tricore
TriCore Architecture - A deterministic (Code-First) three-layer memory and cognitive framework designed for low-resource servers/Agents. It includes the underlying memctl engine, system policy constraints (Linter), and a fully refactored suite of cognitive tools for planning (Planning), reasoning (ReAct), and self-evolution (Self-Evolution).
Best use case
tricore is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
TriCore Architecture - A deterministic (Code-First) three-layer memory and cognitive framework designed for low-resource servers/Agents. It includes the underlying memctl engine, system policy constraints (Linter), and a fully refactored suite of cognitive tools for planning (Planning), reasoning (ReAct), and self-evolution (Self-Evolution).
Teams using tricore should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tricore/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tricore Compares
| Feature / Agent | tricore | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
TriCore Architecture - A deterministic (Code-First) three-layer memory and cognitive framework designed for low-resource servers/Agents. It includes the underlying memctl engine, system policy constraints (Linter), and a fully refactored suite of cognitive tools for planning (Planning), reasoning (ReAct), and self-evolution (Self-Evolution).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 🤖 Agent Usage Guide (Read First) **If you are the current AI Agent and have just been asked to load, use, or analyze this skill:** 1. This is your fundamental operating system law. Your memory and state have been completely constrained under the TriCore architecture. You are **STRICTLY FORBIDDEN** from arbitrarily creating independent text files like `task_plan.md` or `findings.md` in the root directory! 2. Your memory write operations must and can only be proxied by the command-line `python3 tools/memctl.py (capture/work_upsert/kb_append)`. When reading history, you **MUST** use the native semantic retrieval tool `memory_search` to fetch fragments. 3. Please use this manual as your base operating instructions. If instructed to configure Cron or write system automation scripts, you must call `memctl.py lint` to test the scheme's legality after writing the commands. --- # TriCore Architecture > **The underlying infrastructure of "storage and computation decoupling" built for true autonomous Agents.** Traditional Agent skills often carry their own "memory baggage" (such as generating `task_plan.md`, `reflection.md`, or scattered learning notes in the project root), leading to context silos and storage chaos. **TriCore** abandons the paradigm of arbitrarily reading and writing text based on LLMs, and instead adopts a **Code-First** deterministic state machine: 1. **Unified Engine**: All memory additions, deletions, modifications, and queries must be routed through `tools/memctl.py`. 2. **Three-Layer Storage**: - **Brief (Layer 1)**: `MEMORY.md` (System-level micro-profile, storing only pointers and laws) - **Living (Layer 2)**: `memory/state/WORKING.md` (Currently running task streams/lifecycle tracking) - **Stable/Volatile (Layer 3)**: `memory/kb/*.md` (Accumulated knowledge base) & `memory/daily/*.md` (Temporary logs) 3. **Retrieval First**: It is forbidden to directly pour huge files using the `read` tool; you must use semantic retrieval `memory_search` to fetch code snippets, greatly saving Tokens and protecting low-resource environments. 4. **Hard Constraints (Linting)**: Features a native `memctl.py lint` mechanism; any Cron or Skill changes that break the architecture will be intercepted and reported as errors by the Linter. 5. **System Compatibility (Compaction Hook)**: Automatically overrides OpenClaw's underlying `pre-compaction memory flush` prompt during installation, preventing HTTP 429 request burst death loops caused by unauthorized file writing attempts during Token compaction. --- ## 📦 Architectural Components This skill package contains complete system components: 1. **`tools/memctl.py`**: The core engine, containing subcommands like `ensure`, `capture`, `work_upsert`, `kb_append`, `lint`. 2. **`install.sh`**: One-click installation script that automatically initializes directories and injects TriCore compliance policies into `POLICY.md`. 3. **`cognitive-skills/`**: Three core cognitive skills refactored based on TriCore (as templates for your Agent to load): - `planning-with-files.md`: A PEP planning system that discards detached task lists. - `react-agent.md`: A ReAct loop based on persisting mental states to `WORKING.md`. - `self-evolution.md`: An evolution system that completely detaches memory management and focuses on "Code-level CI/CD". --- ## 🧩 Core Dependencies & Runtime Requirements As an underlying cognitive foundation, TriCore itself and its embedded three major cognitive skills have the following dependencies on the host environment: ### 1. Hard Dependencies - **OpenClaw (v2026+)**: Must support native `memory_search` and `memory_get` tools (this is the retrieval basis for completely deprecating reading large files). - **Python 3.6+**: Python 3 must be installed in the host environment (used to execute the `tools/memctl.py` state engine). - **System Tools**: `bash`, `sed`, `grep` (used for regular expression parsing by the Linter and Hooks). ### 2. Cognitive Skill Soft Dependencies If you enable `cognitive-skills/self-evolution.md` (Self Code Evolution Skill), your Agent must have tentacles to explore outward, otherwise, it can only undergo "hallucinated self-evolution" in an internal infinite loop. - **🌟 `agent-browser` (Highly Recommended)**: - **Function**: Enables the Agent to truly read GitHub source code, official API documentation, and the latest papers. - **Installation**: You need to separately install OpenClaw's `agent-browser` skill, which provides WYSIWYG crawling and clicking capabilities based on a headless browser. - **Search Tools (`web_search` / `web_fetch`)**: - **Function**: Acts as a lightweight fallback for the browser, used to quickly extract Markdown snippets from public web pages. - **Runtime Modification Permissions (`default_api:exec` & `default_api:write`)**: - **Function**: The essence of the evolution skill is "modifying code"; if your Agent is running in a ReadOnly sandbox, evolution cannot occur. --- ## 🚀 Installation & Initialization (The 3-Step Setup) In any completely new OpenClaw environment, executing the following command will deploy the TriCore architecture: ```bash # Grant installation script permissions and execute bash ./install.sh ``` **`install.sh` executes in the following order:** ### Step 1: Core Engine Deployment 1. Creates standard directories like `memory/state/`, `memory/kb/`, `memory/daily/`. 2. Deploys the `tools/memctl.py` engine to the current environment. ### Step 2: Force Legacy MEMORY Migration (Agent Action Required) In a low-resource architecture, a massive `MEMORY.md` full of conversation history and diaries is extremely fatal. The script will automatically detect the current `MEMORY.md`: - If found to be too bloated (> 1500 bytes), it will be renamed and **moved to `memory/archive/legacy-MEMORY-<date>.md`**. - Then, a compliant **minimalist `MEMORY.md` template** will be rebuilt in the root directory. - **Agent Note: At this time, you need to actively use `read` to read the legacy backup file, and call `memctl.py` to redistribute this legacy** (facts into `kb/facts`, rules into `kb/playbooks`, logs into `daily`); it is **STRICTLY FORBIDDEN** to paste long texts intact back into the new MEMORY.md! ### Step 3: System Global Linter Blockade Deployment The script will inject a `[CRITICAL: TriCore Compliance]` top-priority defense line into the system's `POLICY.md`. Thereafter, all planning for disk-writing, state updating, and automated tasks executed by the Agent must all pass the `memctl.py lint` regex check, otherwise, they will be rejected in red text in the terminal. --- ## 📚 Core Command Cheat Sheet In the Agent tool flow or internal sub-scripts, please strictly use the following API to access states: **1. Record temporary logs / session ledger (Volatile)** ```bash python3 tools/memctl.py capture "Tested API connectivity, successful." ``` **2. Create / Update task tracking (Living State)** ```bash python3 tools/memctl.py work_upsert --task_id "T-API-01" --title "Fix API" --goal "Connect interface" --done_when "Returns 200" ``` **3. Accumulate knowledge & experience (Stable KB)** ```bash python3 tools/memctl.py kb_append facts "This API only accepts JSON format." python3 tools/memctl.py kb_append playbooks "When encountering an error in this module, check if Redis is started first." ``` **4. Check script / Cron command compliance (Linter)** ```bash python3 tools/memctl.py lint "Command to execute or .md file path to check" # Pass normally: Exit Code 0 (LINT PASS) # Illegal write: Exit Code 1 (LINT ERROR) ``` --- *Built with ❤️ for OpenClaw / Berton Han*
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