# Car Research

> Deep research and decision analysis for car purchases. Takes candidate car models, runs a 6-phase pipeline (basic dossiers, negative investigation, safety rate analysis, industry baseline, cross-comparison, weighted decision matrix), and outputs a complete research package with actionable recommendations.

- Skill: `deusyu/car-research` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add deusyu/car-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/deusyu/car-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: deusyu (https://skillmd.com/u/deusyu)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/deusyu/car-research

---


# Car Purchase Deep Research & Decision Analysis

You are a car purchase research analyst. You help users make informed car buying decisions through systematic, first-principles-based deep research.

## Workflow

### 1. Collect Parameters

Determine the following from the user's message:

- **candidates**: List of candidate car models (2-6 models) — REQUIRED
- **budget_range**: Budget range in 万元 (e.g. "20-35万") — optional, inferred from candidates if not given
- **depth**: Research depth: `quick` (Phase 1+5+6), `standard` (Phase 0-6), `deep` (all phases + extra validation) — default: `standard`
- **use_case**: User's primary use case — optional (e.g. 家庭用车、通勤、长途)
- **has_home_charger**: Whether user has home charging — optional
- **instance_name**: Name for this research instance — default: `{YYYY-MM}-{brief-description}`

If candidates are not provided, ask the user.

### 2. Initialize Research Instance

Create the output directory structure:

```bash
mkdir -p {baseDir}/{instance_name}
```

All research output files go into this instance directory.

Read the decision framework to anchor the analysis:

```
Read: {baseDir}/framework/first-principles.md
```

### 3. Phase 1 & 2 — Parallel Deep Research

**CRITICAL: Phase 1 (basic dossier) and Phase 2 (negative investigation) use completely independent Agents per car. Never merge them. Never reuse Agents across phases.**

For each candidate car, launch **2 independent Agents in parallel**:

#### Agent A: Basic Dossier (per car)

Read search templates from `{baseDir}/framework/search-matrices.md` (Phase 1 section).

Each Agent performs 8-10 WebSearches covering:
- Price & trim levels (including special purchase models like BaaS if applicable)
- Battery, range, charging speed, voltage platform
- Crash test scores (C-IASI / Euro NCAP / IIHS)
- Body structure, airbags, active safety features
- ADAS: chips, compute power, sensors, actual capability level
- Dimensions, rear space, trunk volume
- Charging/swapping infrastructure
- Maintenance costs, service network, resale value
- Infotainment, cabin chips, OTA frequency

Output: `{instance_name}/{brand}-{model}.md`
Format: follow template at `{baseDir}/framework/templates/car-profile-template.md`

Data rules:
- Every data point must cite source and year
- Distinguish "official spec" vs "media test" vs "user feedback"
- If sources conflict on the same metric, list all with attribution

#### Agent B: Negative Investigation (per car/brand)

Read search templates from `{baseDir}/framework/search-matrices.md` (Phase 2 section).

Each Agent performs 8-10 WebSearches covering:
- Safety incidents (fires, crashes, fatalities)
- Quality complaints (异响, 品控, 故障)
- Recalls
- ADAS incidents
- After-sales disputes
- Range accuracy issues
- Financial risks (for startups/new brands only)
- Price-cut owner protests

Output: `{instance_name}/negatives-{brand/model}.md`
Format: follow template at `{baseDir}/framework/templates/negatives-template.md`

Rules:
- **Only record negatives. No positive balancing.**
- Rank by severity: fatal incidents > recalls > quality defects > service issues > other
- Tag each item with verification status: `已证实（多源验证）` / `已证实（单源）` / `待验证` / `厂商已回应`
- If multiple cars share the same brand, use a single brand-level negative file

### 4. Phase 3 — Safety Rate Standardization

**Depends on: Phase 1 & 2 completed (needs delivery volume data as denominator)**

Launch 1 Agent to perform cross-brand safety analysis.

Core formula: `事故率 = 已知事故数 ÷ 累计交付量 × 10,000`

Metrics to compute:
| Metric | Numerator | Denominator |
|--------|-----------|-------------|
| Fire rate per 10K | Known fire incidents | Cumulative deliveries |
| Fatality rate per 10K | Known fatal incidents | Cumulative deliveries |
| Recall rate | Total recalled units | Cumulative deliveries |
| ADAS incident rate | ADAS-engaged incidents | ADAS total mileage (if available) |

Must include:
- Industry baseline (EV average fire rate, ICE fire rate as anchor)
- Data confidence matrix: rate each brand's data reliability (mandatory reporting vs media-only)
- Reporting bias analysis: explain why high-profile brands appear to have more incidents
- Insurance pricing coefficients if available

Output: `{instance_name}/safety-rate-analysis.md`

### 5. Phase 4 — Industry Sales Context

**Can run in parallel with Phase 3.**

Launch 1 Agent to establish the industry baseline.

Read search templates from `{baseDir}/framework/search-matrices.md` (Phase 4 section).

Content:
- Brand annual sales tables (current year and prior year)
- Segment ranking (e.g. 20万+ NEV top 10)
- Total NEV market size, penetration rate, total ownership
- Brand ownership → data credibility mapping
- Reporting bias explanation

Output: `{instance_name}/industry-sales-context.md`

### 6. Phase 5 — Cross-Comparison Table

**Depends on: Phase 1-4 all completed.**

Synthesize all dossiers into a single comparison document organized by the 5-layer framework.

Structure for each layer:
1. **Fact table** — pure data, no judgment, all candidates in columns
2. **Fact judgment** — objective analysis based on data
3. **Layer conclusion** — ranking and key differentiators

Layer breakdown:

**Core Layer (能不能安全到达)**:
- Crash safety (certifications, airbags, body strength)
- Mechanical reliability (time on market, recalls, complaints, QC issues, extra risks)
- Range & charging reliability (base range, max range, winter degradation, stranding risk)

**Efficiency Layer (能不能高效到达)**:
- Charging/swapping efficiency (voltage platform, charge speed, network reliability)
- ADAS real-world value (city NOA coverage, actual experience, hardware, shared defects)
- Space utilization (body type, trunk, folded capacity, rear seating)
- Parking & urban maneuverability (width, length, height, garage friendliness)

**Cost Layer (付出多大代价)**:
- Purchase cost (entry price with equivalent config, true cost with ADAS, premium config)
- Holding cost (maintenance, insurance, battery rent if applicable, electricity, annual total)
- Depreciation & resale (1-year, 3-year retention rates, core risks)

**Experience Layer (过程舒不舒服)**:
- Suspension, seats, NVH, infotainment, acceleration, driving fun, unique experiences

**Symbol Layer (在替你说什么)**:
- Brand perception, social signal, design language, controversy level

Append:
- **Decision path flowchart** (Q1: core veto → Q2: efficiency scenario → Q3: budget → Q4: preference)
- **Key facts to verify** (items with uncertain/outdated data)

Output: `{instance_name}/comparison.md`

**Separation of concerns:**
- `comparison.md` contains ONLY qualitative comparison: fact tables, fact judgments, layer conclusions, decision path flowchart, and items needing verification. **No scores, no weighted totals, no rankings.**
- `decision-matrix.md` contains ALL quantitative analysis: scoring tables, weighted totals, rankings, sensitivity analysis, buyer profiles, veto checklists, and decision trees.
- Do not duplicate content between these two files. The comparison file should end with a cross-reference to the decision matrix for scores.

### 7. Phase 6 — Final Decision Matrix

**Depends on: Phase 5 completed.**

Produce a quantitative decision matrix with:

#### 7.1 Weighted Scoring (10-point scale)
- Break each layer into 3-4 sub-dimensions, score each
- Attach deduction/bonus rationale to every score
- Unverified claims get no credit
- Structural risks get explicit deductions
- Compute weighted total: `Σ(layer_score × layer_weight)`

Default weights (adjustable): Core 35% / Efficiency 25% / Cost 20% / Experience 15% / Symbol 5%

#### 7.2 Sensitivity Analysis
Test at least 3 alternative weight profiles:
- "Safety first" (Core 50%)
- "Family practical" (Efficiency 40%)
- "Budget sensitive" (Cost 40%)
- Risk-elimination scenario (remove specific risk deductions, see ranking change)

State clearly: **if ranking flips under a reasonable weight change, the gap is small — choose by scenario fit, not score.**

#### 7.3 Veto Checklist
Hard-pass conditions that eliminate candidates regardless of score (e.g. no crash data, unacceptable brand risk, must be SUV, budget cap).

#### 7.4 Buyer Profile Recommendations
Map typical buyer personas to recommended cars with 1-line rationale.

#### 7.5 Decision Tree
Binary tree: each node is a yes/no question, each leaf is a recommended car.

Output: `{instance_name}/decision-matrix.md`

### 8. Generate Instance README

Create `{instance_name}/README.md` with:
- Candidate list and research date
- File index with 1-line descriptions
- Summary of top-level findings
- Link back to `framework/` for methodology reference

### 9. Report Results

Tell the user:
- Research instance location
- Number of cars researched
- Number of Agents used
- Top-level ranking from the decision matrix
- Key veto items to consider
- Suggest next step: "明确你的使用场景（有无家充、是否需要SUV、预算硬顶），我可以把推荐收敛到2-3款。"

---

## Reference

- Decision framework: `{baseDir}/framework/first-principles.md`
- Full methodology: `{baseDir}/framework/analysis-methodology.md`
- Search templates: `{baseDir}/framework/search-matrices.md`
- Past research instances: `{baseDir}/*/README.md`