refactor: extract social graph ranking core

AGENTS.md

@@ -1,6 +1,6 @@
 # Everything Claude Code (ECC) — Agent Instructions
 
-This is a **production-ready AI coding plugin** providing 36 specialized agents, 150 skills, 68 commands, and automated hook workflows for software development.
+This is a **production-ready AI coding plugin** providing 36 specialized agents, 151 skills, 68 commands, and automated hook workflows for software development.
 
 **Version:** 1.9.0
 
@@ -146,7 +146,7 @@ Troubleshoot failures: check test isolation → verify mocks → fix implementat
 
 ```
 agents/          — 36 specialized subagents
-skills/          — 150 workflow skills and domain knowledge
+skills/          — 151 workflow skills and domain knowledge
 commands/        — 68 slash commands
 hooks/           — Trigger-based automations
 rules/           — Always-follow guidelines (common + per-language)

README.md

@@ -225,7 +225,7 @@ For manual install instructions see the README in the `rules/` folder. When copy
 /plugin list everything-claude-code@everything-claude-code
 ```
 
-**That's it!** You now have access to 36 agents, 150 skills, and 68 legacy command shims.
+**That's it!** You now have access to 36 agents, 151 skills, and 68 legacy command shims.
 
 ### Multi-model commands require additional setup
 
@@ -1120,7 +1120,7 @@ The configuration is automatically detected from `.opencode/opencode.json`.
 |---------|-------------|----------|--------|
 | Agents | PASS: 36 agents | PASS: 12 agents | **Claude Code leads** |
 | Commands | PASS: 68 commands | PASS: 31 commands | **Claude Code leads** |
-| Skills | PASS: 150 skills | PASS: 37 skills | **Claude Code leads** |
+| Skills | PASS: 151 skills | PASS: 37 skills | **Claude Code leads** |
 | Hooks | PASS: 8 event types | PASS: 11 events | **OpenCode has more!** |
 | Rules | PASS: 29 rules | PASS: 13 instructions | **Claude Code leads** |
 | MCP Servers | PASS: 14 servers | PASS: Full | **Full parity** |
@@ -1229,7 +1229,7 @@ ECC is the **first plugin to maximize every major AI coding tool**. Here's how e
 |---------|------------|------------|-----------|----------|
 | **Agents** | 36 | Shared (AGENTS.md) | Shared (AGENTS.md) | 12 |
 | **Commands** | 68 | Shared | Instruction-based | 31 |
-| **Skills** | 150 | Shared | 10 (native format) | 37 |
+| **Skills** | 151 | Shared | 10 (native format) | 37 |
 | **Hook Events** | 8 types | 15 types | None yet | 11 types |
 | **Hook Scripts** | 20+ scripts | 16 scripts (DRY adapter) | N/A | Plugin hooks |
 | **Rules** | 34 (common + lang) | 34 (YAML frontmatter) | Instruction-based | 13 instructions |

README.zh-CN.md

@@ -106,7 +106,7 @@ cp -r everything-claude-code/rules/perl ~/.claude/rules/
 /plugin list everything-claude-code@everything-claude-code
 ```
 
-**完成！** 你现在可以使用 36 个代理、150 个技能和 68 个命令。
+**完成！** 你现在可以使用 36 个代理、151 个技能和 68 个命令。
 
 ### multi-* 命令需要额外配置
 

WORKING-CONTEXT.md

@@ -110,3 +110,4 @@ Keep this file detailed for only the current sprint, blockers, and next actions.
 - 2026-04-01: Added `brand-voice` as the canonical source-derived writing-style system and wired the content lane to treat it as the shared voice source of truth instead of duplicating partial style heuristics across skills.
 - 2026-04-01: Added `connections-optimizer` as the review-first social-graph reorganization workflow for X and LinkedIn, with explicit pruning modes, browser fallback expectations, and Apple Mail drafting guidance.
 - 2026-04-01: Added `manim-video` as the reusable technical explainer lane and seeded it with a starter network-graph scene so launch and systems animations do not depend on one-off scratch scripts.
+- 2026-04-02: Re-extracted `social-graph-ranker` as a standalone primitive because the weighted bridge-decay model is reusable outside the full lead workflow. `lead-intelligence` now points to it for canonical graph ranking instead of carrying the full algorithm explanation inline, while `connections-optimizer` stays the broader operator layer for pruning, adds, and outbound review packs.

docs/zh-CN/AGENTS.md

@@ -1,6 +1,6 @@
 # Everything Claude Code (ECC) — 智能体指令
 
-这是一个**生产就绪的 AI 编码插件**，提供 36 个专业代理、150 项技能、68 条命令以及自动化钩子工作流，用于软件开发。
+这是一个**生产就绪的 AI 编码插件**，提供 36 个专业代理、151 项技能、68 条命令以及自动化钩子工作流，用于软件开发。
 
 **版本:** 1.9.0
 
@@ -147,7 +147,7 @@
 
 ```
 agents/          — 36 个专业子代理
-skills/          — 150 个工作流技能和领域知识
+skills/          — 151 个工作流技能和领域知识
 commands/        — 68 个斜杠命令
 hooks/           — 基于触发的自动化
 rules/           — 始终遵循的指导方针（通用 + 每种语言）

docs/zh-CN/README.md

@@ -209,7 +209,7 @@ npx ecc-install typescript
 /plugin list everything-claude-code@everything-claude-code
 ```
 
-**搞定！** 你现在可以使用 36 个智能体、150 项技能和 68 个命令了。
+**搞定！** 你现在可以使用 36 个智能体、151 项技能和 68 个命令了。
 
 ***
 
@@ -1096,7 +1096,7 @@ opencode
 |---------|-------------|----------|--------|
 | 智能体 | PASS: 36 个 | PASS: 12 个 | **Claude Code 领先** |
 | 命令 | PASS: 68 个 | PASS: 31 个 | **Claude Code 领先** |
-| 技能 | PASS: 150 项 | PASS: 37 项 | **Claude Code 领先** |
+| 技能 | PASS: 151 项 | PASS: 37 项 | **Claude Code 领先** |
 | 钩子 | PASS: 8 种事件类型 | PASS: 11 种事件 | **OpenCode 更多！** |
 | 规则 | PASS: 29 条 | PASS: 13 条指令 | **Claude Code 领先** |
 | MCP 服务器 | PASS: 14 个 | PASS: 完整 | **完全对等** |
@@ -1208,7 +1208,7 @@ ECC 是**第一个最大化利用每个主要 AI 编码工具的插件**。以
 |---------|------------|------------|-----------|----------|
 | **智能体** | 36 | 共享 (AGENTS.md) | 共享 (AGENTS.md) | 12 |
 | **命令** | 68 | 共享 | 基于指令 | 31 |
-| **技能** | 150 | 共享 | 10 (原生格式) | 37 |
+| **技能** | 151 | 共享 | 10 (原生格式) | 37 |
 | **钩子事件** | 8 种类型 | 15 种类型 | 暂无 | 11 种类型 |
 | **钩子脚本** | 20+ 个脚本 | 16 个脚本 (DRY 适配器) | N/A | 插件钩子 |
 | **规则** | 34 (通用 + 语言) | 34 (YAML 前页) | 基于指令 | 13 条指令 |

manifests/install-modules.json

@@ -287,6 +287,7 @@
         "skills/investor-materials",
         "skills/investor-outreach",
         "skills/lead-intelligence",
+        "skills/social-graph-ranker",
         "skills/market-research"
       ],
       "targets": [

skills/connections-optimizer/SKILL.md

@@ -42,6 +42,7 @@ If the user does not specify a mode, use `default`.
 
 - `x-api` for X graph inspection and recent activity
 - `lead-intelligence` for target discovery and warm-path ranking
+- `social-graph-ranker` when the user wants bridge value scored independently of the broader lead workflow
 - Exa / deep research for person and company enrichment
 - `brand-voice` before drafting outbound
 
@@ -182,6 +183,7 @@ Drafts
 ## Related Skills
 
 - `brand-voice` for the reusable voice profile
+- `social-graph-ranker` for the standalone bridge-scoring and warm-path math
 - `lead-intelligence` for weighted target and warm-path discovery
 - `x-api` for X graph access, drafting, and optional apply flows
 - `content-engine` when the user also wants public launch content around network moves

skills/lead-intelligence/SKILL.md

@@ -89,11 +89,12 @@ x_search = search_recent_tweets(
 
 For each scored target, analyze the user's social graph to find the warmest path.
 
-### Algorithm
+### Ranking Model
 
 1. Pull user's X following list and LinkedIn connections
 2. For each high-signal target, check for shared connections
-3. Rank mutuals by:
+3. Apply the `social-graph-ranker` model to score bridge value
+4. Rank mutuals by:
 
 | Factor | Weight |
 |--------|--------|
@@ -103,47 +104,20 @@ For each scored target, analyze the user's social graph to find the warmest path
 | Industry alignment | 15% — same vertical = natural intro |
 | Mutual's X handle / LinkedIn | 10% — identifiability for outreach |
 
-### Weighted Bridge Ranking
+Canonical rule:
 
-Treat this as the canonical network-ranking stage for lead intelligence. Do not run a separate graph skill when this stage is enough.
+```text
+Use social-graph-ranker when the user wants the graph math itself,
+the bridge ranking as a standalone report, or explicit decay-model tuning.
+```
 
-Given:
-- `T` = target leads
-- `M` = your mutuals / existing connections
-- `d(m, t)` = shortest hop distance from mutual `m` to target `t`
-- `w(t)` = target weight from signal scoring
-
-Compute the base bridge score for each mutual:
+Inside this skill, use the same weighted bridge model:
 
 ```text
 B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
-```
-
-Where:
-- `λ` is the decay factor, usually `0.5`
-- a direct connection contributes full value
-- each extra hop halves the contribution
-
-For second-order reach, expand one level into the mutual's own network:
-
-```text
-B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))
-```
-
-Where:
-- `N(m) \\ M` is the set of people the mutual knows that you do not
-- `α` is the second-order discount, usually `0.3`
-
-Then rank by response-adjusted bridge value:
-
-```text
 R(m) = B_ext(m) · (1 + β · engagement(m))
 ```
 
-Where:
-- `engagement(m)` is a normalized responsiveness score
-- `β` is the engagement bonus, usually `0.2`
-
 Interpretation:
 - Tier 1: high `R(m)` and direct bridge paths -> warm intro asks
 - Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks
@@ -152,6 +126,8 @@ Interpretation:
 ### Output Format
 
 ```
+
+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
 =====================
 

skills/social-graph-ranker/SKILL.md

@@ -0,0 +1,154 @@
+---
+name: social-graph-ranker
+description: Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
+origin: ECC
+---
+
+# Social Graph Ranker
+
+Canonical weighted graph-ranking layer for network-aware outreach.
+
+Use this when the user needs to:
+
+- rank existing mutuals or connections by intro value
+- map warm paths to a target list
+- measure bridge value across first- and second-order connections
+- decide which targets deserve warm intros versus direct cold outreach
+- understand the graph math independently from `lead-intelligence` or `connections-optimizer`
+
+## When To Use This Standalone
+
+Choose this skill when the user primarily wants the ranking engine:
+
+- "who in my network is best positioned to introduce me?"
+- "rank my mutuals by who can get me to these people"
+- "map my graph against this ICP"
+- "show me the bridge math"
+
+Do not use this by itself when the user really wants:
+
+- full lead generation and outbound sequencing -> use `lead-intelligence`
+- pruning, rebalancing, and growing the network -> use `connections-optimizer`
+
+## Inputs
+
+Collect or infer:
+
+- target people, companies, or ICP definition
+- the user's current graph on X, LinkedIn, or both
+- weighting priorities such as role, industry, geography, and responsiveness
+- traversal depth and decay tolerance
+
+## Core Model
+
+Given:
+
+- `T` = weighted target set
+- `M` = your current mutuals / direct connections
+- `d(m, t)` = shortest hop distance from mutual `m` to target `t`
+- `w(t)` = target weight from signal scoring
+
+Base bridge score:
+
+```text
+B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
+```
+
+Where:
+
+- `λ` is the decay factor, usually `0.5`
+- a direct path contributes full value
+- each extra hop halves the contribution
+
+Second-order expansion:
+
+```text
+B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))
+```
+
+Where:
+
+- `N(m) \\ M` is the set of people the mutual knows that you do not
+- `α` discounts second-order reach, usually `0.3`
+
+Response-adjusted final ranking:
+
+```text
+R(m) = B_ext(m) · (1 + β · engagement(m))
+```
+
+Where:
+
+- `engagement(m)` is normalized responsiveness or relationship strength
+- `β` is the engagement bonus, usually `0.2`
+
+Interpretation:
+
+- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks
+- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks
+- Tier 3: low `R(m)` or no viable bridge -> direct outreach or follow-gap fill
+
+## Scoring Signals
+
+Weight targets before graph traversal with whatever matters for the current priority set:
+
+- role or title alignment
+- company or industry fit
+- current activity and recency
+- geographic relevance
+- influence or reach
+- likelihood of response
+
+Weight mutuals after traversal with:
+
+- number of weighted paths into the target set
+- directness of those paths
+- responsiveness or prior interaction history
+- contextual fit for making the intro
+
+## Workflow
+
+1. Build the weighted target set.
+2. Pull the user's graph from X, LinkedIn, or both.
+3. Compute direct bridge scores.
+4. Expand second-order candidates for the highest-value mutuals.
+5. Rank by `R(m)`.
+6. Return:
+   - best warm intro asks
+   - conditional bridge paths
+   - graph gaps where no warm path exists
+
+## Output Shape
+
+```text
+SOCIAL GRAPH RANKING
+====================
+
+Priority Set:
+Platforms:
+Decay Model:
+
+Top Bridges
+- mutual / connection
+  base_score:
+  extended_score:
+  best_targets:
+  path_summary:
+  recommended_action:
+
+Conditional Paths
+- mutual / connection
+  reason:
+  extra hop cost:
+
+No Warm Path
+- target
+  recommendation: direct outreach / fill graph gap
+```
+
+## Related Skills
+
+- `lead-intelligence` uses this ranking model inside the broader target-discovery and outreach pipeline
+- `connections-optimizer` uses the same bridge logic when deciding who to keep, prune, or add
+- `brand-voice` should run before drafting any intro request or direct outreach
+- `x-api` provides X graph access and optional execution paths