hermes-report-quality

name: hermes-report-quality description: Gwern-inspired quality techniques for Hermes AI reports — Anti-Examples stripping, Manual of Style (MoS), Atomic Snippets multi-level abstraction, Generate-Rank-Select iteration, and Engram knowledge pathways. Improves report tone, consistency, depth, and usefulness. tags: - report-writing - quality - anti-examples - style-guide - gwern - atomic-snippets

Hermes Report Quality — Gwern-Inspired Techniques

A set of 5 quality techniques distilled from [[concepts/llm-creative-writing|Gwern's LLM Creative Writing methodology]], adapted specifically for Hermes' wiki reports, digests, and knowledge summaries.

Technique 1: Anti-Examples Slop Stripping

Problem: AI-generated reports default to a generic, "ChatGPTese" tone — verbose, hedging, and formulaic.

Workflow:

1. Generate the report as usual
2. Self-critique: After writing, add a self-review step: "Identify 3-5 phrases in this report that sound like generic AI prose. Explain why they're weak, then rewrite them."
3. Reverse fix: Apply the rewritten versions
4. Meta-cognition check: Force reasoning about why the original phrasing was weak, not just what to change

Example application: In daily hot-posts reports, after listing topics, add a self-review phase checking for:

• Overused transitions ("Meanwhile", "Notably", "It's worth noting")
• Vague praise ("impressive", "significant", "important" without context)
• Formulaic sentence structure (topic sentence → explanation → implication)

Technique 2: Manual of Style (MoS) for Reports

Definition: An explicit style guide for Hermes' voice across all report types.

Core Style Rules

• Japanese for Discord/Telegram reports: Concise bullet-driven format
• English for Slack/technical analysis: Precise, data-rich, no filler
• Every claim needs a source: Wikilink or URL citation mandatory
• One sentence per insight: No padding paragraphs
• Use comparison tables for multi-entity analysis

Anti-Examples (what NOT to do)

• ❌ "It is worth noting that..." → ✅ Just state the fact
• ❌ "This represents a significant development in the field of..." → ✅ Name the specific field + why
• ❌ "As we move forward, it will be interesting to see..." → ✅ Make a specific prediction or state uncertainty
• ❌ Hedging everything ("may", "could", "might", "potentially") → ✅ State confidence level explicitly ("60% probability", "speculative")
• ❌ "In today's rapidly evolving AI landscape..." → ✅ Delete. Start with the content.

Technique 3: Atomic Snippets — Multi-Level Abstraction

Idea: Present the same content at multiple granularity levels so the reader picks their depth.

Three-Level Format (recommended for reports)

## 🔍 Topic: X (1-line summary)
**One-liner**: X just released Y, competing with Z on W.

**Details**:
- Key metric: ...
- Architecture: ...
- Comparison: ...

**Deep dive** (collapsible or optional section):
- Technical architecture details
- Benchmark methodology
- Implications for the ecosystem

Implementation: Start every report section with a one-liner (mandatory for all readers), then progressively add detail. Readers stop when satisfied.

When to use each level

Technique 4: Generate-Rank-Select for Critical Reports

Problem: First-draft reports have hidden quality variance. Gwern's insight: "Adding bits beats slop" — the value is in the search process.

Workflow for important reports:

1. Generate N variants (2-3) of the report title and opening paragraph
2. Self-rank by criteria: informativeness, conciseness, engagement
3. Select & merge the best elements
4. Write the rest using the selected tone

When to apply: When a report has strategic importance (trend analysis, ecosystem comparison, or any report with decision-making implications). Skip for routine "no change" reports.

Catch: Don't spend tokens on G-R-S for every report — reserve for:

• Weekly digests
• Trend analysis
• Reports with specific predictions
• Reports the user has indicated high interest in

Technique 5: Engram Knowledge Pathways

Problem: The model has knowledge it can't recall at generation time — like engrams with few access paths.

Workflow before report generation:

1. Pre-load context: Scan wiki for 3-5 related pages before writing
2. Cross-reference injection: Embed wikilinks to related concepts inline
3. Tag expansion: Include tags from related pages in the report's reference section
4. Multiple query angles: Before writing, ask yourself "what are 3 different ways this topic connects to existing wiki knowledge?" — then use the richest angle

Anti-pattern: Generating a report from scratch without checking what the wiki already knows about the entities and concepts mentioned.

Applying to Existing Cron Jobs

Slack Hot Posts (ai-topics-slack-hot-posts — every 4h)

This is the highest-visibility report.

Current impl status (May 2026): T1-T5 instructions are embedded inline in the prompt body (not just via skill loading). Do NOT suggest adding T1-T5 — it's already done. The prompt has explicit:

• T1 Anti-Examples slop-stripping checklist
• T2 MoS (style, wikilinks, comparison tables)
• T3 Atomic Snippets (One-liner + Details)
• T5 Engram Pathways (pre-scan related_wiki_pages)
• Per-slot posting guidance (morning/midday/evening/night/late-night/pre-morning)

If adding new quality guidance: Append to the existing Quality Requirements section in the prompt rather than duplicating.

Dreaming Wiki Ingest (dreaming-wiki-ingest — daily)

• Anti-Examples (T1): Strip generic analysis from nightly consolidation
• Generate-Rank-Select (T4): For the top 1-2 findings, generate alternatives
• Atomic Snippets (T3): Summary → Detailed findings → Technical appendix
• Engram (T5): Cross-wiki context for each consolidated theme

Weekly AI Digest (telegram, weekly)

• All 5 techniques — this is the highest-stakes report

Reference

• [[concepts/llm-creative-writing]] — Full Gwern methodology
• [[entities/gwern]] — Gwern Branwen
• [[wiki/index.md]] — Current wiki state