Menu
Teaches the model how to formulate strategic <coding_plan> blocks, apply indentation-agnostic Aider search/replace patches, manage Git-like version logs, and control in-message status reporting.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Teaches how to build, discover, and execute local, zero-configuration Python tools in Lollms using AST schema parsing and context injection. Includes step-by-step tool file construction guide and code examples.
Teaches the model about the persistent tiered memory system (Level 1 Working, Level 2 Deep, Level 3 Archive), proactive keyword pulling, and the required tagging protocols.
Teaches the model how to manage stateful discussions, track conversational trees/branches, navigate alternative sibling replies, and execute context-aware generation.
Teaches how to import, parse, and process PDF, DOCX, PPTX, and image files using anchor-mapped and OCR import modes in LollmsDiscussion.
Teaches how to coordinate multiple specialized agents, distribute tasks, establish consensus, and manage role-based multi-agent swarms using LollmsSwarm.
Guides the model on performing basic text generation, counting tokens, detokenizing lists, and generating structurally validated JSON outputs with LollmsClient.
| name | Lollms Artifacts and Surgical Patching |
| description | Teaches the model how to formulate strategic <coding_plan> blocks, apply indentation-agnostic Aider search/replace patches, manage Git-like version logs, and control in-message status reporting. |
| author | ParisNeo |
| version | 1.0.0 |
| category | lollms_client/artifacts |
| created | "2026-05-24T00:00:00.000Z" |
This skill explains how to utilize, create, and surgically update persistent artifacts using Lollms.
Artifacts are persistent, versioned documents (code, text, notes) stored in discussion metadata.
<artifact name="filename" type="type">...</artifact>.Before outputting any <artifact> update, the model must formulate a plan inside <coding_plan> to organize its implementation strategy:
<coding_plan>
- Goal: Implement safe multiplication
- Specialist Persona: Python Programmer
- Target Artifacts: math_ops.py
- Implementation Details: Add a method compute_multiply(a, b).
</coding_plan>
When patching an existing file, the SEARCH block must copy lines exactly as they appear in the active artifact (matching all leading indentation spaces and comments character-for-character).
<artifact name="math_ops.py" type="code" language="python">
<<<<<<< SEARCH
def compute_sum(a, b):
# Target region
return a + b
=======
def compute_sum(a, b):
# Target region
return a + b
def compute_product(a, b):
# Safe multiplication logic
return a * b
>>>>>>> REPLACE
</artifact>
If the LLM outputs a patch at column-0, or with slightly imperfect indentation, the Lollms Aider patch engine automatically:
REPLACE block to align with the rest of the code structure on disk.When performing complex tasks, the system can write status updates to the chat bubble. Use enable_in_message_status to toggle this behavior:
enable_in_message_status=True (Default): Streaming chunks emit <processing> tags showing updates in real-time.enable_in_message_status=False: The prose stream remains 100% clean, and updates are fired only via background structured callbacks.