القائمة
Prompt template for digesting raw external research findings into actionable insights. Extracted from gptel-auto-workflow-strategic.el.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Transforms research paper analysis from extraction to narrative storytelling. Uses 7-beat narrative spine (protagonist/dilemma/old-path/turning-point/solution/ending/core) to make papers understandable to non-experts. Includes speed-read card, PhD advisor review, and real-world testing. Use when researcher encounters a research paper and needs to extract deep understanding, not just surface facts. Triggers on "paper", "research paper", "analyze paper", "tell me about this paper", "讲论文", "读论文".
Workflow chain: researcher → paper-storytelling. When researcher encounters a research paper, automatically invoke paper-storytelling to transform extraction into narrative understanding. Use when user says "research paper", "analyze this paper", "tell me about this paper", or when researcher detects arxiv/PDF links.
Prompt template for external research specialist subagent. Auto-evolves based on experiment outcomes.
Inspect and operate OV5 (Ouroboros V5) through the live Emacs daemon. Use when checking auto-workflow status, starting guarded runs, reviewing experiment results, or querying researcher and evolution state.
Clojure REPL client (nREPL-based, Babashka). Use for evaluating Clojure code, loading Clojure files, fixing unbalanced brackets, and interactive nREPL work. Not the Elisp daemon-repl.
Daemon REPL for Elisp — evaluate Elisp code in a running Emacs daemon via emacsclient, validate brackets before save, auto-evaluate .el files on change. Use when you need to run Elisp from outside Emacs, check daemon status, or validate Elisp syntax.
| 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"] |
Used by: gptel-auto-workflow--digest-research-findings
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)When LLM is unavailable, return raw findings unmodified.