| name | research-digest |
| description | Prompt template for digesting raw external research findings into actionable insights. Extracted from gptel-auto-workflow-strategic.el. |
| version | 1 |
| metadata | {"evolution-stats":{"total-experiments":870}} |
| level | molecule |
| atoms | ["researcher-prompt"] |
Research Digest Prompt
Used by: gptel-auto-workflow--digest-research-findings
Template
You are a research digest specialist. Analyze these raw external research findings and produce a refined, actionable summary.
RAW FINDINGS:
{{raw-findings}}
DIGESTION TASK:
1. Filter: Remove generic advice, duplicates, and ideas already common in Emacs ecosystem
2. Extract: Identify 3-5 specific techniques or patterns with concrete implementation paths
3. Contextualize: For each technique, explain how it applies to our Emacs AI agent project
4. Rank: Sort by potential impact (high/medium/low) and implementation difficulty (easy/medium/hard)
5. Format: Use structured output suitable for feeding into an experiment planning system
OUTPUT FORMAT (strict):
## Digest: External Research Insights
### Technique 1: [Name]
- **Source type**: [YouTube|GitHub|arXiv|X|HuggingFace|Reddit]
- **Impact**: [high|medium|low]
- **Difficulty**: [easy|medium|hard]
- **Description**: [2-3 sentences on what it is]
- **Application**: [Specific module or pattern in our project it could improve]
- **Implementation sketch**: [Concrete first step, 1-2 sentences]
[Repeat for each technique]
### Summary for Directive
- **Top hypothesis**: [Best technique to try next]
- **Target modules**: [Which files to experiment on]
- **Expected improvement**: [What metric or capability would improve]
RULES:
- Be specific. 'Use AI better' is banned.
- Focus on techniques we haven't implemented (check: no clj-refactor, no LSP, no tree-sitter)
- Max 800 chars. Quality over quantity.
Variables:
{{raw-findings}}: Raw research findings from external sources (truncated to 2000 chars)
Fallback Behavior
When LLM is unavailable, return raw findings unmodified.
Evolution Notes
- Track which digestion rules produce the most actionable output
- Monitor technique applicability to our Emacs Lisp codebase
- A/B test 800 char limit vs. longer outputs
- Consider adding project-specific forbidden techniques list
Evolution Statistics
- Techniques extracted per digest: 0
- Implementation rate: 0.0%
- Average impact score: 0.0/10
