CCM
/Skills
SkillsMCPMarketplacesDigestToolsAdvertise

This week in Claude

Every Monday: Claude Code, Agent SDK, MCP, and the Anthropic platform moves worth your time.

Skills by Category
Frontend DevelopmentBackend & APIsTesting & QASecurityDevOps & CI/CDGit & Pull RequestsDocumentationCode Review & QualityAI & Agent BuildingSkill Development
MCP Servers by Category
Sales & MarketingWeb & Browser AutomationDatabasesAI & LLM ToolsCloud & InfrastructureCommunication & MessagingDeveloper ToolsDesign & CreativeDocuments & KnowledgeSearch & Web Crawling
Marketplaces by Category
AI Agents & OrchestrationLLM IntegrationDevelopment ToolsFrontend & UIBackend & APIsDatabasesTesting & Code QualityDevOps & CloudSecurity & ComplianceGit & Version Control

Claude Code Marketplaces

Discover Claude Code plugins, extensions, and tools. Automatically updated directory of Anthropic Claude AI marketplaces with development tools, productivity plugins, and integrations.

Resources

  • Browse Skills
  • Browse MCP Servers
  • Browse Marketplaces
  • Plugins Reference

Community

  • About
  • Tools
  • Feedback
  • Privacy Policy
  • Advertise

Built for the Claude Code community with Claude Code by @mertduzgun

Independent project, not affiliated with Anthropic

Competitors Analysis

daymade/claude-code-skills
193 installs1.1k stars
Summary

This is a structured workflow for analyzing competitor code repositories with strict evidence-based rules. It forces you to clone repos locally, read actual files with cat and ls, and cite every technical claim with source file and line numbers. The whole thing is built around preventing speculation: no "推测", no "可能", no assumptions. You get templates, checklists, and specific commands for extracting tech stack info from package.json, Cargo.toml, or pyproject.toml. It's opinionated about Chinese network issues requiring SSH retries and includes scripts for managing multiple competitor repos. Useful when you need defensible competitive analysis that can be audited, not just vibes from skimming GitHub pages.

Install to Claude Code

npx -y skills add daymade/claude-code-skills --skill competitors-analysis --agent claude-code

Installs into .claude/skills of the current project.

CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
AI notepad for back-to-back meetings
AI notepad for back-to-back meetings
Notes, actions and memory. Without a meeting bot. First month 100% off.
Download for free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Email for Agents: Free tier availableEmail for Agents: Free tier available
Email for Agents: Free tier available
Give your AI agent a complete email layer—sending, inbound inboxes, and sandbox testing.
Get 4K emails/month free →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
CodeScene MCP ServerCodeScene MCP Server
CodeScene MCP Server
Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
AI notepad for back-to-back meetings
AI notepad for back-to-back meetings
Notes, actions and memory. Without a meeting bot. First month 100% off.
Download for free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Email for Agents: Free tier availableEmail for Agents: Free tier available
Email for Agents: Free tier available
Give your AI agent a complete email layer—sending, inbound inboxes, and sandbox testing.
Get 4K emails/month free →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
CodeScene MCP ServerCodeScene MCP Server
CodeScene MCP Server
Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Files
SKILL.mdView on GitHub

Competitors Analysis

Build competitor intelligence that can be shared, re-run, and audited later. This skill has two layers:

  1. Repository evidence: clone or update the competitor code under the durable competitors workspace, then cite facts from actual files and commits.
  2. Landscape synthesis: summarize positioning, pricing, strengths, weaknesses, gaps, and opportunities, but only after separating sourced facts from judgment.

This skill intentionally subsumes lightweight "competitor scan" workflows. A scan is useful for the landscape table, but it is not enough for technical conclusions.

Entry Router

If the user's request is missing the product/market or target customer segment, ask for that context before synthesizing positioning or opportunity claims. Known competitors are optional; if absent, use Discover mode.

Use the user's wording to choose the path:

User intentModeWhat to do
"find competitors", "竞品有哪些", broad market queryDiscoverSearch GitHub and web sources, shortlist candidates, clone only relevant repositories
"add competitor "IngestClone the repository, record remote + commit, then produce a first profile
"analyze competitor", "review this repo"ProfileUpdate or clone locally, read code, write a cited technical profile
"compare", "landscape", "opportunities"LandscapeEnsure each competitor has a profile, then synthesize gaps and opportunities
"latest code", "有没有更新"UpdatePull/fetch existing competitors and report changed commits before analysis

Durable Source Layout

Use a durable workspace, not /tmp. The default base is:

COMPETITORS_BASE="${COMPETITORS_BASE:-$HOME/workspace/competitors}"

Directory convention:

$COMPETITORS_BASE/
└── {product-slug}/
    ├── {owner-repo}/
    └── ...

Use owner-repo for GitHub repositories so forks and similarly named projects do not collide. If the user's machine already has a product directory, use it as the source of truth and do not re-clone elsewhere.

Preflight

Before analysis, establish these facts from commands, not memory:

repo="$COMPETITORS_BASE/{product-slug}/{owner-repo}"
test -d "$repo/.git"
git -C "$repo" remote -v
git -C "$repo" fetch --all --prune
git -C "$repo" log -1 --format='%H%x09%cI%x09%s'

If the repository is missing, clone it first. Prefer SSH for GitHub when possible:

mkdir -p "$COMPETITORS_BASE/{product-slug}"
git clone --depth 1 <git-ssh-url> "$COMPETITORS_BASE/{product-slug}/{owner-repo}"

If SSH fails for a public repository, report the failure and retry with the repository's HTTPS URL only when that keeps the work moving.

Discovery Workflow

Use gh search repos for GitHub repository discovery. Search multiple query phrases; do not trust one keyword.

gh search repos "product keywords" \
  --limit 30 \
  --archived=false \
  --json fullName,url,description,stargazersCount,forksCount,openIssuesCount,language,pushedAt,updatedAt,defaultBranch

For each candidate, record:

FieldSource
Repository name and URLgh search repos / gh repo view
DescriptionGitHub API or README line citation after clone
ActivitypushedAt, latest commit, release notes if present
Stars/forks/issuesGitHub API with retrieval date
Why it is relevantuser's product scope + repository evidence

Clone only candidates that are relevant to the user's product or analysis goal. For broad markets, first present a shortlist with evidence and then analyze the strongest set.

Repository Fact Gathering

Read files in this order and capture exact sources:

  1. Project metadata: package.json, pyproject.toml, Cargo.toml, go.mod, or equivalent.
  2. README and docs: positioning, screenshots, installation, pricing links.
  3. Entry points: main, bin, scripts, src/, app/, packages/.
  4. Core implementation: renderer, parser, storage, export, sync, auth, API, or domain-specific modules.
  5. Tests and fixtures: they often reveal supported data structures and edge cases.
  6. Releases/changelog: current direction and recent changes.

Use nl -ba <file> or an editor with line numbers before citing. Every technical claim about implementation needs file:line evidence.

Report Structure

For a single competitor, use references/profile_template.md.

For a landscape summary, use this structure:

# {Product} Competitor Landscape

## Source Register
| Competitor | Local path | Remote | Commit | Retrieved |
|---|---|---|---|---|

## Positioning
| Competitor | User segment | Primary promise | Source |
|---|---|---|---|

## Product And Technical Comparison
| Dimension | Competitor A | Source | Competitor B | Source | Our product | Source |
|---|---|---|---|---|---|---|

## Strengths
| Competitor | Strength | Evidence | Why it matters |
|---|---|---|---|

## Weaknesses And Gaps
| Competitor | Gap | Evidence | Opportunity |
|---|---|---|---|

## Opportunities
| Opportunity | Evidence base | Product implication | Confidence |
|---|---|---|---|

## Risks And Assumptions
| Item | What is known | What still needs verification | Next check |
|---|---|---|---|

Evidence Rules

Required

Claim typeRequired evidence
Dependency/framework/versionConfig file line citation
Feature supportREADME/docs line citation plus code citation when technical
Parser/export/storage behaviorCode line citation
Pricing/cloud-hosted claimOfficial page citation with retrieval date
Popularity/activityGitHub API/page citation with retrieval date
Opportunity judgmentEvidence rows it derives from plus explicit confidence

Forbidden

Do not write unsupported technical claims. Avoid these patterns unless they appear inside an explicit "bad example" block:

PatternWhy
"推测", "可能", "应该", "大概", "似乎"Blurs evidence and judgment
"未公开", "未披露"Pretends to know disclosure status
"architecture, inferred from UI"Technical architecture must come from code
Unsourced numbersCannot be audited later

When evidence is unavailable, write 待验证 and state the exact next check that would verify it.

Output Quality Bar

Before finishing, run the checks in references/analysis_checklist.md:

  • Local repository exists under $COMPETITORS_BASE/{product-slug}/.
  • Remote URL and latest commit are recorded.
  • Each technical claim has a file:line citation.
  • Market facts have a source and retrieval date.
  • Landscape judgments are separated from facts.
  • The final answer names gaps, opportunities, and risks without pretending they are code facts.

Script

Use scripts/update-competitors.sh as the starting point for durable competitor repository management:

COMPETITORS_BASE="$HOME/workspace/competitors" \
PRODUCT_NAME="{product-slug}" \
./scripts/update-competitors.sh status

./scripts/update-competitors.sh discover "claude code viewer"
./scripts/update-competitors.sh clone-url https://github.com/org/repo
./scripts/update-competitors.sh pull

The script is a template. For a long-running product, copy it into that product's own repo or operations directory and fill the persistent competitor list.

Relationship To Product Analysis

product-analysis may invoke this skill for compare mode. Keep this skill focused on competitor discovery, repository evidence, and competitive synthesis. Do not turn it into a general product audit orchestrator.

Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
AI notepad for back-to-back meetings
AI notepad for back-to-back meetings
Notes, actions and memory. Without a meeting bot. First month 100% off.
Download for free →
Keep your Mac awake
Keep your Mac awake
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →
Email for Agents: Free tier availableEmail for Agents: Free tier available
Email for Agents: Free tier available
Give your AI agent a complete email layer—sending, inbound inboxes, and sandbox testing.
Get 4K emails/month free →
Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →
CodeScene MCP ServerCodeScene MCP Server
CodeScene MCP Server
Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
First SeenJun 3, 2026
View on GitHub

Recommended

caveman

juliusbrussee/caveman

Ultra-compressed communication mode cutting token usage ~75% while preserving technical accuracy.
348.3k
88.9k
grill-me

mattpocock/skills

Relentless interviewing skill that stress-tests plans and designs through systematic questioning.
546.6k
168.9k
improve

shadcn/improve

improve
23.2k
8.1k
systematic-debugging

obra/superpowers

Structured debugging methodology that mandates root cause investigation before attempting any fixes.
185.7k
253.9k
karpathy-guidelines

forrestchang/andrej-karpathy-skills

Behavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, and verifiable success criteria.
17.9k
191.7k
find-skills

vercel-labs/skills

Discover and install specialized agent skills from the open ecosystem when users need extended capabilities.
2.5M
26k