLI≥.90, RMSEA≤.08, etc.) and reports validity concerns such as AVE<0.50 without hiding them. Suitable for academics and graduate students conducting scale development or adaptation studies.
Cronbach Alpha Writer
Transforms the reliability statistics entered by the user (Cronbach's alpha, item count, and subscales) into an academic “Reliability Analysis” paragraph ready to add directly to a paper. Provides an acceptability table for each subscale and clearly reports low alpha values (<.60) without hiding them. Suitable for: graduate students and researchers using scales who want to write the methods section of a thesis or paper quickly and accurately.
CONSORT 2010 Report Writer
From your randomized controlled trial (RCT) data — randomization method, blinding status, power analysis, CONSORT flow numbers (screened/randomized/allocated/analyzed), ITT/PP analysis approach — it produces a complete Abstract + Methods + Results draft compliant with all 25 items of CONSORT 2010. It never confuses ITT/PP; it checks the consistency of flow diagram numbers (randomized ≥ allocated ≥ followed-up ≥ analyzed). It also pre-identifies at least 4 objections journal reviewers frequently raise (e.g., "why wasn't an ITT analysis reported," "was blinding success tested," "was there outcome switching") and suggests a defense sentence for each. Designed for clinical researchers in medicine/nursing/pharmacy conducting RCTs who want methodological robustness before journal submission. What it does not do: it does not perform statistical analysis or generate data — it only converts results you already have into a CONSORT-standard report.
STROBE Methods Writer
Based on your cross-sectional, cohort, or case-control study design; when you enter your participant selection method, variables, sample size rationale, and statistical methods, it produces a complete Methods and Results draft with implicit reference to all 22 items of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist. It also provides a mapping table at the end showing which paragraph corresponds to which STROBE item — ready to answer a journal's "please complete the STROBE checklist" request. Avoids causal language (uses "is associated with" rather than "causes" for observational studies) and explicitly discusses sources of bias/confounding. Suitable for academics working in epidemiology, public health, and clinical research.
Missing Data Reporting Assistant
When you enter your sample size, missing data proportion/pattern (MCAR/MAR/MNAR), missing data method (listwise deletion, multiple imputation, etc.), and outlier detection method (Z-score, IQR, Mahalanobis), it produces an academic paragraph that can be inserted directly into the "Data Pre-processing" section of your manuscript. If there is a methodological contradiction between your method choice and the missing data pattern (e.g., using listwise deletion under an MNAR pattern), it states this explicitly. Suitable for: graduate students and researchers doing survey/clinical data analysis who want to write the data pre-processing section quickly and correctly.
Scale Development Data Audit
This skill systematically audits raw survey data collected as part of a scale development study for missing data, outliers, careless responding (straightlining, longstring, random responding), item-level distribution problems, and the prerequisites for reliability and factorability. It does not produce final EFA/CFA results; instead, it provides an evidence-based “go/no-go” decision on whether the data are ready for subsequent analyses, along with a recommendation for a stratified sample split (EFA/CFA split). All calculations are performed by running real code (Python: pandas, scipy, factor_analyzer, pingouin); no assumed numbers are generated.