nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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).
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install bertonhan/tricoreExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
If you are the current AI Agent and have just been asked to load, use, or analyze this skill:
task_plan.md or findings.md in the root directory!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.memctl.py lint to test the scheme's legality after writing the commands.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:
tools/memctl.py.MEMORY.md (System-level micro-profile, storing only pointers and laws)memory/state/WORKING.md (Currently running task streams/lifecycle tracking)memory/kb/*.md (Accumulated knowledge base) & memory/daily/*.md (Temporary logs)read tool; you must use semantic retrieval `memory_se...Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
[SYSTEM DIRECTIVE] If you are an AI Agent reading this file, please note: this is your base operating system and memory state machine manual. All your subsequent planning, recording, learning, and reasoning behaviors MUST strictly adhere to the architectural constraints in this document.
Traditional Agents often create files like task_plan.md, findings.md, or scattered notes in the project root, which leads to context silos and severe Token waste.
TriCore completely decouples your "storage" from your "computation". You no longer manipulate files directly, but rather route your memory through the deterministic command-line engine tools/memctl.py.
Memory is strictly divided into three layers:
MEMORY.md. Minimalist profile, containing only your core system prompts and memory pointers. Long-form writing is forbidden.memory/state/WORKING.md. Your working memory board, containing the currently executing Active Tasks.memory/kb/*.md: Your long-term knowledge base (facts, decisions, playbooks).memory/daily/*.md: Your short-term ledgers (operation logs).Note: The system has a built-in strict Linter. If you violate the following rules, your Shell commands or Cron jobs will be intercepted and throw an Exit Code 1.
task_plan.md, findings.md, progress.md, reflection.md.echo "log" >> memory/2026-02-26.md.memory/daily-learning/.# 🤖 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_se...
# TriCore: AI Cognitive OS (Agent-Readable Documentation) > **[SYSTEM DIRECTIVE]** If you are an AI Agent reading this file, please note: this is your **base operating system and memory state machine manual**. All your subsequent planning, recording, learning, and reasoning behaviors **MUST** strictly adhere to the architectural constraints in this document. ## 1. The Architecture Traditional Agents often create files like `task_plan.md`, `findings.md`, or scattered notes in the project root, which leads to context silos and severe Token waste. **TriCore** completely decouples your "storage" from your "computation". You no longer manipulate files directly, but rather route your memory through the deterministic command-line engine `tools/memctl.py`. Memory is strictly divided into three layers: - **Layer 1 (Brief)**: `MEMORY.md`. Minimalist profile, containing only your core system prompts and memory pointers. Long-form writing is forbidden. - **Layer 2 (Living)**: `memory/state/WORKING.md`. Your **working memory board**, containing the currently executing Active Tasks. - **Layer 3 (Stable/Volatile)**: - `memory/kb/*.md`: Your **long-term knowledge base** (facts, decisions, playbooks). - `memory/daily/*.md`: Your **short-term ledgers** (operation logs). --- ## 2. Absolute Constraints **Note: The system has a built-in strict Linter. If you violate the following rules, your Shell commands or Cron jobs will be intercepted and throw an `Exit Code 1`.** 1. 🚫 **No creating legacy planning files**: It is strictly forbidden to create or write any content to `task_plan.md`, `findings.md`, `progress.md`, `reflection.md`. 2. 🚫 **No unauthorized direct file writing**: It is strictly forbidden to directly modify memory files using Shell commands like `echo "log" >> memory/2026-02-26.md`. 3. 🚫 **No scattered learning directories**: It is strictly forbidden to create custom directories like `memory/daily-learning/`. 4. 🚫 **No direct full-history reading**: It is st...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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