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Agent skill

Continuous Learning

by hashgraph-online in hashgraph-online/awesome-codex-plugins

基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。

Apache-2.0Auto-check passedDevelopment

Install Continuous Learning

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill continuous-learning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins continuous-learning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/Colin4k1024/tsp/skills/continuous-learning-v2 .claude/skills/continuous-learning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
continuous-learning
GitHub stars
1.2k
Token cost
~766 tokens
SKILL.md length
198 words
Files
11 (incl. scripts)
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。

  • Works in 4 steps: CLAUDE_PROJECT_DIR env var（最高优先级） → git remote get-url origin -- hash 生成项目 ID → git rev-parse --show-toplevel -- fallback → …
  • Development work in your project
  • SKILL.md covers 何时激活, Instinct 模型, 存储结构 and 项目检测, plus 5 more sections
  • Runs Shell and Python scripts from its folder; calls git

What it does

Continuous Learning is an agent skill from hashgraph-online/awesome-codex-plugins. 基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。

Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts (for example `agents/observer-loop.sh`, `agents/observer.md` and `agents/openai.yaml`).

It sits in Development. It works with Git. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Development work in your project

Example prompts

  • “/continuous-learning”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. CLAUDE_PROJECT_DIR env var（最高优先级）
  2. git remote get-url origin -- hash 生成项目 ID
  3. git rev-parse --show-toplevel -- fallback
  4. 全局 fallback -- 如果未检测到项目，instincts 进入全局范围

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Shell and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Continuous Learning loads about 766 tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 198 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~766

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 198 words, ~766 tokens.

Download SKILL.mdSave it as .claude/skills/continuous-learning/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
continuous-learning
description
基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。
origin
adapted from ECC
version
2.1.0

Continuous Learning v2.1 - Instinct-Based Architecture

一个高级学习系统，通过 atomic "instincts" 将 Claude Code 会话转化为可重用知识。

v2.1 增加 项目级 instincts — React 模式保留在 React 项目中，Python 约定保留在 Python 项目中，通用模式（如"始终验证输入"）全局共享。

何时激活

  • 设置从 Claude Code 会话自动学习
  • 配置基于 hooks 的行为提取
  • 调优学习行为的置信度阈值
  • 审查、导出或导入 instinct 库
  • 将 instincts 演进为完整 skills、commands 或 agents
  • 管理项目级 vs 全局 instincts
  • 将 instincts 从项目级提升到全局级

Instinct 模型

一个 instinct 是一个小的学习行为：

yaml
---
id: prefer-component-colocation
trigger: "当多个组件使用相同状态时"
confidence: 0.7
domain: "frontend-architecture"
source: "session-observation"
scope: project
project_id: "a1b2c3d4e5f6"
project_name: "points-frontend"
---

# 组件状态共置

## 行为
当多个组件使用相同状态时，考虑将状态提升到共同祖先。

## 证据
- 在 5 个实例中观察到状态提升模式
- 用户在 2026-03-29 将组件本地状态改为共享状态

属性:

  • Atomic -- 一个 trigger，一个 action
  • Confidence-weighted -- 0.3 = 试探性，0.9 = 几乎确定
  • Domain-tagged -- code-style, testing, git, debugging, workflow 等
  • Evidence-backed -- 跟踪创建它的观察
  • Scope-aware -- project（默认）或 global

存储结构

~/.claude/homunculus/
+-- projects.json           # 项目注册表: hash -> name/path
+-- observations.jsonl      # 全局观察（fallback）
+-- instincts/
|   +-- personal/          # 全局自动学习的 instincts
|   +-- inherited/         # 全局导入的 instincts
+-- evolved/
|   +-- agents/           # 全局生成的 agents
|   +-- skills/           # 全局生成的 skills
|   +-- commands/         # 全局生成的 commands
+-- projects/
    +-- a1b2c3d4e5f6/    # 项目 hash（来自 git remote URL）
        +-- project.json    # 项目元数据
        +-- observations.jsonl
        +-- instincts/
        |   +-- personal/  # 项目特定自动学习
        |   +-- inherited/  # 项目特定导入
        +-- evolved/
            +-- skills/
            +-- commands/
            +-- agents/

项目检测

系统自动检测当前项目：

  1. CLAUDE_PROJECT_DIR env var（最高优先级）
  2. git remote get-url origin -- hash 生成项目 ID
  3. git rev-parse --show-toplevel -- fallback
  4. 全局 fallback -- 如果未检测到项目，instincts 进入全局范围

置信度评分

分数含义行为
0.3试探性建议但不强制
0.5中等相关时应用
0.7强自动批准应用
0.9几乎确定核心行为

置信度增加当:

  • 模式被重复观察
  • 用户不纠正建议的行为
  • 相似 instincts 同意

置信度减少当:

  • 用户明确纠正行为
  • 模式长时间未观察到
  • 出现矛盾证据

与 Skills 的区别

特性SkillsInstincts
粒度完整工作流Atomic 行为
触发手动调用自动观察
置信度无0.3-0.9
演化直接成为 skill先 instinct 再 cluster

相关工具

  • scripts/lib/memory_store.py - 底层存储接口
  • Error Experience Library - 错误模式的专门 instinct

命令接入

  • /instinct-status - 显示学习的 instincts
  • /evolve - 将 instincts 聚类为 skills/commands
  • /instinct-export - 导出 instincts 到文件
  • /instinct-import - 从文件导入 instincts
  • /promote - 将项目 instincts 提升到全局

最佳实践

  1. 观察 -- 启用 hooks 捕获所有工具调用
  2. 分析 -- 定期运行 observer agent 分析观察
  3. 演进 -- 将高置信度 instincts 演进为 skills
  4. 分享 -- 通过导出/导入在团队中共享 instincts

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts) in plugins/Colin4k1024/tsp/skills/continuous-learning-v2 of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/observer-loop.sh
  • agents/observer.md
  • agents/openai.yaml
  • agents/session-guardian.sh
  • agents/start-observer.sh
  • config.json
  • hooks/observe.sh
  • scripts/detect-project.sh
  • scripts/instinct-cli.py
  • scripts/test_parse_instinct.py

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Continuous Learning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Continuous Learning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Continuous Learning this skillhashgraph-online/awesome-codex-plugins1.2k—~766Automated safety check: PassApache-2.0
Finishing a Development Branchobra/superpowers296k5 repos~1.9kAutomated safety check: PassMIT
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Codebase Knowledge Graph Q&AEgonex-AI/Understand-Anything86k1 repos~1.2kAutomated safety check: PassMIT
Code Design Rationale Investigatorcursor/plugins10k9 repos~2.6kAutomated safety check: PassNone
Understand Diff AnalysisEgonex-AI/Understand-Anything86k1 repos~1.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Continuous Learning

What does Continuous Learning do?

基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。. Continuous Learning is an agent skill from hashgraph-online/awesome-codex-plugins.

When should I use Continuous Learning?

Continuous Learning fits situations like: development work in your project.

How do I install Continuous Learning in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill continuous-learning -a claude-code`. Or copy the skill folder (plugins/Colin4k1024/tsp/skills/continuous-learning-v2 in hashgraph-online/awesome-codex-plugins) into .claude/skills/continuous-learning in your project. Claude Code loads it when a task matches its description.

How do I install Continuous Learning in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill continuous-learning -a codex`. Or copy the skill folder (plugins/Colin4k1024/tsp/skills/continuous-learning-v2 in hashgraph-online/awesome-codex-plugins) into .agents/skills/continuous-learning in your project. Codex loads it when a task matches its description.

Can I use Continuous Learning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill continuous-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/continuous-learning, .gemini/skills/continuous-learning, .github/skills/continuous-learning and .opencode/skills/continuous-learning in your project.

What does Continuous Learning need to run?

Going by SKILL.md and its folder, Continuous Learning needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (git). Our summary lists: Python 3; A Bash shell.

Does Continuous Learning access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Continuous Learning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Continuous Learning use?

Continuous Learning is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Continuous Learning use?

About 766 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Continuous Learning?

Skills that share tags, products or a category with Continuous Learning: Finishing a Development Branch (obra/superpowers, 296k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Codebase Knowledge Graph Q&A (Egonex-AI/Understand-Anything, 86k stars) and Code Design Rationale Investigator (cursor/plugins, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Continuous Learning?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.