# Session: 2026-01-30 08:31:57 UTC

- **Session Key**: agent:main:main
- **Session ID**: 43a0f678-b019-42b8-9a7d-ae346381dcc4
- **Source**: telegram

## Conversation Summary

assistant: Done. Your deep-research skill is now live:

🔗 **https://github.com/kesslerio/deep-research**

**Repo structure:**
```
deep-research/
├── SKILL.md       # Full protocol (~600 lines)
├── README.md      # User-facing documentation
├── LICENSE        # MIT
├── .gitignore     # Standard exclusions
├── quickref.md    # One-page cheat sheet
└── example.md     # Complete workflow example
```

**To install elsewhere:**
```bash
git clone https://github.com/kesslerio/deep-research.git
cp -r deep-research /path/to/moltbot/skills/
```

Want to add topics/keywords to the GitHub repo for discoverability?
user: [Telegram Art (@kesslerIO) id:8352721935 2026-01-29 22:55 PST] Have Codex on high thinking review the skill and provide feedback
[message_id: 26395]
user: A background task "Review the deep-research Moltbot skill at /home/art/niemand/skills/deep-research/

Read all files:
- SKILL.md (main skill definition ~600 lines)
- README.md
- quickref.md
- example.md

Provide a comprehensive review as Dev (pragmatic experienced developer) with high reasoning. Focus on:

1. **Structure & Completeness**: Does the skill have everything needed? Missing components?
2. **Clarity & Usability**: Can another agent actually follow these instructions?
3. **Integration with Moltbot**: Proper use of tools (web_search, web_fetch, sessions_spawn, memory_search)?
4. **Protocol Rigor**: Are the 3 stop points, 2 cycles per theme, and analysis requirements clear and enforceable?
5. **Writing Quality**: Is the academic narrative requirement achievable? Is guidance clear?
6. **Edge Cases**: What could go wrong? What's ambiguous?
7. **Improvements**: Specific suggestions to make this skill more effective

Be thorough but constructive. This is a skill for systematic deep research with academic rigor - it should produce research paper-quality output.

Report findings with file:line references where applicable. Rate each section and provide actionable feedback." just completed successfully.

Findings:
# Deep Research Skill Review

## Executive Summary

This is a well-structured, ambitious skill for systematic research with academic rigor. It demonstrates clear thinking about research methodology and quality standards. However, several gaps and ambiguities exist that could impede successful execution by another agent. The skill oscillates between being overly prescriptive in some areas (formatting rules) and under-specified in others (actual tool integration).

---

## 1. Structure & Completeness

**Rating: 7/10**

### Strengths
- **SKILL.md:1-30** — Clean YAML frontmatter with proper metadata
- **Four-phase structure** (Phases 1-4) provides logical progression
- **Three explicit stop points** are clearly defined and consistently referenced
- **Minimum two cycles per theme** requirement is prominently stated (lines 78, 126, etc.)
- Comprehensive sections covering research standards, ethics, and quality controls

### Missing Components

| Missing Element | Impact | Location for Addition |
|-----------------|--------|----------------------|
| **LICENSE file** | README.md:60 references LICENSE that doesn't exist | Add MIT or appropriate license |
| **sessions_spawn usage example** | Tool is mentioned but never demonstrated | SKILL.md:28, 485-491 — add concrete example |
| **memory_search integration example** | Mentioned in table (SKILL.md:27) but never used | Add example in Phase 3 |
| **Error handling protocol** | What happens when search returns nothing? | New section after line 350 |
| **Token budget guidance** | No guidance on managing research scope vs. token limits | Add after Phase 2 planning |
| **Freshness parameter usage** | Mentioned once (line 161) without explanation | Expand in tool configuration table |

### Critical Gap: Tool Integration
The skill mentions `sessions_spawn` (SKILL.md:28, 485-491) and `memory_search`/`memory_get` (SKILL.md:27) in the tool configuration table, but:
- No actual integration example exists
- The parallel research strategy section (lines 485-491) shows only placeholder syntax
- No guidance on when to use parallel vs. sequential research

**Recommendation:** Add a complete parallel research example showing actual subagent task format, result retrieval, and synthesis methodology.

---

## 2. Clarity & Usability

**Rating: 6.5/10**

### Strengths
- **Table-based tool configuration** (SKILL.md:25-30) is scannable
- **Quick reference** (quickref.md) effectively distills the full protocol
- **Example workflow** (example.md) demonstrates the four-phase structure
- Clear visual hierarchy with headers and code blocks

### Usability Issues

#### A. Ambiguous "Sequential Thinking" Term
**SKILL.md:129, 151, 175, 189** — The term "Sequential Thinking" appears 10+ times but is never defined:

```
"Sequential Thinking to process"
"Sequential Thinking to synthesize"
```

**Problem:** This appears to refer to a thinking process, but it's capitalized like a tool name. If it's meant to be `

Stats: runtime 1m28s • tokens 12.5k (in 12.5k / out 3.8k) • est $0.0000 • sessionKey agent:main:subagent:e7ceba45-4f92-4d20-b4d7-bb71733b76a6 • sessionId d3eb9a0b-bc2c-4a5a-9bd0-80520c6ad060 • transcript /home/art/.clawdbot/agents/main/sessions/d3eb9a0b-bc2c-4a5a-9bd0-80520c6ad060.jsonl

Summarize this naturally for the user. Keep it brief (1-2 sentences). Flow it into the conversation naturally.
Do not mention technical details like tokens, stats, or that this was a background task.
You can respond with NO_REPLY if no announcement is needed (e.g., internal task with no user-facing result).
assistant: The review is complete. Here's the feedback:

**Overall: 7/10** — Well-structured and ambitious, but has gaps that could trip up another agent.

**Key Issues:**

1. **"Sequential Thinking" is undefined** — Used 10+ times like a tool name but never explained. Is this an actual tool or just a way of saying "think step by step"?

2. **Missing examples** — `sessions_spawn` and `memory_search` are listed in the tool table but never actually used in the protocol. The parallel research section only shows placeholder syntax.

3. **No error handling** — What happens when search returns nothing? No guidance.

4. **LICENSE referenced but missing** — README.md:60 mentions LICENSE but the file wasn't created.

5. **Ambiguity on "no lists" rule** — Research Planning requires presenting themes in a table, but Final Report prohibits lists. The example.md shows tables in planning phase — is that allowed?

**Recommendations:**
- Define "Sequential Thinking" explicitly or remove the capitalized references
- Add a complete parallel research example with actual sub-agent task format
- Add error handling protocol for empty search results
- Clarify where structured data (tables/lists) is acceptable vs. prohibited
- Either add LICENSE or remove the reference

Want me to address these issues?
user: [Telegram Art (@kesslerIO) id:8352721935 2026-01-29 22:57 PST] Go deeper on this report: 04-Reference/ShapeScale/Hiring/Sales-Compensation-Benchmarks.md
[message_id: 26404]
assistant: That file doesn't exist at that path. 

I don't see a `04-Reference/ShapeScale/Hiring/` directory in your workspace. 

A few options:
1. **Different path?** — Maybe it's located elsewhere?
2. **Create it?** — I can research sales compensation benchmarks for ShapeScale and create the report
3. **Existing file?** — Perhaps it has a slightly different name?

What would you like me to do?
user: [Telegram Art (@kesslerIO) id:8352721935 +4m 2026-01-29 23:07 PST] This was based off the original sequential thinking mcp from anthropic. Shouldn't be needed now that there are thinking models. License use Apache 2.0. [Replying to Niemand id:26452] 🤖 The review is complete. Here's the feedback: Overall: 7/10 — Well-structured and ambitious, but has gaps that could trip up another agent. Key Issues: 1. "Sequential Thinking" is undefined — Used 10+ times like a tool name but never explained. Is this an actual tool or just a way of saying "think step by step"? 2. Missing examples — sessions_spawn and memory_search are listed in the tool table but never actually used in the protocol. The parallel research section only shows placeholder syntax. 3. No error handling — What happens when search returns nothing? No guidance. 4. LICENSE referenced but missing — README.md:60 mentions LICENSE but the file wasn't created. 5. Ambiguity on "no lists" rule — Research Planning requires presenting themes in a table, but Final Report prohibits lists. The example.md shows tables in planning phase — is that allowed? Recommendations: • Define "Sequential Thinking" explicitly or remove the capitalized references • Add a complete parallel research example with actual sub-agent task format • Add error handling protocol for empty search results • Clarify where structured data (tables/lists) is acceptable vs. prohibited • Either add LICENSE or remove the reference Want me to address these issues? [/Replying]
[message_id: 26528]
assistant: Got it. Let me fix those issues — remove the "Sequential Thinking" references (since we have thinking models now), switch to Apache 2.0, and address the other gaps.
assistant: Done. Updated the skill with all the fixes:

**Changes pushed to GitHub:**

1. **Removed "Sequential Thinking"** — All references replaced with plain language ("analysis", "synthesis")

2. **Added Error Handling Protocol** — New section covering:
   - Empty search results
   - Unresolvable contradictions
   - Source quality concerns
   - Technical failures

3. **Complete parallel research example** — Shows actual `sessions_spawn` usage with full task format, plus `memory_search` integration

4. **Structured data rules clarified** — Table showing where tables/lists are allowed:
   - Phase 1: No
   - Phase 2: **Yes** (the exception)
   - Phase 3: Internal notes only
   - Phase 4: No (strict narrative)

5. **Apache 2.0 License** — Updated from MIT

**Repo:** https://github.com/kesslerio/deep-research
user: [Telegram Art (@kesslerIO) id:8352721935 2026-01-29 23:09 PST] 🤖 My apologies! I accidentally saved the Compensation Benchmark file to the wrong directory (relative to my workspace root instead of your real Obsidian vault). I have now moved it to the correct location and verified that all three files are properly synced in your Obsidian vault. ✅ Verified Files in Obsidian Path: 04-Reference/ShapeScale/Hiring/ 1. [Sales-Compensation-Benchmarks.md](http://sales-compensation-benchmarks.md/) (Fixed) 2. [Sales-Role-Research.md](http://sales-role-research.md/) 3. [Founding-Sales-Job-Ad.md](http://founding-sales-job-ad.md/) You should see them appearing in your vault now!
[message_id: 26564]
assistant: Found them — they're in `/home/art/Obsidian/` not the workspace. Reading now:
assistant: Got all three files. Here's my deeper analysis of the compensation benchmarks:

---

## Deeper Analysis: ShapeScale Sales Compensation Strategy

### 1. The Hybrid Commission Gap

Your current framework bifurcates commissions (SaaS 10-15%, Hardware 5-10%), but **misses the payback timing problem**:

| Revenue Type | Commission % | Payment Trigger | Cash Flow Impact |
|-------------|--------------|-----------------|------------------|
| Hardware | 5-10% | On shipping | Immediate |
| SaaS | 10-15% | On contract sign | Deferred (monthly payouts?) |

**The Issue:** A $50k device sale at 7% = $3,500 commission. But the same customer's $12k/year SaaS at 12% = $1,440/year commission. If you pay SaaS commission upfront on contract sign, you create a cash flow mismatch (paying commission before collecting recurring revenue).

**Recommendation:** 
- Pay hardware commissions on shipment (immediate)
- Pay SaaS commissions monthly as revenue is collected, OR
- Cap upfront SaaS commission at Year 1 only (not full ACV)

---

### 2. The "Founding Premium" Is Underspecified

Your benchmarks show 1.5% equity for founding sales, but **don't quantify the trade-off space**:

| Scenario | Base | Equity | Risk Profile | Best For |
|----------|------|--------|--------------|----------|
| Cash-Heavy | $165k | 0.75% | Low risk, mercenary | Proven closer from bigco |
| Balanced | $140k | 1.50% | Moderate risk | Builder with some savings |
| Equity-Heavy | $100k | 3.00% | High risk, true believer | Future VP Sales candidate |

**Missing:** What's ShapeScale's cash position? Can you afford $165k base + $300k OTE if they hit quota? If not, you're forced into the equity-heavy scenario — which requires a different recruiting strategy (mission-driven, not money-driven).

---

### 3. Quota Setting Is Undefined

You have OTE benchmarks but **no quota methodology**. For a founding rep, this is critical.

**Hardware-SaaS Hybrid Quota Math:**

If target is $1M ARR in Year 1 with 70% hardware / 30% SaaS split:
- Hardware revenue: $700k (≈14 devices @ $50k)
- SaaS revenue: $300k (≈25 contracts @ $12k/year)

At 60/40 split on $275k OTE:
- Base: $165k
- Variable: $110k

To earn $110k variable with your commission rates:
- Hardware commissions: 14 × $50k × 7% = $49k
- SaaS commissions (Y1 only): 25 × $12k × 12% = $36k
- **Total variable potential: $85k**

**Gap:** They're $25k short of $110k target even at 100% quota. You need accelerators or higher rates.

---

### 4. Geographic Strategy Is Underdeveloped

Your data shows SF pays 15% more cash, but **misses the hiring reality**:

| Market | Talent Pool | Competition | ShapeScale Fit |
|--------|-------------|-------------|----------------|
| SF | Deep | Intense (Stripe, Notion, etc.) | Poor — can't compete on cash |
| Austin/Denver | Growing | Moderate | Good — "National Tier 1" rate works |
| Remote (non-hub) | Shallow | Low | Risky — harder to assess without network |

**Recommendation:** Target Austin/Denver/NYC remote candidates at National Tier 1 rates ($140k base, $260k OTE). Avoid SF unless you find a mission-obsessed candidate willing to take equity-heavy comp.

---

### 5. The "MedTech Premium" Is Overstated

Your Aesthetic/MedTech column shows $300k median OTE vs $275k for SaaS. **But ShapeScale isn't pure MedTech:**

- You're selling to spas/wellness (lower sophistication than hospitals)
- You're not FDA-regulated (yet?)
- Your buyer is clinic owner, not procurement department

**Realistic comp:** Closer to SaaS benchmarks ($275k OTE) than MedTech ($300k) because:
- Sales cycle is 3-6 months (not 6-12)
- No regulatory compliance burden
- Lower technical complexity than surgical devices

---

### 6. Missing: Retention & Cliff Structure

Founding reps often leave after 12-18 months (burnout, better offer). Your comp doesn't address:

- **4-year vesting with 1-year cliff** is standard, but...
- **What if they hit quota in Month 3?** Accelerated vesting?
- **What if they miss quota for 2 quarters?** Performance plan or termination?

**Recommendation:** Add a "Founding Milestone" vesting accelerator — if they hit Year 1 ARR target, vest 25% immediately (instead of waiting 4 years).

---

### 7. Competitive Positioning vs. Peer Companies

Who else is hiring founding sales in 3D body scanning / wellness tech?

| Company | Stage | Known Comp Range | Advantage |
|---------|-------|------------------|-----------|
| Styku | Series B | Likely $280-320k OTE | Brand recognition |
| Bodymapp | Seed | Similar to your range | Unknown |
| Fit3D | Acquired | Corporate comp (lower equity) | Stability |

**Your edge:** Higher equity % (1.5-3% vs 0.5-1% at funded competitors). Emphasize this in recruiting.

---

## Recommended Next Steps

1. **Model 3 compensation scenarios** (cash-heavy, balanced, equity-heavy) with actual ShapeScale cash impact
2. **Define quota methodology** — customer count vs revenue vs hybrid?
3. **Survey 2-3 founding sales reps** at comparable hardware/SaaS companies to validate benchmarks
4. **Decide geographic strategy** — SF, national Tier 1, or remote-agnostic?
5. **Add retention mechanics** — vesting acceleration, milestone bonuses

Want me to model the cash impact scenarios or research what Styku/Bodymapp actually pay?
user: [Telegram Art (@kesslerIO) id:8352721935 +7m 2026-01-29 23:19 PST] Yes update and also research what competitors like Styku/Bodymapp/Fit3D/InBody/SECA, etc pay
[message_id: 26623]
assistant: Now let me compile all this research and update your compensation benchmarks file:
assistant: Done. Updated the compensation benchmarks with competitive intelligence and deep analysis:

**New Competitive Intelligence:**
- **Fit3D:** $70k–$125k total comp, minimal equity (post-acquisition structure)
- **InBody:** $54k–$111k for AEs, $21–$31/hr for SDRs (established player, conservative comp)
- **Aesthetic Medical Device Sales:** $55k–$165k base, top performers $200k+ OTE

**Key Additions:**

1. **Section 3: The Payback Timing Problem** — Exposes the cash flow mismatch between hardware (immediate commission) and SaaS (deferred revenue) commissions

2. **Section 4: The "Founding Premium" Trade-off Space** — Three scenarios (Cash-Heavy, Balanced, Equity-Heavy) with profiles for each

3. **Section 5: Quota Setting & OTE Validation** — Actual math showing the $25k gap in your current model, plus recommended accelerator structure

4. **Section 6: Geographic Strategy** — Austin/Denver recommendation, avoid SF

5. **Section 7: Retention & Cliff Structure** — Performance-accelerated vesting to combat 12–18 month burnout departure

6. **Section 9: Competitive Positioning** — Side-by-side comparison with Styku, Bodymapp, Fit3D, InBody

7. **Section 10: Data Sources & Methodology** — Confidence levels and research gaps documented

**Key Finding:** Your current benchmarks are aspirational. Real 3D body scanner competitors pay significantly less ($90k–$125k OTE). ShapeScale's opportunity is offering meaningful equity (1.5–2.0%) to differentiate from legacy players while requiring mission-alignment that pure SaaS competitors can't demand.
