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Install in OpenClaw
/install reflex-learn
Description
Detects repeated queries as implicit negative feedback and non-repetition as positive feedback, enabling continuous learning by writing reflections and patte...
Usage Guidance
This skill appears to do what it claims. Before installing: review install.sh and consider running it inside a Python virtual environment to avoid altering system packages; confirm you have ~80 MB free for the model cache; be aware that reflections and patterns will be written to ~/.openclaw/MEMORY.md, ~/.openclaw/reflexlearn-pending.md (default cautious mode) and optionally ~/.openclaw/SOUL.md if you switch to aggressive mode or manually accept pending entries. If you plan to use Ollama features, verify Ollama runs locally (http://localhost:11434) — the skill contacts only localhost for that integration. If you want tighter control, keep MODE=cautious and review reflexlearn-pending.md regularly before promoting changes to SOUL.md.
Capability Analysis
Type: OpenClaw Skill
Name: reflex-learn
Version: 1.0.2
ReflexLearn is a continuous learning skill that uses query similarity (via sentence-transformers) to infer user satisfaction. The code is well-structured and includes explicit security features, such as path validation in `reflex_learn.py` to prevent file operations outside the `~/.openclaw/` directory. Network activity is transparently handled: `install.sh` performs a one-time download of model weights from Hugging Face, and runtime operations are offline by default (with an optional local-only Ollama integration). No evidence of data exfiltration, malicious execution, or harmful prompt injection was found.
Capability Assessment
Purpose & Capability
Name/description (detect repeated queries and write reflections/patterns) align with required binaries (python3, bash), included Python code, and files that read/write ~/.openclaw/*. No unrelated credentials, external services, or binaries are requested.
Instruction Scope
SKILL.md and the script instruct only to embed queries, compare against ~/.openclaw/reflex_history.json, and write to MEMORY.md / SOUL.md / reflexlearn-pending.md under ~/.openclaw. Optional Ollama calls are to localhost. The skill reads its SKILL.md for config. All referenced files and operations are consistent with the documented behavior.
Install Mechanism
install.sh performs pip installs from PyPI and pre-caches a Hugging Face model (~80 MB). These are declared and require explicit user confirmation. Note: the script uses the system 'pip' (no virtualenv), so consider using a virtual environment if you want to avoid modifying global Python packages.
Credentials
No environment variables or external credentials are requested. Network access is confined to the documented install step and an optional local-only Ollama instance; the runtime supports a strict --offline mode and enforces writes only under ~/.openclaw/.
Persistence & Privilege
Skill is not always-enabled, does not request system-wide privileges, and restricts all filesystem writes to ~/.openclaw/. It does not attempt to modify other skills or global agent settings. Autonomous invocation is allowed by default (normal for skills) but not escalated.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install reflex-learn - After installation, invoke the skill by name or use
/reflex-learn - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
- Added install.sh script for easy, one-step setup (installs dependencies and downloads model weights).
- Enforced strict path validation: all file operations restricted to ~/.openclaw/.
- Introduced model-download guard with explicit warning and new --offline flag for offline operation.
- Removed unused scikit-learn dependency.
- All network access now occurs only in install.sh, with user confirmation and clear listing of operations.
v1.0.1
**v1.1.0 adds robust multi-repeat handling, modifier detection, tiered memory updates, and optional review mode.**
- Requires multiple repeats before flagging an answer as failed, reducing false positives.
- Detects modifier words in repeated queries and extracts user preferences instead of flagging failure.
- Adds cautious (pending file) and aggressive (direct write) modes for updating SOUL.md.
- Stages potential SOUL.md updates in reflexlearn-pending.md for review in cautious mode.
- Introduces new configuration options for repeat threshold, session timing, and operational mode.
- Updates documentation and usage examples to reflect new features and slash commands.
v1.0.0
ReflexLearn v1.0.0
- Introduces implicit feedback-based continuous learning by detecting repeated or non-repeated queries as signals of negative or positive feedback.
- Automatically writes reflections and learned patterns to MEMORY.md and SOUL.md in response to user behavior.
- Operates after agent responses and on regular heartbeat intervals, with triggers for both.
- Configurable learning parameters such as similarity threshold, lookback interactions, and reinforcement delay.
- Embeds queries using local sentence-transformers; optionally supports richer reflection via local Ollama.
- Designed to never call external APIs (except optional local Ollama) and only modifies permitted files in ~/.openclaw/.
Metadata
Frequently Asked Questions
What is Reflexlearn?
Detects repeated queries as implicit negative feedback and non-repetition as positive feedback, enabling continuous learning by writing reflections and patte... It is an AI Agent Skill for Claude Code / OpenClaw, with 161 downloads so far.
How do I install Reflexlearn?
Run "/install reflex-learn" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Reflexlearn free?
Yes, Reflexlearn is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Reflexlearn support?
Reflexlearn is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Reflexlearn?
It is built and maintained by Kinvectum (@kaventures); the current version is v1.0.2.
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