npx claudepluginhub niloykumarbarman/everything-claude-codeThis skill uses the workspace's default tool permissions.
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Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Designs, implements, and audits WCAG 2.2 AA accessible UIs for Web (ARIA/HTML5), iOS (SwiftUI traits), and Android (Compose semantics). Audits code for compliance gaps.
Agent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.
web_search_exa)X_BEARER_TOKEN, plus write-context credentials such as X_CONSUMER_KEY, X_CONSUMER_SECRET, X_ACCESS_TOKEN, X_ACCESS_TOKEN_SECRET)┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐
│ 1. Signal │────>│ 2. Mutual │────>│ 3. Warm Path │────>│ 4. Enrich │────>│ 5. Outreach │
│ Scoring │ │ Ranking │ │ Discovery │ │ │ │ Draft │
└─────────────┘ └──────────────┘ └─────────────────┘ └──────────────┘ └─────────────────┘
Do not draft outbound from generic sales copy.
Run brand-voice first whenever the user's voice matters. Reuse its VOICE PROFILE instead of re-deriving style ad hoc inside this skill.
If live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo/site material available.
Search for high-signal people in target verticals. Assign a weight to each based on:
| Signal | Weight | Source |
|---|---|---|
| Role/title alignment | 30% | Exa, LinkedIn |
| Industry match | 25% | Exa company search |
| Recent activity on topic | 20% | X API search, Exa |
| Follower count / influence | 10% | X API |
| Location proximity | 10% | Exa, LinkedIn |
| Engagement with your content | 5% | X API interactions |
# Step 1: Define target parameters
target_verticals = ["prediction markets", "AI tooling", "developer tools"]
target_roles = ["founder", "CEO", "CTO", "VP Engineering", "investor", "partner"]
target_locations = ["San Francisco", "New York", "London", "remote"]
# Step 2: Exa deep search for people
for vertical in target_verticals:
results = web_search_exa(
query=f"{vertical} {role} founder CEO",
category="company",
numResults=20
)
# Score each result
# Step 3: X API search for active voices
x_search = search_recent_tweets(
query="prediction markets OR AI tooling OR developer tools",
max_results=100
)
# Extract and score unique authors
For each scored target, analyze the user's social graph to find the warmest path.
social-graph-ranker model to score bridge value| Factor | Weight |
|---|---|
| Number of connections to targets | 40% — highest weight, most connections = highest rank |
| Mutual's current role/company | 20% — decision maker vs individual contributor |
| Mutual's location | 15% — same city = easier intro |
| Industry alignment | 15% — same vertical = natural intro |
| Mutual's X handle / LinkedIn | 10% — identifiability for outreach |
Canonical rule:
Use social-graph-ranker when the user wants the graph math itself,
the bridge ranking as a standalone report, or explicit decay-model tuning.
Inside this skill, use the same weighted bridge model:
B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
R(m) = B_ext(m) · (1 + β · engagement(m))
Interpretation:
R(m) and direct bridge paths -> warm intro asksR(m) and one-hop bridge paths -> conditional intro asks
If the user explicitly wants the ranking engine broken out, the math visualized, or the network scored outside the full lead workflow, run `social-graph-ranker` as a standalone pass first and feed the result back into this pipeline.
MUTUAL RANKING REPORT
=====================
#1 @mutual_handle (Score: 92)
Name: Jane Smith
Role: Partner @ Acme Ventures
Location: San Francisco
Connections to targets: 7
Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7
Best intro path: Jane invested in Target1's company
#2 @mutual_handle2 (Score: 85)
...
For each target, find the shortest introduction chain:
You ──[follows]──> Mutual A ──[invested in]──> Target Company
You ──[follows]──> Mutual B ──[co-founded with]──> Target Person
You ──[met at]──> Event ──[also attended]──> Target Person
For each qualified lead, pull:
Generate personalized outreach for each lead. The draft should match the source-derived voice profile and the target channel.
Pick one primary channel in this order:
Use multi-channel only when there is a strong reason and the cadence will not feel spammy.
Goal:
Avoid:
Goal:
Avoid:
For each target, produce:
If browser control is available:
If desktop automation is available:
Do not send messages automatically without explicit user approval.
Users should set these environment variables:
# Required
export X_BEARER_TOKEN="..."
export X_ACCESS_TOKEN="..."
export X_ACCESS_TOKEN_SECRET="..."
export X_CONSUMER_KEY="..."
export X_CONSUMER_SECRET="..."
export EXA_API_KEY="..."
# Optional
export LINKEDIN_COOKIE="..." # For browser-use LinkedIn access
export APOLLO_API_KEY="..." # For Apollo enrichment
This skill includes specialized agents in the agents/ subdirectory:
User: find me the top 20 people in prediction markets I should reach out to
Agent workflow:
1. signal-scorer searches Exa and X for prediction market leaders
2. mutual-mapper checks user's X graph for shared connections
3. enrichment-agent pulls company data and recent activity
4. outreach-drafter generates personalized messages for top ranked leads
Output: Ranked list with warm paths, voice profile summary, and channel-specific outreach drafts or drafts-in-app
brand-voice for canonical voice captureconnections-optimizer for review-first network pruning and expansion before outreach