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
• continuous-agent-loop when the lookup spans multiple stores, compaction summaries, and follow-up recovery steps
• 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 prompt says use the context summary above, proceed, or otherwise hands off loaded context plus a concrete writing, editing, or response task
• 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
   • if the prompt already says use the context summary above or asks you to proceed with writing, editing, or responding, continue from that loaded context first and search only the missing variables
   • 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, unless the user already named the store:
   • 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/ and /Users/affoon/.hermes/openclaw-home/hub/workspace/memory/ for historical context
3. If the user says the answer is in a specific memory store, pivot there immediately after the initial targeted recall:
   • openclaw memory means favor /Users/affoon/GitHub/affaans_knowledge_base/ and /Users/affoon/.hermes/openclaw-home/hub/workspace/memory/
   • not in this session means stop digging through the current thread and move to persistent stores instead of re-reading current-session files
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 turn a loaded-context handoff into a pure retrieval loop when the user already asked for an actual draft, edit, or response
• do not keep searching the current session after the user already named OpenClaw or another persistent store as the likely source
• 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