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episodic-archiver

Episodic Memory Archiver. Stores full conversation transcripts with embeddings and analysis into ArangoDB. Tracks UNRESOLVED sessions for reflection with structured failure episodes (trigger/diagnosis/action/outcome), K~4 similar failure retrieval, user behavioral profiling, and federated taxonomy classification.

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grahama1970/agent-stack-public
Última atividade na origem
24 de setembro de 2026 às 15:51
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inglês
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SKILL.md
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name
episodic-archiver
description
Episodic Memory Archiver. Stores full conversation transcripts with embeddings and analysis into ArangoDB. Tracks UNRESOLVED sessions for reflection with structured failure episodes (trigger/diagnosis/action/outcome), K~4 similar failure retrieval, user behavioral profiling, and federated taxonomy classification.
internal
true
allowed-tools
Bash
triggers
["archive conversation","save episode","store transcript","remember this conversation","list unresolved","fix success rate","similar failures","user profile"]
metadata
{"short-description":"Analyzes and stores episodic conversation memory with failure learning and user profiling"}
provides
["episodic-archiver"]
composes
["memory","edge-verifier","scheduler","treesitter","interview","task-monitor","agentic-evals"]
disciplines
["memory-knowledge"]
> STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT. # Episodic Archiver Analyzes conversation transcripts, embeds them for search, categorizes turns, **tracks unresolved sessions** for later reflection, and **builds per-user behavioral profiles**. Implements the **self-healing agentic pattern** with: - **Structured failure episodes** (trigger/diagnosis/action/outcome) - **K~4 similar failure retrieval** (research shows K~4 is optimal) - **Fix outcome tracking** (what worked, which lessons helped) - **User behavioral profiling** (communication style, expertise, bridge affinities) - **Federated taxonomy** with high-fidelity LLM mode for nightly analysis ## Commands ```bash # Archive a conversation transcript ./run.sh archive transcript.json # Archive recent sessions from all registered sources ./run.sh archive-recent --hours 24 # Deep LLM analysis of an archived session ./run.sh analyze <session_id> # List unresolved sessions (for reflection) ./run.sh list-unresolved # Mark a session as resolved WITH fix tracking ./run.sh resolve <session_id> --fix "What fixed it" --lessons lesson1,lesson2 --outcome success # View fix success rate metrics ./run.sh stats # Register a transcript source ./run.sh register <name> <path> <glob> ``` ## User Behavioral Profiling Each session is analyzed to extract: ```json { "communication_style": "technical|casual|formal|mixed", "expertise_domains": ["python", "security"], "expertise_level": "beginner|intermediate|advanced|expert", "response_preferences": { "verbosity": "concise|balanced|detailed", "format": "code-first|explanation-first|mixed" }, "bridge_affinities": {"Precision": 0.8, "Resilience": 0.6} } ``` Profiles are incrementally merged into `user_priors` collection (RGMem-style): - Bridge affinities: weighted average across sessions - Expertise domains: union (accumulate) - Communication style: most-recent-3-sessions voting ## LLM Model Selection - **Real-time archiving**: scillm `quick_completion()` (fast, low latency) - **Nightly analysis**: `deepseek-ai/DeepSeek-V3.1-TEE` via `CHUTES_MODEL_ID` - All LLM calls go through scillm (no raw httpx) ## Storage **Collections:** - `agent_conversations` - Individual turns with embeddings, user_id, persona_id - `unresolved_sessions` - Sessions needing follow-up (with failure episodes) - `session_summaries` - LLM-analyzed session assessments with taxonomy - `user_priors` - Per-user behavioral profiles (incrementally updated) **Turn categories:** Task, Question, Solution, Error, Chat, Meta ## Input Format ```json { "session_id": "task_123", "user_id": "graham", "persona_id": "pi", "messages": [ {"from": "User", "content": "Fix the bug in auth", "timestamp": 1234567890}, {"from": "Agent", "content": "Looking at auth.py...", "timestamp": 1234567891} ] } ``` ## Integration | Skill | How | |-------|-----| | `monitor-episodic-archiver` | Nightly pipeline, health monitoring | | `memory` | Stores lessons from resolved sessions | | `dogpile` | Researches unresolved gaps | | `taxonomy` | Federated bridge classification | | `scillm` | All LLM calls (quick_completion, acompletion) | | `train-convo-steering` | State bucket estimation for steering | ## Common Mistakes ### WRONG: Archiving without tracking resolution status ```bash ./run.sh archive transcript.json # archived but never resolved ``` ### RIGHT: Track unresolved sessions and resolve with fix tracking ```bash ./run.sh archive transcript.json ./run.sh list-unresolved # check what needs follow-up ./run.sh resolve <session_id> --fix "What fixed it" --lessons lesson1 --outcome success ``` ### WRONG: Using raw httpx for LLM calls instead of scillm ```python resp = httpx.post("https://api.chutes.ai/...", ...) # bypass scillm ``` ### RIGHT: All LLM calls go through scillm ```python from scillm import quick_completion result = quick_completion("Analyze this session...") ``` ### WRONG: Forgetting to register transcript sources ```bash ./run.sh archive-recent # no sources registered, archives nothing ``` ### RIGHT: Register sources first, then archive ```bash ./run.sh register pi-sessions ~/.pi/sessions/ "*.json" ./run.sh archive-recent --hours 24 ```
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