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* 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)
76 lines
2.5 KiB
Markdown
76 lines
2.5 KiB
Markdown
---
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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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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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- 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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