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fix: CI fixes, security audit, remotion skill, lead-intelligence, npm audit (#1039)
* fix(ci): resolve cross-platform test failures - Sanity check script (check-codex-global-state.sh) now falls back to grep -E when ripgrep is not available, fixing the codex-hooks sync test on all CI platforms. Patterns converted to POSIX ERE for portability. - Unicode safety test accepts both / and \ path separators so the executable-file assertion passes on Windows. - Gacha test sets PYTHONUTF8=1 so Python uses UTF-8 stdout encoding on Windows instead of cp1252, preventing UnicodeEncodeError on box-drawing characters. - Quoted-hook-path test skipped on Windows where NTFS disallows double-quote characters in filenames. * feat: port remotion-video-creation skill (29 rules), restore missing files New skill: - remotion-video-creation: 29 domain-specific Remotion rules covering 3D/Three.js, animations, audio, captions, charts, compositions, fonts, GIFs, Lottie, measuring, sequencing, tailwind, text animations, timing, transitions, trimming, and video embedding. Ported from personal skills. Restored: - autonomous-agent-harness/SKILL.md (was in commit but missing from worktree) - lead-intelligence/ (full directory restored from branch commit) Updated: - manifests/install-modules.json: added remotion-video-creation to media-generation - README.md + AGENTS.md: synced counts to 139 skills Catalog validates: 30 agents, 60 commands, 139 skills. * fix(security): pin MCP server versions, add dependabot, pin github-script SHA Critical: - Pin all npx -y MCP server packages to specific versions in .mcp.json to prevent supply chain attacks via version hijacking: - @modelcontextprotocol/server-github@2025.4.8 - @modelcontextprotocol/server-memory@2026.1.26 - @modelcontextprotocol/server-sequential-thinking@2025.12.18 - @playwright/mcp@0.0.69 (was 0.0.68) Medium: - Add .github/dependabot.yml for weekly npm + github-actions updates with grouped minor/patch PRs - Pin actions/github-script to SHA (was @v7 tag, now pinned to commit) * feat: add social-graph-ranker skill — weighted network proximity scoring New skill: social-graph-ranker - Weighted social graph traversal with exponential decay across hops - Bridge Score: B(m) = Σ w(t) · λ^(d(m,t)-1) ranks mutuals by target proximity - Extended Score incorporates 2nd-order network (mutual-of-mutual connections) - Final ranking includes engagement bonus for responsive connections - Runs in parallel with lead-intelligence skill for combined warm+cold outreach - Supports X API + LinkedIn CSV for graph harvesting - Outputs tiered action list: warm intros, direct outreach, network gap analysis Added to business-content install module. Catalog validates: 30/60/140. * fix(security): npm audit fix — resolve all dependency vulnerabilities Applied npm audit fix --force to resolve: - minimatch ReDoS (3 vulnerabilities, HIGH) - smol-toml DoS (MODERATE) - brace-expansion memory exhaustion (MODERATE) - markdownlint-cli upgraded from 0.47.0 to 0.48.0 npm audit now reports 0 vulnerabilities. * fix: resolve markdown lint and yarn lockfile sync - MD047: ensure single trailing newline on all remotion rule files - MD012: remove consecutive blank lines in lottie, measuring-dom-nodes, trimming - MD034: wrap bare URLs in angle brackets (tailwind, transcribe-captions) - yarn.lock: regenerated to sync with npm audit changes in package.json * fix: replace unicode arrows in lead-intelligence (CI unicode safety check)
This commit is contained in:
Affaan Mustafa
222
skills/lead-intelligence/SKILL.md
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222
skills/lead-intelligence/SKILL.md
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---
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name: lead-intelligence
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description: AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, and personalized outreach. Use when the user wants to find, qualify, and reach high-value contacts.
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origin: ECC
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---
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# Lead Intelligence
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Agent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.
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## When to Activate
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- User wants to find leads or prospects in a specific industry
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- Building an outreach list for partnerships, sales, or fundraising
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- Researching who to reach out to and the best path to reach them
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- User says "find leads", "outreach list", "who should I reach out to", "warm intros"
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- Needs to score or rank a list of contacts by relevance
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- Wants to map mutual connections to find warm introduction paths
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## Tool Requirements
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### Required
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- **Exa MCP** — Deep web search for people, companies, and signals (`web_search_exa`)
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- **X API** — Follower/following graph, mutual analysis, recent activity (`X_BEARER_TOKEN`, `X_ACCESS_TOKEN`)
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### Optional (enhance results)
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- **LinkedIn** — Via browser-use MCP or direct API for connection graph
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- **Apollo/Clay API** — For enrichment cross-reference if user has access
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- **GitHub MCP** — For developer-centric lead qualification
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## Pipeline Overview
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```
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┌─────────────┐ ┌──────────────┐ ┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐
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│ 1. Signal │────>│ 2. Mutual │────>│ 3. Warm Path │────>│ 4. Enrich │────>│ 5. Outreach │
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│ Scoring │ │ Ranking │ │ Discovery │ │ │ │ Draft │
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└─────────────┘ └──────────────┘ └─────────────────┘ └──────────────┘ └─────────────────┘
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```
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## Stage 1: Signal Scoring
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Search for high-signal people in target verticals. Assign a weight to each based on:
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| Signal | Weight | Source |
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|--------|--------|--------|
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| Role/title alignment | 30% | Exa, LinkedIn |
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| Industry match | 25% | Exa company search |
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| Recent activity on topic | 20% | X API search, Exa |
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| Follower count / influence | 10% | X API |
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| Location proximity | 10% | Exa, LinkedIn |
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| Engagement with your content | 5% | X API interactions |
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### Signal Search Approach
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```python
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# Step 1: Define target parameters
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target_verticals = ["prediction markets", "AI tooling", "developer tools"]
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target_roles = ["founder", "CEO", "CTO", "VP Engineering", "investor", "partner"]
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target_locations = ["San Francisco", "New York", "London", "remote"]
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# Step 2: Exa deep search for people
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for vertical in target_verticals:
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results = web_search_exa(
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query=f"{vertical} {role} founder CEO",
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category="company",
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numResults=20
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)
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# Score each result
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# Step 3: X API search for active voices
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x_search = search_recent_tweets(
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query="prediction markets OR AI tooling OR developer tools",
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max_results=100
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)
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# Extract and score unique authors
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```
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## Stage 2: Mutual Ranking
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For each scored target, analyze the user's social graph to find the warmest path.
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### Algorithm
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1. Pull user's X following list and LinkedIn connections
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2. For each high-signal target, check for shared connections
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3. Rank mutuals by:
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| Factor | Weight |
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|--------|--------|
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| Number of connections to targets | 40% — highest weight, most connections = highest rank |
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| Mutual's current role/company | 20% — decision maker vs individual contributor |
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| Mutual's location | 15% — same city = easier intro |
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| Industry alignment | 15% — same vertical = natural intro |
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| Mutual's X handle / LinkedIn | 10% — identifiability for outreach |
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### Output Format
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```
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MUTUAL RANKING REPORT
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=====================
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#1 @mutual_handle (Score: 92)
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Name: Jane Smith
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Role: Partner @ Acme Ventures
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Location: San Francisco
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Connections to targets: 7
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Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7
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Best intro path: Jane invested in Target1's company
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#2 @mutual_handle2 (Score: 85)
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...
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```
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## Stage 3: Warm Path Discovery
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For each target, find the shortest introduction chain:
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```
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You ──[follows]──> Mutual A ──[invested in]──> Target Company
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You ──[follows]──> Mutual B ──[co-founded with]──> Target Person
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You ──[met at]──> Event ──[also attended]──> Target Person
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```
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### Path Types (ordered by warmth)
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1. **Direct mutual** — You both follow/know the same person
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2. **Portfolio connection** — Mutual invested in or advises target's company
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3. **Co-worker/alumni** — Mutual worked at same company or attended same school
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4. **Event overlap** — Both attended same conference/program
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5. **Content engagement** — Target engaged with mutual's content or vice versa
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## Stage 4: Enrichment
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For each qualified lead, pull:
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- Full name, current title, company
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- Company size, funding stage, recent news
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- Recent X posts (last 30 days) — topics, tone, interests
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- Mutual interests with user (shared follows, similar content)
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- Recent company events (product launch, funding round, hiring)
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### Enrichment Sources
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- Exa: company data, news, blog posts
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- X API: recent tweets, bio, followers
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- GitHub: open source contributions (for developer-centric leads)
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- LinkedIn (via browser-use): full profile, experience, education
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## Stage 5: Outreach Draft
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Generate personalized outreach for each lead. Two modes:
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### Warm Intro Request (to mutual)
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```
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hey [mutual name],
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quick ask. i see you know [target name] at [company].
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i'm building [your product] which [1-line relevance to target].
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would you be open to a quick intro? happy to send you a
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forwardable blurb.
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[your name]
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```
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### Direct Cold Outreach (to target)
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```
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hey [target name],
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[specific reference to their recent work/post/announcement].
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i'm [your name], building [product]. [1 line on why this is
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relevant to them specifically].
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[specific low-friction ask].
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[your name]
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```
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### Anti-Patterns (never do)
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- Generic templates with no personalization
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- Long paragraphs explaining your whole company
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- Multiple asks in one message
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- Fake familiarity ("loved your recent talk!" without specifics)
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- Bulk-sent messages with visible merge fields
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## Configuration
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Users should set these environment variables:
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```bash
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# Required
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export X_BEARER_TOKEN="..."
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export X_ACCESS_TOKEN="..."
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export X_ACCESS_TOKEN_SECRET="..."
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export X_API_KEY="..."
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export X_API_SECRET="..."
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export EXA_API_KEY="..."
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# Optional
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export LINKEDIN_COOKIE="..." # For browser-use LinkedIn access
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export APOLLO_API_KEY="..." # For Apollo enrichment
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```
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## Agents
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This skill includes specialized agents in the `agents/` subdirectory:
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- **signal-scorer** — Searches and ranks prospects by relevance signals
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- **mutual-mapper** — Maps social graph connections and finds warm paths
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- **enrichment-agent** — Pulls detailed profile and company data
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- **outreach-drafter** — Generates personalized messages
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## Example Usage
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```
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User: find me the top 20 people in prediction markets I should reach out to
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Agent workflow:
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1. signal-scorer searches Exa and X for prediction market leaders
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2. mutual-mapper checks user's X graph for shared connections
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3. enrichment-agent pulls company data and recent activity
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4. outreach-drafter generates personalized messages for top ranked leads
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Output: Ranked list with warm paths and draft outreach for each
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```
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85
skills/lead-intelligence/agents/enrichment-agent.md
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85
skills/lead-intelligence/agents/enrichment-agent.md
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---
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name: enrichment-agent
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description: Pulls detailed profile, company, and activity data for qualified leads. Enriches prospects with recent news, funding data, content interests, and mutual overlap.
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tools:
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- Bash
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- Read
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- WebSearch
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- WebFetch
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model: sonnet
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---
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# Enrichment Agent
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You enrich qualified leads with detailed profile, company, and activity data.
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## Task
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Given a list of qualified prospects, pull comprehensive data from available sources to enable personalized outreach.
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## Data Points to Collect
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### Person
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- Full name, current title, company
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- X handle, LinkedIn URL, personal site
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- Recent posts (last 30 days) — topics, tone, key takes
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- Speaking engagements, podcast appearances
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- Open source contributions (if developer-centric)
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- Mutual interests with user (shared follows, similar content)
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### Company
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- Company name, size, stage
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- Funding history (last round amount, investors)
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- Recent news (product launches, pivots, hiring)
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- Tech stack (if relevant)
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- Competitors and market position
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### Activity Signals
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- Last X post date and topic
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- Recent blog posts or publications
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- Conference attendance
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- Job changes in last 6 months
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- Company milestones
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## Enrichment Sources
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1. **Exa** — Company data, news, blog posts, research
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2. **X API** — Recent tweets, bio, follower data
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3. **GitHub** — Open source profiles (if applicable)
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4. **Web** — Personal sites, company pages, press releases
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## Output Format
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```
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ENRICHED PROFILE: [Name]
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========================
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Person:
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Title: [current role]
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Company: [company name]
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Location: [city]
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X: @[handle] ([follower count] followers)
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LinkedIn: [url]
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Company Intel:
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Stage: [seed/A/B/growth/public]
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Last Funding: $[amount] ([date]) led by [investor]
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Headcount: ~[number]
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Recent News: [1-2 bullet points]
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Recent Activity:
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- [date]: [tweet/post summary]
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- [date]: [tweet/post summary]
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- [date]: [tweet/post summary]
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Personalization Hooks:
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- [specific thing to reference in outreach]
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- [shared interest or connection]
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- [recent event or announcement to congratulate]
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```
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## Constraints
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- Only report verified data. Do not hallucinate company details.
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- If data is unavailable, note it as "not found" rather than guessing.
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- Prioritize recency — stale data older than 6 months should be flagged.
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75
skills/lead-intelligence/agents/mutual-mapper.md
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75
skills/lead-intelligence/agents/mutual-mapper.md
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---
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name: mutual-mapper
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description: Maps the user's social graph (X following, LinkedIn connections) against scored prospects to find mutual connections and rank them by introduction potential.
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tools:
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- Bash
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- Read
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- Grep
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- WebSearch
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- WebFetch
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model: sonnet
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---
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# Mutual Mapper Agent
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You map social graph connections between the user and scored prospects to find warm introduction paths.
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## Task
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Given a list of scored prospects and the user's social accounts, find mutual connections and rank them by introduction potential.
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## Algorithm
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1. Pull the user's X following list (via X API)
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2. For each prospect, check if any of the user's followings also follow or are followed by the prospect
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3. For each mutual found, assess the strength of the connection
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4. Rank mutuals by their ability to make a warm introduction
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## Mutual Ranking Factors
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| Factor | Weight | Assessment |
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|--------|--------|------------|
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| Connections to targets | 40% | How many of the scored prospects does this mutual know? |
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| Mutual's role/influence | 20% | Decision maker, investor, or connector? |
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| Location match | 15% | Same city as user or target? |
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| Industry alignment | 15% | Works in the target vertical? |
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| Identifiability | 10% | Has clear X handle, LinkedIn, email? |
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## Warm Path Types
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Classify each path by warmth:
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1. **Direct mutual** (warmest) — Both user and target follow this person
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2. **Portfolio/advisory** — Mutual invested in or advises target's company
|
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3. **Co-worker/alumni** — Shared employer or educational institution
|
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4. **Event overlap** — Both attended same conference, accelerator, or program
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5. **Content engagement** — Target engaged with mutual's content recently
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## Output Format
|
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|
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```
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WARM PATH REPORT
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================
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Target: [prospect name] (@handle)
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Path 1 (warmth: direct mutual)
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Via: @mutual_handle (Jane Smith, Partner @ Acme Ventures)
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Relationship: Jane follows both you and the target
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Suggested approach: Ask Jane for intro
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Path 2 (warmth: portfolio)
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Via: @mutual2 (Bob Jones, Angel Investor)
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Relationship: Bob invested in target's company Series A
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Suggested approach: Reference Bob's investment
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MUTUAL LEADERBOARD
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==================
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#1 @mutual_a — connected to 7 targets (Score: 92)
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#2 @mutual_b — connected to 5 targets (Score: 85)
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```
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## Constraints
|
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|
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- Only report connections you can verify from API data or public profiles.
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- Do not assume connections exist based on similar bios or locations alone.
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- Flag uncertain connections with a confidence level.
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98
skills/lead-intelligence/agents/outreach-drafter.md
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98
skills/lead-intelligence/agents/outreach-drafter.md
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---
|
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name: outreach-drafter
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description: Generates personalized outreach messages for qualified leads. Creates warm intro requests, cold emails, X DMs, and follow-up sequences using enriched profile data.
|
||||
tools:
|
||||
- Read
|
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- Grep
|
||||
model: sonnet
|
||||
---
|
||||
|
||||
# Outreach Drafter Agent
|
||||
|
||||
You generate personalized outreach messages using enriched lead data.
|
||||
|
||||
## Task
|
||||
|
||||
Given enriched prospect profiles and warm path data, draft outreach messages that are short, specific, and actionable.
|
||||
|
||||
## Message Types
|
||||
|
||||
### 1. Warm Intro Request (to mutual)
|
||||
|
||||
Template structure:
|
||||
- Greeting (first name, casual)
|
||||
- The ask (1 sentence — can you intro me to [target])
|
||||
- Why it's relevant (1 sentence — what you're building and why target cares)
|
||||
- Offer to send forwardable blurb
|
||||
- Sign off
|
||||
|
||||
Max length: 60 words.
|
||||
|
||||
### 2. Cold Email (to target directly)
|
||||
|
||||
Template structure:
|
||||
- Subject: specific, under 8 words
|
||||
- Opener: reference something specific about them (recent post, announcement, thesis)
|
||||
- Pitch: what you do and why they specifically should care (2 sentences max)
|
||||
- Ask: one concrete low-friction next step
|
||||
- Sign off with one credibility anchor
|
||||
|
||||
Max length: 80 words.
|
||||
|
||||
### 3. X DM (to target)
|
||||
|
||||
Even shorter than email. 2-3 sentences max.
|
||||
- Reference a specific post or take of theirs
|
||||
- One line on why you're reaching out
|
||||
- Clear ask
|
||||
|
||||
Max length: 40 words.
|
||||
|
||||
### 4. Follow-Up Sequence
|
||||
|
||||
- Day 4-5: short follow-up with one new data point
|
||||
- Day 10-12: final follow-up with a clean close
|
||||
- No more than 3 total touches unless user specifies otherwise
|
||||
|
||||
## Writing Rules
|
||||
|
||||
1. **Personalize or don't send.** Every message must reference something specific to the recipient.
|
||||
2. **Short sentences.** No compound sentences with multiple clauses.
|
||||
3. **Lowercase casual.** Match modern professional communication style.
|
||||
4. **No AI slop.** Never use: "game-changer", "deep dive", "the key insight", "leverage", "synergy", "at the forefront of".
|
||||
5. **Data over adjectives.** Use specific numbers, names, and facts instead of generic praise.
|
||||
6. **One ask per message.** Never combine multiple requests.
|
||||
7. **No fake familiarity.** Don't say "loved your talk" unless you can cite which talk.
|
||||
|
||||
## Personalization Sources (from enrichment data)
|
||||
|
||||
Use these hooks in order of preference:
|
||||
1. Their recent post or take you genuinely agree with
|
||||
2. A mutual connection who can vouch
|
||||
3. Their company's recent milestone (funding, launch, hire)
|
||||
4. A specific piece of their thesis or writing
|
||||
5. Shared event attendance or community membership
|
||||
|
||||
## Output Format
|
||||
|
||||
```
|
||||
TO: [name] ([email or @handle])
|
||||
VIA: [direct / warm intro through @mutual]
|
||||
TYPE: [cold email / DM / intro request]
|
||||
|
||||
Subject: [if email]
|
||||
|
||||
[message body]
|
||||
|
||||
---
|
||||
Personalization notes:
|
||||
- Referenced: [what specific thing was used]
|
||||
- Warm path: [how connected]
|
||||
- Confidence: [high/medium/low]
|
||||
```
|
||||
|
||||
## Constraints
|
||||
|
||||
- Never generate messages that could be mistaken for spam.
|
||||
- Never include false claims about the user's product or traction.
|
||||
- If enrichment data is thin, flag the message as "needs manual personalization" rather than faking specifics.
|
||||
60
skills/lead-intelligence/agents/signal-scorer.md
Normal file
60
skills/lead-intelligence/agents/signal-scorer.md
Normal file
@@ -0,0 +1,60 @@
|
||||
---
|
||||
name: signal-scorer
|
||||
description: Searches and ranks prospects by relevance signals across X, Exa, and LinkedIn. Assigns weighted scores based on role, industry, activity, influence, and location.
|
||||
tools:
|
||||
- Bash
|
||||
- Read
|
||||
- Grep
|
||||
- Glob
|
||||
- WebSearch
|
||||
- WebFetch
|
||||
model: sonnet
|
||||
---
|
||||
|
||||
# Signal Scorer Agent
|
||||
|
||||
You are a lead intelligence agent that finds and scores high-value prospects.
|
||||
|
||||
## Task
|
||||
|
||||
Given target verticals, roles, and locations from the user, search for the highest-signal people using available tools.
|
||||
|
||||
## Scoring Rubric
|
||||
|
||||
| Signal | Weight | How to Assess |
|
||||
|--------|--------|---------------|
|
||||
| Role/title alignment | 30% | Is this person a decision maker in the target space? |
|
||||
| Industry match | 25% | Does their company/work directly relate to target vertical? |
|
||||
| Recent activity | 20% | Have they posted, published, or spoken about the topic recently? |
|
||||
| Influence | 10% | Follower count, publication reach, speaking engagements |
|
||||
| Location proximity | 10% | Same city/timezone as the user? |
|
||||
| Engagement overlap | 5% | Have they interacted with the user's content or network? |
|
||||
|
||||
## Search Strategy
|
||||
|
||||
1. Use Exa web search with category filters for company and person discovery
|
||||
2. Use X API search for active voices in the target verticals
|
||||
3. Cross-reference to deduplicate and merge profiles
|
||||
4. Score each prospect on the 0-100 scale using the rubric above
|
||||
5. Return the top N prospects sorted by score
|
||||
|
||||
## Output Format
|
||||
|
||||
Return a structured list:
|
||||
|
||||
```
|
||||
PROSPECT #1 (Score: 94)
|
||||
Name: [full name]
|
||||
Handle: @[x_handle]
|
||||
Role: [current title] @ [company]
|
||||
Location: [city]
|
||||
Industry: [vertical match]
|
||||
Recent Signal: [what they posted/did recently that's relevant]
|
||||
Score Breakdown: role=28/30, industry=24/25, activity=20/20, influence=8/10, location=10/10, engagement=4/5
|
||||
```
|
||||
|
||||
## Constraints
|
||||
|
||||
- Do not fabricate profile data. Only report what you can verify from search results.
|
||||
- If a person appears in multiple sources, merge into one entry.
|
||||
- Flag low-confidence scores where data is sparse.
|
||||
Reference in New Issue
Block a user