SharpPad: Agent for Claude Code

.claude/agents/memory-network-builder.md

memory-network-builder is an agent for Claude Code from gaoconggit/SharpPad. It costs 215 tokens per session (1,040 once invoked), scanned A, original, MIT.

An agent for storing technical knowledge as connected memory entries. It records decisions, implementation notes, lessons, concepts, and issues in a structured knowledge network.

In plain words
What is it for?
Use it to create memory entries, classify their type, write decision or implementation records, and connect related pieces of project knowledge.
Why use it?
It gives a project a place to preserve why decisions were made and how problems were solved, rather than leaving that information only in chat or scattered documents.

Agent for Claude Code

Written for Claude Code: installed under .claude/.

This is gaoconggit/SharpPad's own configuration. It tells Claude Code how to work on SharpPad itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything SharpPad configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/gaoconggit/SharpPad/main/.claude/agents/memory-network-builder.md
Clone the repo
git clone --depth 1 https://github.com/gaoconggit/SharpPad

Made for: Claude Code.

Wrote 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.

agentmods badge for memory-network-builder

README.md
[![agentmods](https://agentmods.dev/badge/agents/gaoconggit/sharppad/memory-network-builder/github.svg)](https://agentmods.dev/agents/gaoconggit/sharppad/memory-network-builder)
Your own site
<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.

agentmods 80×15 button for memory-network-builder

Your own site · 80×15
<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>
Per session 215 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,040 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 13d ago against content hash 478e2021933a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.claude/agents/memory-network-builder.md · 91 lines

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:

  1. 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
  2. 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")
  3. 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"
  4. 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
    
  5. 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
  6. Atomicity Principle:

    • One memory = one conclusion
    • Multiple related conclusions = multiple linked memories
    • Express relationships through links, not combined content
  7. 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
  8. 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:

  1. Listen for insights, decisions, or learnings from the user
  2. Extract the core conclusion
  3. Classify the memory type
  4. Create a descriptive English ID and conclusion-focused Chinese title
  5. Structure the content following the template
  6. Identify and establish relevant links
  7. 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.

Changes

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.

  1. 13d ago First seen · 91 lines · 215 tokens per session scan A 478e2021933a

Subscribe to this mod's changes

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.

Related

Other agents, from other repositories

Scribe

Maintains session logs, records decisions, and preserves project memory for the Squad team.

sabbour/agentweaver · 20 tokens

wpf-performance-optimizer

WPF rendering and performance optimization specialist. Implements DrawingContext, DrawingVisual, VirtualizingStackPanel, Freezable patterns, memory optimization.

christian289/dotnet-with-claudecode · 0 tokens

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.

Yuno15-bb/c-brain · 66 tokens

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.

Yuno15-bb/c-brain · 67 tokens

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.

Yuno15-bb/c-brain · 45 tokens

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…

jsharma1105/Koshi · 95 tokens