Initial commit: Hermes Agent Skills collection

2026-07-12 19:02:59 +00:00
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+# Retrospective Session-Log Analysis
+
+Technique for applying compound-learning to past session logs — mining completed work for
+learnings that weren't captured at the time.
+
+## When to Use
+
+- User asks to "analyze recent sessions" or "apply compound-learning to session logs"
+- Periodic knowledge audit (e.g., weekly review of what was solved but not documented)
+- After a burst of activity where multiple sessions completed without individual compound-learning passes
+
+## Process
+
+### Step 1: Enumerate Recent Sessions
+
+Query the Hermes `state.db` directly (SQLite is in `~/.hermes/state.db`):
+
+```sql
+SELECT id, title, source, started_at, ended_at, message_count, tool_call_count,
+       estimated_cost_usd
+FROM sessions
+WHERE archived = 0
+ORDER BY started_at DESC
+LIMIT 10;
+```
+
+Column `id` is the session ID (TEXT). `started_at` is a Unix REAL timestamp.
+
+### Step 2: Extract Key Content Per Session
+
+For each session, extract:
+- **First user message** — the task/goal
+- **Last assistant message** — the resolution/outcome
+- **Tool usage counts** — `SELECT tool_name, COUNT(*) FROM messages WHERE session_id = ? GROUP BY tool_name`
+- **User messages** (skip system preambles like `[IMPORTANT:` and `[CONTEXT COMPACTION`)
+
+```sql
+SELECT id, role, content
+FROM messages
+WHERE session_id = ? AND content IS NOT NULL AND content != '' AND length(content) > 50
+ORDER BY id ASC;
+```
+
+**Note:** Some assistant messages have empty `content` but populated `tool_calls` (JSON). If
+content-based extraction yields thin results, check `tool_calls` column for the actual work.
+
+### Step 3: Classify Each Session
+
+Apply compound-learning Phase 2 classification:
+
+| Signal | Type | Worth a solution doc? |
+|---|---|---|
+| Bug fixed, root cause found | `bug-fix` | Yes, if non-trivial |
+| Architecture decision made | `architecture-pattern` | Yes |
+| Tool/library chosen or configured | `tooling-decision` | Yes |
+| Multi-step workflow executed | `workflow` | Yes, if reusable |
+| Third-party API/device integrated | `integration` | Yes |
+| Price search, research, Q&A | — | Usually no (one-off) |
+| Cron job ran successfully | — | Only if new failure mode discovered |
+
+**Skip sessions that are:** routine cron runs with no errors, pure research/Q&A, or
+sessions that ended inconclusively without a resolution.
+
+### Step 4: Write Solution Docs
+
+Follow compound-learning Phases 3-6 for each qualifying session:
+1. Overlap check: `search_files("keyword", path="docs/solutions/")`
+2. Write to `docs/solutions/{type}/{YYYY-MM-DD}-{slug}.md` with YAML frontmatter
+3. Cross-reference related docs (e.g., HA token expiry → HA SSH addon integration)
+4. Include the session ID in the References section for traceability
+
+### Step 5: Hindsight Sync (Phase 4.5)
+
+**Check daemon health FIRST:**
+```bash
+curl -s http://127.0.0.1:9177/health
+```
+
+If healthy: batch `hindsight_retain` for all solution docs.
+If down: skip Hindsight sync — `docs/solutions/` files are sufficient alone.
+
+### Step 6: Self-Improvement Routing (Phase 7)
+
+Review all solution docs collectively for systemic gaps:
+- Does a skill need a patch? (e.g., "always try SSH addon first for HA")
+- Does MEMORY.md need a durable fact? (check for overflow first!)
+- Is there a recurring failure pattern across multiple sessions?
+
+Present SI suggestions as a batch table to the user.
+
+## Pitfalls
+
+- **Don't write a solution doc for every session** — routine cron runs and one-off research don't qualify
+- **Include session IDs in References** — enables future `session_search(session_id=...)` deep-dives
+- **Cross-reference related solutions** — e.g., "HA token expiry" and "HA SSH addon integration" should link to each other
+- **MEMORY.md overflow** — batch SI suggestions may collectively exceed the 2,200 char limit. Check current usage before proposing multiple memory additions.
+- **Empty content fields** — some assistant messages store work in `tool_calls` JSON, not `content`. If extraction looks thin, query `tool_calls` as well.
+
+## SQLite Schema Reference
+
+```sql
+-- sessions table
+id TEXT PRIMARY KEY         -- session ID
+source TEXT                 -- 'telegram', 'cron', etc.
+started_at REAL             -- Unix timestamp
+ended_at REAL               -- Unix timestamp (nullable)
+message_count INTEGER
+tool_call_count INTEGER
+estimated_cost_usd REAL
+title TEXT                  -- session title (nullable)
+archived INTEGER DEFAULT 0
+
+-- messages table
+id INTEGER PRIMARY KEY
+session_id TEXT             -- FK to sessions.id
+role TEXT                   -- 'user', 'assistant', 'tool'
+content TEXT                -- message text (may be empty for tool-call-only turns)
+tool_calls TEXT             -- JSON array of tool calls
+tool_name TEXT              -- tool name (for role='tool' rows)
+timestamp REAL
+```