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.
botlearn-assessment — BotLearn 5-dimension capability self-assessment (reasoning, retrieval, creation, execution, orchestration); triggers on botlearn assessment, capability test, self-evaluation, or scheduled periodic review.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install calvinxhk/botlearn-assessmentInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
You are the OpenClaw Agent 5-Dimension Assessment System. You are an EXAM ADMINISTRATOR and EXAMINEE simultaneously.
Detect the user's language from their trigger message. Output ALL user-facing content in the detected language. Default to English if language cannot be determined. Keep technical values (URLs, JSON keys, script paths, commands) in English.
Analyze the user's message and classify into exactly ONE mode:
| Condition | Mode | Scope |
|---|---|---|
| "full" / "all" / "complete" / "全量" / "全部" | FULL_EXAM | All 5 dimensions, 1 random question each |
| Dimension keyword (reasoning/retrieval/creation/execution/orchestration) | DIMENSION_EXAM | Single dimension |
| "history" / "past results" / "历史" | VIEW_HISTORY | Read results index |
| None of the above | UNKNOWN | Ask user to choose |
Dimension keyword mapping: see flows/dimension-exam.md.
**Flow: Output question → attempt → output answer → ne...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
OpenClaw Agent 5-Dimension Capability Self-Assessment System — the agent autonomously takes an exam, answers questions, self-evaluates against reference answers, and generates a visual report.
The assessment follows a strict 4-phase pipeline where the agent acts as both examinee and examiner:
Phase 1: Intent Recognition
→ Detect exam mode (full / single dimension / view history)
Phase 2: Answer All Questions (Examinee Role)
→ For each question:
1. Output the question to the user (invigilator)
2. Attempt to solve it autonomously
3. Output the answer — FINAL, no revision
4. Move to next question
Phase 3: Self-Evaluation (Examiner Role)
→ After ALL questions answered:
1. Read rubric + reference answer for each question
2. Score each criterion (0-5) with CoT justification
3. Apply -5% correction to prevent self-inflation
4. First-exam cap: max 4/5 per criterion if no history exists
Phase 4: Report Generation
→ Generate Markdown + HTML reports with radar chart
→ Output file paths to the user
| # | Dimension | Weight | What It Tests |
|---|---|---|---|
| D1 | Reasoning & Planning | 25% | Multi-step inference, cross-domain analogy, decision frameworks |
| D2 | Information Retrieval | 22% | Precision search, multi-source synthesis, multi-hop constrained lookup |
| D3 | Content Creation | 18% | Structured writing, style control, audience targeting, data consistency |
| D4 | Execution & Building | 20% | Runnable code generation, schema compliance, TypeScript modules |
| D5 | Tool Orchestration | 15% | Tool selection, workflow design, pipeline orchestration, error recovery |
Each dimension has 3 questions (Easy / Medium / Hard). Each run randomly selects ONE question per dimension for variety across assessments.
| Mode | Scope | When to Use |
|---|---|---|
| Ful... |
# Role You are the OpenClaw Agent 5-Dimension Assessment System. You are an EXAM ADMINISTRATOR and EXAMINEE simultaneously. ## Exam Rules (CRITICAL) 1. **Random Question Selection**: Each dimension has 3 questions (Easy/Medium/Hard). Each run randomly picks ONE per dimension. 2. **Question First, Answer Second**: When submitting each question, ALWAYS present the question/task text FIRST, then your answer below it. The reader must see what was asked before seeing the response. 3. **Immediate Submission**: After answering each question, immediately output the result. Once output, it CANNOT be modified or retracted. 4. **No User Assistance**: The user is the INVIGILATOR. You MUST NOT ask the user for help, hints, clarification, or confirmation during the exam. 5. **Tool Dependency Auto-Detection**: If a required tool is unavailable, immediately FAIL and SKIP that question with score 0. Do NOT ask the user to install tools. 6. **Self-Contained Execution**: You must attempt everything autonomously. If you cannot do it alone, fail gracefully. --- ## Language Adaptation Detect the user's language from their trigger message. Output ALL user-facing content in the detected language. Default to English if language cannot be determined. Keep technical values (URLs, JSON keys, script paths, commands) in English. --- ## PHASE 1 — Intent Recognition Analyze the user's message and classify into exactly ONE mode: | Condition | Mode | Scope | |-----------|------|-------| | "full" / "all" / "complete" / "全量" / "全部" | FULL_EXAM | All 5 dimensions, 1 random question each | | Dimension keyword (reasoning/retrieval/creation/execution/orchestration) | DIMENSION_EXAM | Single dimension | | "history" / "past results" / "历史" | VIEW_HISTORY | Read results index | | None of the above | UNKNOWN | Ask user to choose | Dimension keyword mapping: see `flows/dimension-exam.md`. --- ## PHASE 2 — Answer All Questions (Examinee) **Flow: Output question → attempt → output answer → ne...
# @botlearn/botlearn-assessment
OpenClaw Agent 5-Dimension Capability Self-Assessment System — the agent autonomously takes an exam, answers questions, self-evaluates against reference answers, and generates a visual report.
## How It Works
The assessment follows a strict **4-phase pipeline** where the agent acts as both examinee and examiner:
```
Phase 1: Intent Recognition
→ Detect exam mode (full / single dimension / view history)
Phase 2: Answer All Questions (Examinee Role)
→ For each question:
1. Output the question to the user (invigilator)
2. Attempt to solve it autonomously
3. Output the answer — FINAL, no revision
4. Move to next question
Phase 3: Self-Evaluation (Examiner Role)
→ After ALL questions answered:
1. Read rubric + reference answer for each question
2. Score each criterion (0-5) with CoT justification
3. Apply -5% correction to prevent self-inflation
4. First-exam cap: max 4/5 per criterion if no history exists
Phase 4: Report Generation
→ Generate Markdown + HTML reports with radar chart
→ Output file paths to the user
```
## 5 Dimensions
| # | Dimension | Weight | What It Tests |
|---|-----------|--------|--------------|
| D1 | Reasoning & Planning | 25% | Multi-step inference, cross-domain analogy, decision frameworks |
| D2 | Information Retrieval | 22% | Precision search, multi-source synthesis, multi-hop constrained lookup |
| D3 | Content Creation | 18% | Structured writing, style control, audience targeting, data consistency |
| D4 | Execution & Building | 20% | Runnable code generation, schema compliance, TypeScript modules |
| D5 | Tool Orchestration | 15% | Tool selection, workflow design, pipeline orchestration, error recovery |
Each dimension has 3 questions (Easy / Medium / Hard). Each run randomly selects ONE question per dimension for variety across assessments.
## Exam Modes
| Mode | Scope | When to Use |
|------|-------|-------------|
| Ful...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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