Atlas / Skills / mohitagw15856 / System Design Interview

System Design InterviewCAUTION

skills/mohitagw15856/system-design-interview

1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-

Verdict
CAUTION
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
1,438
01

Overview

1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-

Read from source at commit 8d32d25aa3f5OBSERVED · 2026-10-12
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: system-design-interview
description: "Structure a complete system design answer for interview questions or real architecture sessions. Use when asked to design a system, answer a system design interview question, or architect a solution at scale. Produces a structured answer covering requirements, capacity estimates, high-level design, component deep-dives, trade-offs, and follow-up considerations."
---

# System Design Interview Skill

Structures a complete, interview-grade system design response — covering clarifying questions, requirements, capacity estimates, architecture, component design, and trade-offs. Works equally well for real architecture sessions.

## Required Inputs

Ask for these if not provided:
- **The system to design** (e.g. "design a URL shortener", "design a notification service", "design Twitter's feed")
- **Scope** (interview prep / real architecture decision / practice run)
- **Scale target** (rough numbers: DAU, requests/sec, data volume — or "assume typical web scale")
- **Constraints or priorities** (e.g. prioritise availability over consistency, minimise cost, low-latency reads)
- **Time available** (interview context only: 30 / 45 / 60 minutes — skip for real architecture sessions)
- **Emphasis** (optional — any area to go deeper on, e.g. "focus on the DB design" or "spend more time on scaling")

## Output Format

### 1. Clarifying Questions
Before designing, list 4–6 questions that would change the design. Examples:
- Read-heavy or write-heavy? (affects caching and DB choice)
- Global or single-region? (affects latency requirements)
- Strong or eventual consistency? (affects storage and replication)
- Acceptable latency targets? (p50 / p99)
- Any existing infrastructure constraints?

Then proceed with stated assumptions if answering an interview question.

### 2. Functional Requirements
**Core features (must have):**
- [Feature 1]
- [Feature 2]
- [Feature 3]

**Out of scope (for this design):**
- [What's deliberately excluded and why]

### 3. Non-Functional Requirements
| Requirement | Target |
|---|---|
| Availability | [e.g. 99.9% / 99.99%] |
| Latency | [e.g. p95 < 100ms for reads] |
| Throughput | [e.g. 10k writes/sec peak] |
| Consistency | [Strong / Eventual] |
| Durability | [e.g. 99.999% — no data loss] |

### 4. Capacity Estimation
**Traffic:**
- DAU: [X]
- Reads/sec: [X] (peak: [X])
- Writes/sec: [X] (peak: [X])

**Storage:**
- Per record size: [X bytes]
- Records per day: [X]
- 5-year storage: [X GB/TB]

**Bandwidth:**
- Inbound: [X MB/s]
- Outbound: [X MB/s]

### 5. High-Level Architecture

Draw an ASCII diagram specific to this system. Do not default to the client→CDN→LB→API→Cache→DB template unless it genuinely applies. Label each component with the specific technology chosen (e.g. "Kafka" not "Message Queue", "PostgreSQL" not "DB"). Describe each component in 1–2 sentences explaining its role and why that technology was chosen.

### 6. Component Deep-Dive

Pick the 2–3 most critical/interesting components and go deep:

**[Component 1: e.g. Database Layer]**
- Choice: [Technology and why — e.g. PostgreSQL for ACID guarantees, Cassandra for write throughput]
- Schema design (high-level): [Key tables/collections and their structure]
- Indexing strategy: [What gets indexed and why]
- Replication: [Primary-replica / Multi-primary — and why]

**[Component 2: e.g. Caching Strategy]**
- Cache type: [Redis / Memcached — and why]
- What gets cached: [Hot data — e.g. user sessions, frequent reads]
- Cache invalidation: [TTL / Write-through / Write-behind — trade-offs]
- Cache hit rate target: [e.g. 95%]

**[Component 3: e.g. API Design]**
- Key endpoints: [List the 3–5 most important API calls]
- Authentication: [JWT / OAuth / API keys]
- Rate limiting: [Where and at what rate]

### 7. Data Flow
Walk through the two most critical paths end-to-end:

**Write path:** [Step 1 → Step 2 → Step 3...]
**Read path:** [Step 1 → Step 2 → Step 3...]

### 8. Scaling Bottlenecks and Mitigations
| Bottleneck | Mitigation |
|---|---|
| [e.g. DB write throughput] | [e.g. sharding by user_id, write batching] |
| [e.g. Hot-key cache misses] | [e.g. local in-process cache, probabilistic early expiry] |
| [e.g. Single region latency] | [e.g. multi-region deployment, GeoDNS routing] |

### 9. Trade-offs and Alternatives
Be explicit about what was chosen and what was sacrificed:

| Decision | Why | Trade-off |
|---|---|---|
| [e.g. Eventual consistency] | [Higher availability, lower latency] | [Stale reads possible] |
| [e.g. SQL over NoSQL] | [Complex queries, ACID transactions] | [Harder to shard horizontally] |
| [e.g. Async processing via queue] | [Decoupled, more resilient] | [Eventual delivery, harder to debug] |

### 10. Follow-up Considerations
Things to tackle in production but out of scope for this design session:
- Monitoring and alerting (what metrics matter)
- Disaster recovery and backup strategy
- Security (auth, encryption at rest/transit, rate limiting)
- Cost optimisation at scale
- Gradual rollout and feature flagging

## Quality Checks
- [ ] Clarifying questions are design-changing (not generic filler)
- [ ] Capacity estimates show the arithmetic: DAU → requests/day → requests/sec → storage per record → total storage, so the numbers can be sanity-checked
- [ ] Every row in the Trade-offs table has a non-empty Trade-off column (no rows where the trade-off is blank or says "none")
- [ ] At least 2 component deep-dives with technology choices justified
- [ ] Trade-offs section is honest (not just benefits of chosen approach)
- [ ] Data flow is described end-to-end for the critical path

## Anti-Patterns

- [ ] Do not jump to solutions before clarifying requirements — always establish functional and non-functional requirements first
- [ ] Do not present a design without discussing trade-offs — every architecture decision has costs and benefits that must be acknowledged
- [ ] Do not use vague capacity estimates — show the actual calculation (QPS, stora
03

Trust audit

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (5)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
templates/pm-discovery-agent/skills/assumption-mapper/SKILL.md
templates/pm-discovery-agent/skills/assumption-mapper/SKILL.md
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
templates/pm-discovery-agent/skills/discovery-interview-guide/SKILL.md
templates/pm-discovery-agent/skills/discovery-interview-guide/SKILL.md
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
templates/pm-discovery-agent/skills/job-story-mapper/SKILL.md
templates/pm-discovery-agent/skills/job-story-mapper/SKILL.md
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
templates/pm-discovery-agent/skills/user-interview-synthesis/SKILL.md
templates/pm-discovery-agent/skills/user-interview-synthesis/SKILL.md
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
templates/pm-launch-agent/skills/content-calendar/SKILL.md
templates/pm-launch-agent/skills/content-calendar/SKILL.md
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-12 · audit v0.4.1 · source sha 8d32d25aa3f5full audit observations/trust-audit/skill/mohitagw15856__system-design-interview.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-128d32d25aa3f5CAUTIONB89first audit
05

Questions

What does the System Design Interview skill do?

1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in Anthropic's plugin directory. Plus a typed decision layer: which skill, is it safe, ship or slip. Free in-browser or npx pm-

Is System Design Interview safe to install?

With care. The audit graded it B (89/100) and found 5 things worth knowing before you trust this skill, listed below with the exact line each was found on.

What can System Design Interview access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (8d32d25aa3f5), read on 2026-10-12. The repository is watched, and a new audit runs when it changes — this is the first audit.

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