Skill: mercury7353/live-evo (version 0.1.0)

--- name: live-evo description: Self-evolving memory system that learns from verifiable tasks. Use when completing tasks where you can verify the outcome (coding, predictions, analysis). Automatically retrieves relevant past experiences and generates task-specific guidelines. user-invocable: true disable-model-invocation: false allowed-tools: Bash(python *), Read, Write, Edit --- # Live-Evo: Online Self-Evolving Memory You are using the Live-Evo memory system that learns from past mistakes through experience accumulation and adaptive evaluation. **IMPORTANT — Script location**: All scripts are in the `scripts/` subdirectory next to this SKILL.md file. When running scripts, use the absolute path to the `scripts/` directory relative to where this file is located. For example, if this SKILL.md is at `/path/to/live-evo/SKILL.md`, the scripts are at `/path/to/live-evo/scripts/`. Experience data is stored persistently at `~/.live-evo/experience_db.jsonl` (independent of skill installation location). ## Core Workflow ### 1. Retrieve & Compile (Before Acting) Run the experience retrieval script to find relevant past experiences: ```bash python <scripts-dir>/retrieve.py --query "YOUR_TASK_DESCRIPTION" ``` If experiences are found, they will be compiled into a task-specific guideline. **Use this guideline to inform your approach.** ### 2. Decide: Verify or Direct Apply **You must judge whether contrastive verification (two attempts) is worthwhile** based on: | Factor | Do Contrastive Eval | Skip, Direct Apply | |---|---|---| | Cost of re-running | Low (e.g. run a test) | High (e.g. long build, API costs, heavy computation) | | Verifiability | Clear ground truth exists (tests, known answer) | No easy way to verify programmatically | | Task complexity | Simple enough to attempt twice | Too complex/large to reasonably duplicate | | Guideline relevance | Retrieved guideline is highly relevant | Guideline is loosely related or no guideline found | **If contrastive eval IS worthwhile** → Go to Step 2A **If contrastive eval is NOT worthwhile** → Go to Step 2B #### Step 2A: Contrastive Evaluation (Two Attempts) Make **two independent attempts**: **Attempt A (Without Memory):** - Solve the task using only your base knowledge - Record your answer/approach **Attempt B (With Guideline):** - Apply the retrieved guideline - Solve the task with this informed approach - Record your answer/approach Then verify and update weights: ```bash python <scripts-dir>/update.py \ --task "TASK_DESCRIPTION" \ --result-a "RESULT_WITHOUT_MEMORY" \ --result-b "RESULT_WITH_GUIDELINE" \ --correct "CORRECT_ANSWER" \ --experience-ids "id1,id2,..." ``` #### Step 2B: Direct Apply with Feedback-Based Learning When contrastive evaluation is not feasible: 1. **Apply the guideline directly** (if one was retrieved) and complete the task 2. **Observe feedback** from any of these sources: - User feedback (corrections, complaints, approval) - Environment signals (test results, error messages, build output) - Outcome observation (did the result work as expected?) 3. **Store experience directly** if feedback reveals a lesson: ```bash python <scripts-dir>/add_experience.py \ --question "THE_TASK_QUESTION" \ --failure-reason "What went wrong (from feedback)" \ --improvement "Key lesson learned" \ --category "coding|analysis|prediction|debugging|other" ``` No contrastive comparison needed — just learn from what happened. ### 3. Add New Experience (On Any Failure) Whenever a task fails or feedback reveals a learnable lesson — regardless of which path you took — store the experience: ```bash python <scripts-dir>/add_experience.py \ --question "THE_TASK_QUESTION" \ --failure-reason "What went wrong" \ --improvement "Key lesson learned" \ --category "coding|analysis|prediction|debugging|other" ``` ### 4. Update Weights (When Possible) If you used a retrieved guideline and can determine whether it helped: ```bash python <scripts-dir>/update.py \ --task "TASK_DESCRIPTION" \ --result-a "WHAT_WOULD_HAVE_HAPPENED" \ --result-b "WHAT_ACTUALLY_HAPPENED" \ --correct "CORRECT_OUTCOME" \ --experience-ids "id1,id2,..." ``` If you cannot determine whether the guideline helped, **skip weight updates** — no update is better than a wrong update. ## When to Use Live-Evo Use this system for: - **Coding tasks**: Bug fixes, implementations where tests can verify - **Analysis tasks**: Where ground truth can be checked - **Predictions**: Forecasting with eventual verification - **Problem solving**: Tasks with objectively correct answers - **Any task with user feedback**: Even without formal verification, user corrections are valuable signals ## Experience Format Each experience contains: - `question`: The original task/question - `failure_reason`: What went wrong in the original attempt - `improvement`: Key lesson or approach that would have helped - `missed_information`: Information sources or considerations that were missed - `weight`: Quality score (0.1-2.0) updated based on usefulness - `category`: Domain category for filtering ## Key Principles 1. **Cost-Aware Verification**: Only do contrastive evaluation when the cost is justified — don't waste tokens/time on expensive double-runs 2. **Feedback is Gold**: User corrections, test failures, and error messages are direct learning signals — always store these 3. **Selective Acquisition**: Only store experiences that contain a genuine, actionable lesson 4. **Weight-based Retrieval**: Good experiences rise, bad ones fade 5. **Task-Specific Guidelines**: Don't apply raw experiences — synthesize them into actionable guidance 6. **When in Doubt, Store**: It's better to store a potentially useful experience than to miss a lesson; low-quality experiences will naturally decay via weight updates ## Manual Commands **View all experiences:** ```bash python <scripts-dir>/list_experiences.py ``` **Search experiences:** ```bash python <scripts-dir>/retrieve.py --query "your search query" --top-k 5 ``` **Get statistics:** ```bash python <scripts-dir>/stats.py ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)