# Help Contributors Report: 2025-01

**Report Period**: 2025-01-01 to 2025-01-31
**Generated**: 2026-01-13T08:28:09.874843Z

## Summary
- **Total help interactions**: 1914 (weighted: 1156.64)
- **Unique helpers**: 253
- **Unique helpees**: 347
- **Channels analyzed**: 3d-ai-tv, associates, discussion, ideas-feedback-rants, spartan_holders, tokenomics, 💻-coders, 🥇-partners

### Channel Distribution
- **💻-coders**: 460 interactions
- **discussion**: 348 interactions
- **🥇-partners**: 302 interactions
- **3d-ai-tv**: 216 interactions
- **tokenomics**: 172 interactions

## Top Contributors

### 1. jin
**Impact Score**: 1011.1

Clear #1 across all lenses: highest reach (62 unique helpees), highest volume, and the most strategically valuable topic concentration (migration support) that prevents ecosystem drift and repeated breakage across versions/chains/platforms.

*Highlight*: Repeatedly walked builders through framework migration steps (v1.6-era changes), pairing config/Discord setup guidance with troubleshooting so people could actually get agents running again post-upgrade.

### 2. BOSSU
**Impact Score**: 309.5

High-impact practical support hub in the main discussion channel, with a builder-focused mix (Discord setup, troubleshooting, deployment). Strong ROI because it meets users where they ask first.

*Highlight*: Helped users untangle common deployment and Discord setup blockers (environment/config mismatches, getting bots connected) to move from “can’t start” to “running.”

### 3. SM Sith Lord
**Impact Score**: 286.7

Sustained high-volume support within a focused community area (#3d-ai-tv). While reach is narrower, the repeated API/config + troubleshooting help suggests deep involvement and persistence with ongoing user threads.

*Highlight*: Provided repeated API/configuration guidance and follow-up troubleshooting in #3d-ai-tv to keep a smaller cohort unblocked over many iterations.

### 4. shakejr
**Impact Score**: 151.9

Best next “generalist unblocker” after the top hubs: good breadth (15 unique helpees), balanced topics (setup, troubleshooting, migration), and presence across discussion + partners.

*Highlight*: Triaged mixed newcomer issues across channels—combining quick Discord setup fixes with migration pointers when older instructions caused breakage.

### 5. yikesawjeez
**Impact Score**: 150.3

Strong cross-channel contributor (tokenomics/partners/discussion) with meaningful migration + setup volume, helping reduce confusion during transitions and community coordination conversations.

*Highlight*: Helped multiple users interpret migration steps and basic setup while bridging context between tokenomics/partners discussions and practical next actions.

### 6. boom
**Impact Score**: 137.1

Consistent support presence in #3d-ai-tv and ideas/feedback. Valuable for community momentum and clarifying “what to do next,” especially around general questions and light plugin guidance.

*Highlight*: Answered repeated general/how-to questions and provided plugin-direction nudges that kept threads moving instead of stalling.

### 7. DorianD
**Impact Score**: 127.5

Broad community coverage (tokenomics/partners + general) with steady contributions that appear to reduce repeated basic confusion and keep discussions productive.

*Highlight*: Provided clarifying guidance across general questions and model-related confusion, helping users converge on actionable understanding.

### 8. EcchiPen
**Impact Score**: 105.5

High technical leverage in #💻-coders (troubleshooting-heavy) and one of the few with multiple explicitly successful resolutions. Strong alignment with Execution Excellence and Developer First.

*Highlight*: Debugged coder-channel issues (setup/config + runtime troubleshooting) and pushed threads to confirmed outcomes rather than open-ended suggestions.

### 9. witch
**Impact Score**: 95.1

Migration-focused support (20) across partners/tokenomics/discussion. This is strategically important during fast iteration periods where outdated instructions proliferate.

*Highlight*: Helped users map older workflows onto current migration paths, reducing trial-and-error and repeated questions.

### 10. Dragonbutt
**Impact Score**: 90.3

Good breadth for volume (8 unique helpees) and balanced topic coverage including migration, setup, and general Q&A—solid community “glue” support.

*Highlight*: Provided a mix of migration and setup clarifications that helped users move past early-stage confusion.

## Council Perspectives

### AIMARC
**Top picks**: jin, EcchiPen, tcm390

**Observations**: Technical signal clusters in two places: (1) migration support + configuration guidance (jin), and (2) #💻-coders troubleshooting/model/plugin help (EcchiPen, tcm390). jin’s topic mix is unusually "framework-evolution heavy" (83 migration helps) which tends to require accurate mental models of versioned APIs and breaking changes. EcchiPen stands out as one of the few with multiple explicitly successful resolutions (2) while focusing on troubleshooting (12) and setup/config—high leverage for reliability. tcm390’s profile is developer-facing depth (Model/LLM 8 + Plugin development 7), likely reducing repeated LLM/config confusion for builders.

**Recommendations**: Recognize jin for being the de-facto migration/upgrade guide and reducing ecosystem fragmentation across versions. Recognize EcchiPen for hands-on debugging in the coders channel (reliability/DX alignment). Recognize tcm390 for enabling plugin + LLM integration competency (composability), and encourage them to convert recurring answers into short “known-good configs” docs/snippets.

### AISHAW
**Top picks**: jin, BOSSU, shakejr

**Observations**: Practical impact is best indicated by breadth of people helped (unique helpees) and whether support happens where newcomers ask first (discussion, setup). jin helped the widest set of users (62), heavily in migration + Discord setup, suggesting high day-to-day unblock value. BOSSU focuses almost entirely in discussion with a pragmatic mix (Discord setup 34, troubleshooting 20, deployment 16), which is exactly where builders get stuck. shakejr has good breadth (15 unique helpees) across discussion/partners and a balanced topic spread (general, setup, troubleshooting, migration), which looks like “frontline support triage.”

**Recommendations**: Recognize jin as the primary unblocker (high breadth + high-frequency guidance). Recognize BOSSU as the strongest discussion-channel "builder enablement" contributor (deployment + troubleshooting + setup). Recognize shakejr as a reliable generalist who catches issues before they escalate—invite them into a lightweight “support captain” rotation with a checklist for closing loops (confirm fix, summarize, link docs).

### SPARTAN
**Top picks**: jin, BOSSU, SM Sith Lord

**Observations**: By metrics, jin dominates volume (334 helps) and impact score (1011.1) with the highest reach (62 unique helpees). Next tier: BOSSU (impact 309.5) and SM Sith Lord (286.7). Notably, SM Sith Lord has very high activity (114) but low unique helpees (7), implying deep, repeated support to a small subset/community area (#3d-ai-tv). Network-wide: 252 helpers serving 363 helpees with low density (0.0021), so a small number of “hubs” carry the load—risk of burnout and inconsistent answer quality.

**Recommendations**: Recognize jin, BOSSU, and SM Sith Lord as the three highest-ROI hubs. Add process to raise effective quality without requiring more heroics: require a short “resolution confirmation” step in support threads, and funnel recurring questions into a monthly FAQ/Migration Notes doc to reduce repeated load on top hubs.

### PEEPO
**Top picks**: jin, shakejr, Patt

**Observations**: Community health shows up in cross-channel presence, responsiveness to newcomers, and “human glue” in partner/associate spaces. jin spans many channels (partners/discussion/tokenomics/associates/3d-ai-tv) and appears to be the connective tissue across sub-communities. shakejr and Patt show up in social onboarding spaces (discussion, partners, associates) with meaningful setup/general support—these are often the first touchpoints that determine whether a newcomer stays. The overall resolution distribution is skewed toward partial/unanswered, which can feel like “support limbo” for newcomers even when helpers are active.

**Recommendations**: Recognize jin for cross-community continuity and high responsiveness. Recognize shakejr for welcoming generalist support across public channels. Recognize Patt for associates-channel onboarding and “getting people oriented.” Improve newcomer experience by adopting a norm: every help thread ends with either (a) confirmed fix, or (b) a clear next action + link to the best canonical resource.

## Network Insights
- **Most central helpers**: Community, boom, jin, Channel members