amcs/llm/memory.md

# AMCS Memory Instructions

AMCS (Avalon Memory Crystal Server) is an MCP server for capturing and retrieving thoughts, memory, and project context. It is backed by Postgres with pgvector for semantic search.

You have access to an MCP memory server named AMCS.

Use AMCS as memory with two scopes:
- Project memory: preferred when the current work clearly belongs to a known project.
- Global notebook memory: allowed only when the information is not tied to any specific project.

## Scope Selection Rules

1. Infer the current project from the repo, current working directory, README, package or app name, and any explicit user instruction.
2. Call `get_active_project`.
3. If the active project clearly matches the current work, use it.
4. If not, call `list_projects` and look for a strong match by name or explicit user intent.
5. If a strong match exists, call `set_active_project` and use project-scoped memory.
6. If no strong project match exists, you may use global notebook memory with no project.
7. If multiple projects plausibly match, ask the user before reading or writing project memory.

## Session Startup

At the very start of any session with AMCS:

1. Call `describe_tools` to get the full list of available tools with their categories and any notes you have previously annotated. Read the notes before using a tool — they contain accumulated gotchas, workflow patterns, and field-ordering requirements you have recorded from prior sessions.

## Project Session Startup

After setting the active project:

1. Call `list_project_skills` to load any saved agent behaviour instructions for the project.
2. Call `list_project_guardrails` to load any saved agent constraints for the project.
3. Apply all returned skills and guardrails immediately and for the duration of the session.
4. Only generate or define new skills and guardrails if none are returned. If you do create new ones, store them with `add_skill` or `add_guardrail` and link them to the project with `add_project_skill` or `add_project_guardrail` so they persist for future sessions.

## Project Memory Rules

- Use project memory for code decisions, architecture, TODOs, debugging findings, and context specific to the current repo or workstream.
- Before substantial work, always retrieve context with `get_project_context` or `recall_context` so prior decisions inform your approach.
- Save durable project facts with `capture_thought` after completing meaningful work.
- Use `save_file` or `upload_file` for project assets the memory should retain, such as screenshots, PDFs, audio notes, and other documents.
- If the goal is to retain the artifact itself, store the file directly instead of first reading, transcribing, or summarizing its contents.
- For binary files or files larger than 10 MB, call `upload_file` with `content_path` (absolute server-side path) first to get an `amcs://files/{id}` URI, then pass that URI to `save_file` as `content_uri` to link it to a thought. This avoids base64 encoding entirely.
- For small files (≤10 MB) where a server path is not available, use `save_file` or `upload_file` directly with `content_base64`.
- Link files to a specific memory with `thought_id` when the file belongs to one thought, or to the project with `project` when the file is broader project context.
- Use `list_files` to browse project files or thought-linked files before asking the user to resend something that may already be stored.
- Use `load_file` when you need the actual stored file contents back. The result includes both `content_base64` and an embedded MCP binary resource at `amcs://files/{id}` — prefer the embedded resource when your client supports it.
- You can also read a stored file's raw binary content directly via MCP resources using the URI `amcs://files/{id}` without calling `load_file`.
- Stored files and attachment metadata must not be sent to the metadata extraction client.
- Do not attach memory to the wrong project.

## Global Notebook Rules

- Use global memory only for information that is genuinely cross-project or not project-bound.
- Examples: user preferences, stable personal workflows, reusable conventions, general background facts, and long-lived non-project notes.
- If information might later be confused as project-specific, prefer asking or keep it out of memory.

## Memory Hygiene

- Save only durable, useful information.
- Do not save secrets, raw logs, or transient noise.
- Prefer concise summaries.
- Prefer linking a file to a thought plus a concise thought summary instead of storing opaque binary artifacts without context.
- Do not read a file just to make it storable; store the file directly and read it only when the file contents are needed for reasoning.
- Do not base64-encode a file to pass it to `save_file` if an `amcs://files/{id}` URI is already available from a prior `upload_file` or HTTP upload.
- When saving, choose the narrowest correct scope: project if project-specific, global if not.

## Tool Annotations

As you learn non-obvious behaviours, gotchas, or workflow patterns for individual tools, persist them with `annotate_tool`:

```json
{ "tool_name": "capture_thought", "notes": "Always pass project explicitly — session state is unreliable in this client." }
```

Notes are returned by `describe_tools` in future sessions. Annotate whenever you discover something worth remembering: a required field combination, a parameter that behaves unexpectedly, or a preferred call sequence. Pass an empty string to clear a note.

## Skills and Guardrails

- **Skills** are reusable agent behaviour instructions (e.g. output formatting rules, reasoning strategies, workflow conventions).
- **Guardrails** are agent constraints and safety rules (e.g. never delete without confirmation, do not expose secrets). Each guardrail has a severity: `low`, `medium`, `high`, or `critical`.
- Use `add_skill` / `add_guardrail` to create new entries, `list_skills` / `list_guardrails` to browse the full library, and `remove_skill` / `remove_guardrail` to delete entries.
- Use `add_project_skill` / `add_project_guardrail` to attach entries to the current project, and `remove_project_skill` / `remove_project_guardrail` to detach them.
- Always load project skills and guardrails at session start before generating new ones — see Project Session Startup above.

## Short Operational Form

At the start of every session, call `describe_tools` to read the full tool list and any accumulated usage notes. Use AMCS memory in project scope when the current work matches a known project; if no clear project matches, global notebook memory is allowed for non-project-specific information. At the start of every project session call `list_project_skills` and `list_project_guardrails` and apply what is returned; only create new skills or guardrails if none exist. If your MCP client does not preserve sessions across calls, pass `project` explicitly instead of relying on `set_active_project`. Store durable notes with `capture_thought`. For binary files or files larger than 10 MB, call `upload_file` with `content_path` to stage the file and get an `amcs://files/{id}` URI, then pass that URI to `save_file` as `content_uri` to link it to a thought. For small files, use `save_file` or `upload_file` with `content_base64` directly. Browse stored files with `list_files`, and load them with `load_file` only when their contents are needed. Stored files can also be read as raw binary via MCP resources at `amcs://files/{id}`. Never store project-specific memory globally when a matching project exists, and never store memory in the wrong project. If project matching is ambiguous, ask the user. Whenever you discover a non-obvious tool behaviour, gotcha, or workflow pattern, record it with `annotate_tool` so future sessions benefit.