Marketing Team
PicnicCheesecakePopcorn
Team and agent shape derived from indexed Markdown.
U-level framework used to resolve context conflicts.
Navigate public zstack Markdown by folder and source file.
Executives and specialists loaded from each agent.md file.
Current operating groupings for coordinated agent work.
Culture, engineering standards, preferences, decisions, and operating context.
Reusable zstack workflows and custom skills.
Reusable Markdown templates and references.
Accepted operating choices and understanding-level decisions.
Completed ship records from the release manager.
Resolved conflicts between agent context and operating rules.
Ad hoc operator notes saved directly into zstack.
Indexed public zstack Markdown, searched from Postgres.
| Source | Collection | Size | Excerpt |
|---|---|---|---|
Kitty High-Fidelity Stock Regeneration Log organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_high_fidelity_stock_regeneration_log.md | organization | 138.7 KB | Batch: 2026-05-02 large 50-plan generated thumbnail batch Reason: Zack rejected the procedural/Pillow regeneration pass as blockish and low-resolution. This pass replaced those assets with high-fidelity CC0/Public Domain Mark stock images discovered via Openverse and cropped/downsampled to the existing 800x800 PNG filenames. Previous assets backed up to: `C:\Developer\interactor-service\devtools\training_plans_generator\generated_assets\low_fidelity_backup_20260502-184255` Manifest: `C:\Devel... |
2026-05-02-large-batch-50 Image Generation Log organization/training-plan-generation-logs/2026-05-02-large-batch-50/2026-05-02-large-batch-50_image_generation_log.md | organization | 122.4 KB | Zack later asked Panda to fix the low-step/blocky output problem and work with Kitty to regenerate the images for all 50 plans. Panda delegated five Kitty workers to create high-fidelity per-plan source plans over ranges 01-10, 11-20, 21-30, 31-40, and 41-50, each using the exact plan name and full plan description as input. Because the local Image API CLI path was blocked by a missing `OPENAI_API_KEY`, this replacement pass used Kitty-approved high-fidelity stock selection instead of procedu... |
Interactor Feature Ideas organization/executives/feature-lead/interactor-feature-ideas.md | organization | 40.8 KB | This file captures Zack's product and feature ideas before they are turned into scoped feature requests. Ideas here are not committed roadmap items. The Feature Lead should clarify, prioritize, and involve the right specialists before implementation. Date: 2026-05-10 Status: P1, P3, P4, P8, P9, P11, P12, P14, P15, P16, P17, and P18 have been merged. P3, P14, P15/P16/P18, and P11/P12/P17 have been released end to end to dev and prod. Date: 2026-05-10 Source: Zack / Panda triage Owner: Hammer /... |
2026-05-02-large-batch-50 Crafting Log organization/training-plan-generation-logs/2026-05-02-large-batch-50/2026-05-02-large-batch-50_crafting_log.md | organization | 40.3 KB | Existing-exercise first: all selected movements come from `.claude/prompts/Exercise-library.txt` and no default exercise additions were needed. Beginner plans use stable movements, modest volume, and simple progression so users can practice instead of constantly relearning exercises. Intermediate plans add frequency or density only when the schedule and recovery demands make sense. Every strength-oriented plan includes a reasonable mix of push, pull, squat/lunge, hinge, and core or mobility w... |
Google Ads Specialist Learning: Foundational Best Practices organization/specialists/google-ads-specialist/learning/2026-04-27-foundational-best-practices.md | organization | 34.3 KB | Date: 2026-04-27 Scope: Google Ads and paid-search foundations for Interactor: keyword strategy, match types, bidding, conversion tracking, attribution, auction insights, search terms, landing pages, experimentation, and campaign diagnostics. Sources emphasize Google official documentation, then practitioner sources and practical sentiment. 1. What should the Google Ads Specialist treat as the foundation of paid-search performance before recommending spend, bidding, or keyword expansion? 2. H... |
Fitness Specialist Learning: Foundational Best Practices organization/specialists/fitness-specialist/learning/2026-04-27-foundational-best-practices.md | organization | 32.2 KB | Date: 2026-04-27 Scope: Exercise science and coaching foundations for Interactor plan quality, including hypertrophy, strength, endurance, periodization, mesocycles, progression, fatigue management, recovery, risk-reward, special populations, sports performance, coaching practice, and current fitness trends. Sources are current through April 27, 2026. No prior Fitness Specialist learning notes existed at the start of this pass. 1. What principles should govern resistance training plans for he... |
Business Director Learning: Foundational Growth And Marketing Best Practices organization/executives/business-director/learning/2026-04-27-foundational-best-practices.md | organization | 30.3 KB | Date: 2026-04-27 Scope: Growth strategy, positioning, acquisition, conversion, analytics, pricing and monetization readiness, experimentation, channel strategy, and marketing leadership for Interactor. This pass uses Interactor's current loop as the working context: beginners looking for workout plans, Google Ads as the initial acquisition channel, product refinement and conversion tracking as the near-term bottlenecks, and paid plans as a later goal. 1. What should Interactor's first coheren... |
SEO Specialist Learning: Foundational SEO Best Practices organization/specialists/seo-specialist/learning/2026-04-27-foundational-best-practices.md | organization | 25.1 KB | Date: 2026-04-27 Scope: Technical SEO, on-page SEO, content SEO, structured data, search intent, internal linking, crawlability, indexability, performance, search quality guidance, and tasteful keyword use for Interactor and zstack. 1. What does Google currently treat as the durable baseline for search eligibility, crawling, indexing, and page understanding? 2. How should Interactor use keywords, page structure, titles, descriptions, and visible content without slipping into search-engine-fir... |
Feature Lead Learning: Foundational Engineering Best Practices organization/executives/feature-lead/learning/2026-04-27-foundational-best-practices.md | organization | 25.1 KB | Date: 2026-04-27 Scope: Software architecture, clean code, maintainable full-stack design, modularity, testability, performance, cost-aware engineering, developer tooling, refactoring, and AI-agent-friendly engineering workflows for Interactor and zstack. 1. What durable architecture practices help a small full-stack product stay easy to change as features, growth work, and AI-agent collaboration increase? 2. How should clean code, modularity, and refactoring be applied without creating specu... |
Generate Workout Plans skills/generate-workout-plans/SKILL.md | skills | 24.9 KB | --- name: generate-workout-plans description: > Generate Interactor training plan Python scripts through an organization-level workflow spanning Dev Team, Marketing Team, Fitness Specialist/Button, Training Plan Image Sketcher/Kitty, and Panda coordination. Use this skill whenever the user asks to generate workout plans, create training plans, make new training routines, research plan ideas, or produce training plan code/images/logs for the platform. Also trigger when the user says "generate... |
Kitty Image Regeneration Segment 21-30 organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_image_regeneration_segment_21_30.md | organization | 23.0 KB | Batch: 2026-05-02-large-batch-50 Scope: regenerated only plan indexes 21-30 and wrote final 800x800 PNG assets using approved generated/drawn art directions. No stock images were used, so no external attribution is required. **Plan description used as input:** 8-Week Beginner Gym Machine Confidence Plan is a 8-week beginner training plan for first-time gym users learning machines. It runs 3 days per week (Monday, Wednesday, Friday) and uses gym access, machines so the plan is easy to match to... |
Feature Lead Agent organization/executives/feature-lead/agent.md | organization | 23.0 KB | Name: Hammer Role: Feature Lead Type: Executive The Feature Lead owns the technical direction and execution quality of feature development across Interactor's full stack. The Feature Lead understands both `C:\Developer\interactor-service` and `C:\Developer\interactor-web`. The Feature Lead should think like a highly experienced software engineer and tech lead: clarify the feature, challenge weak assumptions, propose strong implementation options, and make sure the resulting code is clean, mai... |
Generic Agent Definition organization/agent-definition.md | organization | 22.6 KB | This is the shared, iterative template for agents in Zack's organization. Every `agent.md` should start with: ```markdown Name: <Agent name> Role: <Agent role> Type: Executive | Specialist ``` `Name` comes first so Zack can refer to the agent naturally. Agent names should be declared only in the header. After the header, describe and refer to the agent by role rather than by name so names can be changed without rewriting the full brief. Use these sections unless a role has a good reason to di... |
Kitty Image Regeneration Segment 01-10 organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_image_regeneration_segment_01_10.md | organization | 22.4 KB | Batch: 2026-05-02 large 50-plan generated thumbnail batch Source input: `C:\Developer\zstack\organization\training-plan-generation-logs\2026-05-02-large-batch-50\kitty_regeneration_inputs_01_10.json` Scope: regenerated only plan indexes 01-10 using the existing asset filenames from the JSON. All outputs are 800x800 PNG files, contain no in-image text/logos/watermarks, and use approved generated/drawn styles only. **Plan description used as input:** 8-Week Beginner Walking Strength Starter is... |
Decision Log organization/decision-log.md | organization | 22.0 KB | Understanding Level: U2 This file records decisions whose reasoning should be remembered. It is not for every small reversible choice. Use this when a decision affects future agent behavior, product direction, architecture, marketing direction, zstack structure, or conflict resolution. ```markdown Status: Proposed | Accepted | Superseded | Reversed Understanding Level: Owner: Context: Decision: Rationale: Tradeoffs: Revisit Trigger: Related Files: ``` Status: Accepted Understanding Level: U0... |
Kitty Image Regeneration Segment 41-50 organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_image_regeneration_segment_41_50.md | organization | 21.8 KB | Batch: 2026-05-02 large 50-plan batch Source input: `C:\Developer\zstack\organization\training-plan-generation-logs\2026-05-02-large-batch-50\kitty_regeneration_inputs_41_50.json` All assets are local generated/drawn PNGs using approved visual directions only. No stock images, in-image text, logos, watermarks, copyrighted characters, or brand references were used. **Plan description used as input:** 8-Week Tennis and Racquet Sport Strength Base is a 8-week intermediate training plan for racqu... |
Kitty Image Regeneration Segment 31-40 organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_image_regeneration_segment_31_40.md | organization | 21.0 KB | Range owner: Kitty, Training Plan Image Sketcher Scope: regenerated only Interactor large 2026 May plan image assets for indexes 31-40 using the exact JSON plan names, descriptions, and filenames from `kitty_regeneration_inputs_31_40.json`. Batch style mix: soft semi-geometric local generated bitmap: 3; pixel art local generated bitmap: 2; rough graphite manga-animation sketch local generated bitmap: 3; retro sports-shonen anime cel local generated bitmap: 2. Stock was not used in this segmen... |
Kitty Image Regeneration Segment 11-20 organization/training-plan-generation-logs/2026-05-02-large-batch-50/kitty_image_regeneration_segment_11_20.md | organization | 20.4 KB | Batch: 2026-05-02 large 50-plan batch Assigned range: plans 11-20 Source input: `C:\Developer\zstack\organization\training-plan-generation-logs\2026-05-02-large-batch-50\kitty_regeneration_inputs_11_20.json` Output rule: one final 800x800 PNG per plan, overwriting only the assigned `large_2026_may_11_*.png` through `large_2026_may_20_*.png` assets. Plan description used as input: 8-Week Dumbbell Strength Starter for Beginners is a 8-week beginner training plan for new lifters with a pair of d... |