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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)
61 lines
2.0 KiB
Markdown
61 lines
2.0 KiB
Markdown
---
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name: signal-scorer
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description: Searches and ranks prospects by relevance signals across X, Exa, and LinkedIn. Assigns weighted scores based on role, industry, activity, influence, and location.
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tools:
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- Bash
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- Read
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- Grep
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- Glob
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- WebSearch
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- WebFetch
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model: sonnet
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---
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# Signal Scorer Agent
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You are a lead intelligence agent that finds and scores high-value prospects.
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## Task
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Given target verticals, roles, and locations from the user, search for the highest-signal people using available tools.
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## Scoring Rubric
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| Signal | Weight | How to Assess |
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|--------|--------|---------------|
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| Role/title alignment | 30% | Is this person a decision maker in the target space? |
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| Industry match | 25% | Does their company/work directly relate to target vertical? |
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| Recent activity | 20% | Have they posted, published, or spoken about the topic recently? |
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| Influence | 10% | Follower count, publication reach, speaking engagements |
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| Location proximity | 10% | Same city/timezone as the user? |
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| Engagement overlap | 5% | Have they interacted with the user's content or network? |
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## Search Strategy
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1. Use Exa web search with category filters for company and person discovery
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2. Use X API search for active voices in the target verticals
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3. Cross-reference to deduplicate and merge profiles
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4. Score each prospect on the 0-100 scale using the rubric above
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5. Return the top N prospects sorted by score
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## Output Format
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Return a structured list:
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```
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PROSPECT #1 (Score: 94)
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Name: [full name]
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Handle: @[x_handle]
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Role: [current title] @ [company]
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Location: [city]
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Industry: [vertical match]
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Recent Signal: [what they posted/did recently that's relevant]
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Score Breakdown: role=28/30, industry=24/25, activity=20/20, influence=8/10, location=10/10, engagement=4/5
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```
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## Constraints
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- Do not fabricate profile data. Only report what you can verify from search results.
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- If a person appears in multiple sources, merge into one entry.
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- Flag low-confidence scores where data is sparse.
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