meta-prompt
Skill275 starsChanged 36 days agoApache-2.0View on GitHub
Meta-prompting framework for critiquing responses, analyzing solution trajectories, and evaluating AI-generated content quality
5 other projects use a version of this.
Adopted by
Most recently taken up by
- hmbseaotter/meta-prompt-toolkittheir own version, first seen Jul 19, 2026
- Haochenhust/ch-skillstheir own version, first seen Jul 12, 2026
- naticorrea-03/claude-code-skillstheir own version, first seen Jul 1, 2026
- GlamgarOnDiscord/claude-saas-blueprinttheir own version, first seen May 17, 2026
- mizukaizen/hive-doctrine-skillstheir own version, first seen Mar 9, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- Haochenhust/ch-skills★ 9same name
- naticorrea-03/claude-code-skills★ 4same name
- GlamgarOnDiscord/claude-saas-blueprint★ 2same name
- hmbseaotter/meta-prompt-toolkit★ 0same name
- mizukaizen/hive-doctrine-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository264 other files it gives its agents.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 27
When evaluating responses, consider the following:
- Distinctive: line 67
When analyzing a trajectory:
- Distinctive: line 74
Do not generate additional steps.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Meta-Prompt
A collection of meta-prompting techniques for evaluating and analyzing AI responses and solution paths.
Response Quality Evaluator
A framework for critiquing and reflecting on the quality of responses, providing a score and indicating whether the response has fully solved the question or task.
Evaluation Fields
Reflections: The critique and reflections on the sufficiency, superfluency, and general quality of the response.
Score: Score from 0-10 on the quality of the candidate response.
Found_solution: Whether the response has fully solved the question or task.
Evaluation Criteria
When evaluating responses, consider the following:
- Accuracy: Does the response correctly address the question or task?
- Completeness: Does it cover all aspects of the question or task?
- Clarity: Is the response clear and easy to understand?
- Conciseness: Is the response appropriately concise without sacrificing important details?
- Relevance: Does the response stay focused on the question or task at hand?
Provide thoughtful reflections on these aspects and any other relevant factors. Use the score to indicate the overall quality, and set found_solution to true only if the response fully addresses the question or completes the task.
Example Usage
reflections: "The response was clear and concise, addressing the main question effectively. However, it could have provided more context on edge cases."
score: 8
found_solution: trueQuestion-Answering Trajectory Analyzer
Guidelines for analyzing solution paths to question-answering tasks.
Trajectory Components
Observations: Environmental information about the current situation that provides context for decision-making.
Thoughts: Reasoning about the current situation, analyzing what has been observed and planning next steps.
Actions: The steps taken to progress toward solving the task.
Action Types
Search[entity]: Searches for the exact entity and returns relevant information if the entity exists. If not, returns suggestions for similar entities.
Lookup[keyword]: Returns the next relevant passage that contains the keyword. Used for finding specific information within retrieved content.
Finish[answer]: Returns the answer and finishes the task. Used when sufficient information has been gathered to provide a definitive response.
Analysis Guidelines
When analyzing a trajectory:
- Evaluate whether each observation provides useful information
- Assess if thoughts demonstrate logical reasoning
- Determine if actions are appropriate given the current state
- Score the trajectory correctness from 1-10
- Evaluate reasoning validity even in incomplete trajectories
- Do not generate additional steps; only analyze what is provided
Prompt Engineering Patterns
Chain of Thought
Guide the model through step-by-step reasoning:
Let's approach this step by step:
1. First, identify the key components
2. Then, analyze each component
3. Finally, synthesize the findingsFew-Shot Learning
Provide examples to establish the pattern:
Example 1: [input] -> [output]
Example 2: [input] -> [output]
Now apply this pattern to: [new input]Self-Consistency
Generate multiple reasoning paths and select the most consistent answer.
Reflection Prompts
Encourage self-critique:
Review your response and identify:
- Any potential errors or oversights
- Areas that could be explained more clearly
- Missing information that would strengthen the answerQuality Metrics
Response Scoring Rubric
- 10: Perfect response, fully addresses all aspects with exceptional clarity
- 8-9: Excellent response with minor room for improvement
- 6-7: Good response that addresses the main points but lacks depth
- 4-5: Adequate response with significant gaps or unclear explanations
- 2-3: Poor response that misses key aspects or contains errors
- 0-1: Response fails to address the question or is completely incorrect
Trajectory Scoring
- 10: Optimal path with efficient, logical steps
- 7-9: Good path with minor inefficiencies
- 4-6: Acceptable path but with unnecessary steps or missed opportunities
- 1-3: Poor path with fundamental reasoning errors
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

