# 葉っぱの葉(Yoh) (@hskfwy) on x

- id: koinon:creator:x_hskfwy
- url: https://koinonlink.com/kol/x/hskfwy
- schema_version: 1.4

## Identity
- platform: x
- handle: hskfwy
- platform_url: https://twitter.com/hskfwy
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 1159
- following: 1436
- posts_total: 2914
- posts_observed: 6
- posts_per_day: 5.103
- account_age_days: 571
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00431
- best_post_interactions: 5
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: martech
- category_confidence: 0.8
- category_source: derived_from_recent_content
- topics: Data visualization storytelling, Data challenges (e.g., DATASaber), Tableau Public usage, Personal data analytics, MCP server costs, Self-driven learning in data skills

## Audience (inferred)
- status: present
- description: Data visualization and analytics enthusiasts, mainly early-career practitioners and learners in Japan who are participating in data challenges like DATASaber
- roles: data
- seniority: practitioner
- pain_points: Struggling to find unique content or articles to write on data skills, Having difficulty deciding how to structure storytelling and datasets from personal wellness data, Facing unexpected costs when using MCP servers, Running out of time due to high pacing in data challenges
- posts_analysed: 8
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 40
- authenticity_basis: engagement_observed
- credibility_score: 40
- flagged_for_review: false
- koinon_bd_grade: C — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_hskfwy#d5262ea1] 予定入ってしまったので、断念。。。 (https://twitter.com/hskfwy)
- [x_hskfwy#feddfddc] Ord4提出。ハイペースで行って、復習に時間を当てたい #DATASaber 86/90
- [x_hskfwy#9bd4c7db] 「あなたのウェルネス」から、どういうストーリーテリングとデータセットにするかが難しい #VizGames
- [x_hskfwy#07a3c183] sf-skillsの書こうとしたら、いい記事先に書かれてしまった！
- [x_hskfwy#1fb77b99] Apprenticeたるもの、連絡を待つな！自分から情報を取りに行け！は1回目の反省点 #DATASaber https://t.co/ss0bzLJGXF
- [x_hskfwy#32934b26] Tableau Publicで公開するということは、DATA Saberのチャレンジとしても作れますね？ #DATASaber
- [x_hskfwy#c4c7880c] MCPサーバーにクレジットかかる‥だと‥？ https://t.co/SMuIFSfd6B
- [x_hskfwy#00f78f8e] なんか私出てきた笑 #DATASaberBridge202607

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=80, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=80, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=121, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.5, freshness_days=80, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=121, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=121, method=platform_profile

## Freshness
- profile_checked_days_ago: 121
- content_analysed_days_ago: 80
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 12
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_hskfwy
- Request removal: https://koinonlink.com/claim?k=x_hskfwy
- Terms: https://koinonlink.com/for-ai