smithery/rickoslyder

digest-generation

Generate a weekly AI intelligence digest from synthesized topic analyses and hype assessments. Use after synthesis and hype assessment to produce a readable, opinionated summary for sophisticated technical readers.

Installation

$ npx skills add smithery/rickoslyder --skill digest-generation

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,798 B
  • docs SUMMARY.md 239 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Digest Generation Skill

Generate a weekly digest of AI research intelligence for sophisticated technical readers.

Audience

Your readers are:

  • Technical professionals who follow AI closely
  • Don't need basics explained
  • Want signal, not noise
  • Appreciate direct, opinionated takes
  • Value evidence-based analysis
  • Skeptical of hype but interested in real progress

CRITICAL: Balanced Topic Coverage

You MUST cover ALL major topics proportionally to their claim volume.

Before writing, check the claim distribution. If a topic has 15% of claims, it should get roughly 15% of the digest. Do NOT let hype signals dominate - a topic with 5% of claims but high hype should NOT get more coverage than a topic with 15% of claims.

Topics to always cover (if they have claims):

  • multimodal, reasoning, agents, infrastructure, benchmarks, scaling (the "core capability" topics)
  • policy, safety, rlhf, interpretability (the "meta" topics)
  • robotics, general

If multimodal has 15% of claims and RLHF has 5%, multimodal should get 3x the coverage.

Digest Structure

TL;DR

5-7 bullet points covering the BREADTH of topics analyzed.

Format:

  • Include at least one bullet from each major topic area (capabilities, safety, infrastructure)
  • Lead with the topic that has the most claims, not the most hype
  • Ensure diverse topic representation - don't let 2-3 topics dominate

Hype Check

Brief assessment of what's overhyped and underhyped.

For each:

  • Name the topic
  • Explain WHY in 1-2 sentences
  • Cite specific evidence

Example:

Overhyped: Agents (+0.4 delta) — Lab enthusiasm for autonomous agents continues to outpace demonstrated reliability. Recent production deployments show 30-40% failure rates on complex tasks.

Underhyped: Interpretability (-0.3 delta) — Golden Gate Claude and follow-on work show feature steering is becoming practical. Most coverage focuses on capabilities, missing this control story.

Topic Breakdown

REQUIRED SECTION - Brief summary of EACH major topic with claims.

For each topic with >3% of claims, include:

  • Topic name and claim count
  • Key finding or trend in 1-2 sentences
  • Notable quote if available

Example:

Multimodal (137 claims, 15%): Video generation architectures converging on diffusion with temporal attention. Key debate: compute efficiency vs quality tradeoffs.

Reasoning (101 claims, 11%): Chain-of-thought still dominant but tree-of-thought gaining traction. Critics note benchmark gaming concerns.

Research Signals

What lab researchers are hinting at or claiming.

Cover signals from MULTIPLE topics, not just the most hyped.

Focus on:

  • Hints about unreleased work
  • Specific capability claims
  • Unexpected admissions of limitations
  • Predictions from credible sources

Quote notable statements with attribution.

Critic Corner

What skeptics are saying and why.

Focus on:

  • Substantive critiques (not just dismissals)
  • Specific counter-arguments to lab claims
  • Alternative explanations for results
  • Concerns worth considering

Key Debates

The most important ongoing disagreements.

For each debate:

  • State the question
  • Summarize both positions
  • Note any new evidence this week

Predictions Tracker

Notable predictions made this week.

Format as table or list:

Prediction Author Confidence Timeframe
"..." Name High/Med/Low Near/Med/Long

Worth Watching

Topics or threads that may become important in coming weeks.

Brief bullets on:

  • Emerging narratives
  • Quiet developments
  • Things that might break through

Tone Guidelines

Do:

  • Be direct and opinionated
  • Take positions based on evidence
  • Call out hype when warranted
  • Acknowledge genuine progress
  • Use specific examples and quotes
  • Write for experts

Don't:

  • Hedge excessively
  • Repeat conventional wisdom without analysis
  • Use marketing language
  • Explain basics
  • Be boring
  • Exceed 1500 words

Example Opening

This week's AI discourse was dominated by [topic], with lab researchers claiming [X] while critics countered with [Y]. The most interesting signal came from [source], who hinted that [implication]. Meanwhile, [underhyped topic] continues to see quiet progress that deserves more attention.

Output Format

Return the digest as markdown, ready for publication.

Include frontmatter:

---
title: AI Intelligence Digest - Week of [DATE]
generated: [TIMESTAMP]
claims_analyzed: [N]
topics_covered: [LIST]
---