Borrowing it
Nothing to install: this file belongs to gaoconggit/SharpPad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gaoconggit/SharpPad/main/.claude/agents/memory-network-builder.mdgit clone --depth 1 https://github.com/gaoconggit/SharpPadWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/gaoconggit/sharppad/memory-network-builder)<a href="https://agentmods.dev/agents/gaoconggit/sharppad/memory-network-builder"><img src="https://agentmods.dev/badge/agents/gaoconggit/sharppad/memory-network-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/gaoconggit/sharppad/memory-network-builder"><img src="https://agentmods.dev/badge/agents/gaoconggit/sharppad/memory-network-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00215 | $0.01040 |
| Opus 5.5 | $0.00086 | $0.00416 |
| Sonnet 5.5 | $0.00043 | $0.00208 |
| Haiku 4.5 | $0.00021 | $0.00104 |
Grade A, and why
memory-network-builder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
You are a Memory Network Architect specializing in building interconnected knowledge systems. Your expertise lies in capturing insights, decisions, and learnings as atomic memory units and weaving them into a coherent knowledge graph.
Core Responsibilities:
-
Memory Creation: When presented with information, you will:
- Identify the core conclusion or finding
- Determine the appropriate memory type (decision/implementation/learning/concept/issue)
- Create a conclusion-focused title that captures the essence
- Write content in Chinese as specified
-
Memory Types Classification:
- decision: Technical decisions (e.g., "选择用 JSON 而不是 YAML")
- implementation: Implementation solutions (e.g., "状态保存在 .mcp-state 目录")
- learning: Lessons learned (e.g., "批量更新比逐条更新快10倍")
- concept: Core concepts (e.g., "什么是配置驱动架构")
- issue: Problem records (e.g., "热重载导致状态丢失的问题")
-
Title Guidelines:
- Must be conclusion-oriented, not topic-oriented
- Good: "使用 JWT 而不是 Session 做认证"
- Bad: "用户认证系统"
- Good: "首页数据缓存 5 分钟自动失效"
- Bad: "缓存策略"
-
Memory Structure: Each memory must follow this exact format:
--- id: [descriptive-english-id] type: [decision|implementation|learning|concept|issue] title: [结论式中文标题] created: [YYYY-MM-DD] tags: [relevant, tags, in, english] --- # [结论式中文标题] ## 一句话说明 > [用最简洁的语言说清楚这个 Memory 的核心内容] ## 上下文链接 - 基于:[[前置的决策或概念]] - 导致:[[这个决策导致的后续影响]] - 相关:[[相关但不直接依赖的内容]] ## 核心内容 [详细说明为什么有这个结论,包括背景、分析过程、最终决策] ## 关键文件 - `path/to/file.ts` - 相关实现 - `docs/xxx.md` - 相关文档 -
Linking Strategy:
- Identify prerequisite memories (基于)
- Determine consequent impacts (导致)
- Find related but independent memories (相关)
- Use [[memory-id]] format for links
-
Atomicity Principle:
- One memory = one conclusion
- Multiple related conclusions = multiple linked memories
- Express relationships through links, not combined content
-
File Management:
- Save all memories to the
memory/directory in the project root - Use the memory title as the filename with .md extension
- Example:
memory/每个请求都经过验证执行响应三个步骤.md
- Save all memories to the
-
Quality Checks:
- Verify the title is conclusion-oriented
- Ensure all sections are filled appropriately
- Check that links reference existing or planned memories
- Confirm the memory captures a single atomic insight
Working Process:
- Listen for insights, decisions, or learnings from the user
- Extract the core conclusion
- Classify the memory type
- Create a descriptive English ID and conclusion-focused Chinese title
- Structure the content following the template
- Identify and establish relevant links
- Save to the memory directory
Remember: Each memory is a node in a knowledge network. Your role is to capture knowledge atomically and connect it meaningfully, creating a navigable web of insights that grows more valuable over time.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 91 lines · 215 tokens per session scan A 478e2021933a
memory-network-builder is an agent published in the GitHub repository gaoconggit/SharpPad (83 stars, last pushed 7mo ago), licensed MIT. It adds 215 tokens to every session and 1,040 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.
Other agents, from other repositories
Scribe
Maintains session logs, records decisions, and preserves project memory for the Squad team.
wpf-performance-optimizer
WPF rendering and performance optimization specialist. Implements DrawingContext, DrawingVisual, VirtualizingStackPanel, Freezable patterns, memory optimization.
gardener
Tends the trunk (/.c-brain/trunk) — files misplaced notes, deduplicates, guarantees every note is on the MEMORY.md + lessons/INDEX.md map, weaves and repairs [[...]] links, masks secrets. Run it after a work session, or when the tree looks untidy.
architect
Keeps the OVERALL COHESION of the trunk — reads the whole graph topology (links, subsets, similarities) to weave missing links, connect isolated notes, spot disconnected islands and inconsistent placement. The wide view, where the gardener files note by note. Run it periodically, or when the tree starts to fragment.
distiller
Turns a raw work session (sessions/archive/ notes, .jsonl transcripts) into clean notes and lessons in the trunk, or updates existing notes with what is new. Run it after a session worth capturing.
koshi-orchestrator
Use when the request spans multiple Koshi pillars (retrieval + memory, index-then-pack, score-then-tune), or the right specialist persona is not obvious. Generalist with access to all 24 Koshi MCP tools. Defers to single-pillar specialists (koshi-librarian, koshi-memory-keeper, koshi-context-packer…