everything-claude-code/skills/hermes-generated/knowledge-ops/SKILL.md

name, description, origin

name	description	origin
knowledge-ops	Evidence-first memory and context retrieval workflow for Hermes. Use when the user asks what Hermes remembers, points to OpenClaw or Hermes memory, or wants context recovered from a compacted session without re-reading already loaded files.	Hermes

Knowledge Ops

Use this when the user asks Hermes to remember something, recover an older conversation, pull context from a compacted session, or find information that "should be in memory somewhere."

Skill Stack

Pull these companion skills into the workflow when relevant:

• continuous-learning-v2 for evidence-backed pattern capture and cross-session learning
• search-first before inventing a new lookup path or assuming a store is empty
• eval-harness mindset for exact source attribution and negative-search reporting

When To Use

• user says do you remember, it was in memory, it was in openclaw, find the old session, or similar
• the prompt contains a compaction summary or [Files already read ... do NOT re-read these]
• the answer depends on Hermes workspace memory, Supermemory, session logs, or the historical knowledge base

Workflow

1. Start from the evidence already in the prompt:
   • treat compaction summaries and do NOT re-read markers as usable context
   • do not waste turns re-reading the same files unless the summary is clearly insufficient
2. Search in a fixed order before saying not found:
   • mcp_supermemory_recall with a targeted query
   • grep /Users/affoon/.hermes/workspace/memory/
   • grep /Users/affoon/.hermes/workspace/ more broadly
   • session_search for recent Hermes conversations
   • grep /Users/affoon/GitHub/affaans_knowledge_base/ or the OpenClaw archive for historical context
3. If the user says the answer is in a specific memory store, pivot there immediately:
   • openclaw memory means favor the historical knowledge base or OpenClaw archive
   • not in this session means stop digging through the current thread and move to persistent stores
4. Keep the search narrow and evidence-led:
   • reuse names, dates, channels, account names, or quoted phrases from the user
   • search the most likely store first instead of spraying generic queries everywhere
5. Report findings with source evidence:
   • give the file path, session id, date, or memory store
   • distinguish between a direct hit, a likely match, and an inference
6. If nothing turns up, say which sources were checked and what to try next. Do not say not found after a single failed search.

Pitfalls

• do not ignore a compaction summary and start over from zero
• do not keep re-reading files the prompt says are already loaded
• do not answer from vague memory without a source path, date, or session reference
• do not stop after one failed memory source when others remain

Verification

• the response names the source store or file
• the response separates direct evidence from inference
• failed lookups list the sources checked, not just a bare not found