# AxisAgile Apps (@AxisAgileApps) on youtube

- id: koinon:creator:youtube_axisagileapps
- url: https://koinonlink.com/kol/youtube/AxisAgileApps
- schema_version: 1.4

## Identity
- platform: youtube
- handle: AxisAgileApps
- platform_url: https://www.youtube.com/@AxisAgileApps
- country: Australia
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 15900
- posts_observed: 5
- verified: false
- size_tier: koc

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- 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: devtools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: Jira features for sprint readiness indicators, JQL queries for issue tracking and filtering, Product backlog and sprint management in Jira, Agile/Scrum practices using Jira tools
- bio: Welcome! This channel is all about helping you understand how to use Jira with Scrum, and other popular Agile frameworks.

## Audience (inferred)
- status: present
- description: Software development teams and Agile practitioners using Jira for Scrum, mainly Scrum Masters, Product Owners, and team members handling backlogs and sprints
- roles: dev
- seniority: practitioner
- pain_points: Identifying and marking sprint-ready items in the product backlog, Writing JQL queries to locate specific issues or updates quickly, Tracking estimates, time logs, and status on sub-tasks and issues, Keeping the backlog clean and up-to-date over long periods
- posts_analysed: 14
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.6
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 47
- authenticity_basis: profile_shape_only
- credibility_score: 47
- flagged_for_review: false
- koinon_bd_grade: B — 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
- [youtube_axisagileapps#c87a92d3] Using "Card Colors" in Jira to indicate "Sprint Ready" Product Backlog Items #shorts (https://www.youtube.com/@AxisAgileApps)
- [youtube_axisagileapps#e86833f8] Using labels in Jira to indicate "Sprint Ready" Product Backlog Items #shorts
- [youtube_axisagileapps#61c8d704] Using the Jira "Flag" feature to indicate "Sprint Ready" Product Backlog Items #shorts
- [youtube_axisagileapps#102fadf8] Using Jira Sprints to indicate Sprint Ready Product Backlog Items #shorts
- [youtube_axisagileapps#b45284b6] What are "Sprint Ready" Product Backlog Items? #shorts
- [youtube_axisagileapps#5f29a13e] Find Product Backlog items completed in the last Sprint #shorts
- [youtube_axisagileapps#30cda968] JQL: Find issues in a future Sprint with no Story Point estimate #shorts
- [youtube_axisagileapps#7b872004] JQL: Find sub-tasks that are in progress with no time logged #shorts

## 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=55, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=55, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=55, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=55, 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=55, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=86, 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=0.25, freshness_days=55, 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=86, 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=86, method=platform_profile

## Freshness
- profile_checked_days_ago: 86
- content_analysed_days_ago: 55
- audience_updated_days_ago: 55

## 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=youtube_axisagileapps
- Request removal: https://koinonlink.com/claim?k=youtube_axisagileapps
- Terms: https://koinonlink.com/for-ai