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Self-service enhancement package for Hermes agents to adopt pi-style conduct quality. Contains: - prompts/behavioral-core.md: Distilled Three Pillars (~800 tokens) - config/compression.yaml: 256K model optimization (80% threshold) - config/mcp-servers.yaml: Tool parity (Context7, GitHub, Firecrawl, etc.) - skills/: On-demand skills for conduct, verification, self-healing - CHECKLIST-POC.md: Tanko POC verification checklist POC pilot: Tanko
2.3 KiB
2.3 KiB
POC Checklist — Tanko Enhancement Verification
Pilot: Tanko Date: 2026-06-24 Status: ⬜ Not started
Pre-Checks
- Read this entire repo (README.md, INSTALL.md, all prompts and configs)
- Verify current Hermes version
- Backup current config.yaml, SOUL.md, and skills directory
- Note baseline context usage (prompt tokens before any changes)
Installation
- Merge
prompts/behavioral-core.mdinto SOUL.md behavioral section - Add MCP servers from
config/mcp-servers.yamlto config.yaml - Verify MCP tools appear (expect: ra-h-os, context7, github, firecrawl, searxng)
- Copy skills/ to skills directory
- Update compression config from
config/compression.yaml - Update
external_dirsin config.yaml for skills loading - Restart Hermes session
Behavioral Verification
- Agent demonstrates Proactive behavior — suggests improvements unprompted
- Agent demonstrates Persistent behavior — recovers from compaction without missing a beat
- Agent demonstrates Self-Improving behavior — diagnoses errors, logs learnings
- Agent verifies CLI flags and API params before answering (typescript-like discipline)
- Agent is direct and decisive — no option-listing, no unnecessary politeness
Tool Parity Verification
- Can query RA-H OS knowledge graph (
ra-h-os-queryNodes) - Can verify docs via Context7 (
context7-resolve-library-id+context7-query-docs) - Can search code via GitHub MCP (
github-search_code) - Can scrape web pages via Firecrawl (
firecrawl-scrape) - Can search web via SearXNG (
searxng-searxng_web_search)
Performance Verification
- Measure first-token latency before/after
- Measure total system prompt tokens before/after (target: 50-60% reduction)
- Verify compression triggers at correct threshold (80% of 256K = 204,800 tokens)
- Verify no functionality lost — can still accomplish all prior tasks
Rollback
- If enhancement causes regression, restore from backup
- Report what failed to
SyslogSolution/hermes-pi-enhancementissues
Report
After completing, Tanko should create a relay message or issue with:
- Before/after measurements (prompt tokens, latency)
- What felt different (qualitative)
- Any tools that didn't work
- Recommendations for other agents