Skill System Memory

Skill System Memory (PostgreSQL)

Persistent shared memory for all AI agents. PostgreSQL 14+ on Linux or Windows. Memory failures look like intelligence failures — this skill ensures the right memory is retrieved at the right time.

Contract notes:

• Module-first: mem.py and MCP tooling are the primary runtime surface.
• Plugin optional: OpenCode plugin integration is an adapter layer, not a required dependency.
• Prefer project-scoped DB naming: <project>-memory (for example skills-memory).

DB Targeting — Project Isolation

Skills are installed globally but memory is project-scoped. Isolation is at the database level: each project gets its own PostgreSQL database named <project>-memory.

Cross-DB recall is an explicit read-side feature, not a default mode. Search and context reads stay on the current memory DB unless the caller opts in with scope=all or cross_db=true. All write paths remain pinned to the local SKILL_PGDATABASE target.

Search Path Role Architecture (SK-C)

The compatible rollout path is additive:

• legacy mode remains DB-per-project via SKILL_PGDATABASE=<project>-memory
• shared-DB mode uses an explicit shared DB plus schema isolation
• runtime roles are split into admin, connector, and visitor_<identity>

Role model:

• admin: migration/init only; creates schemas, grants, and roles
• connector: CONNECT-only credential; opens the DB connection and immediately SET ROLEs
• visitor_<identity>: daily query role with enforced search_path

Visitor search_path matrix:

• visitor_hermes: ep_scope, sk_scope, fd_scope, work_scope, public
• visitor_atlas: ep_scope, public
• visitor_athena: sk_scope, public
• visitor_fd_coder: fd_scope, public
• visitor_ep_coder: ep_scope, public
• visitor_sk_coder: sk_scope, public
• visitor_hephaestion: work_scope, sk_scope, public

Shared-DB mode is explicit and backward-compatible:

• SKILL_PGSHARED_DB=<shared-db-name> selects the shared DB
• SKILL_PGSCHEMA=<scope-schema> selects the local writable schema
• SKILL_PGDATABASE remains valid for legacy DB-per-project mode

Runtime flow in shared-DB mode:

1. connect as connector
2. SET ROLE visitor_<identity>
3. rely on the visitor search_path for scoped queries
4. allow Hermes-only explicit override for all-scopes audit queries

Shared-DB examples:

# Provision shared DB schemas/roles for SK scope
python3 scripts/mem.py init-project \
  --project-id sk \
  --db-name shared-memory \
  --schema-name sk_scope \
  --shared-db-mode

# Run local SK queries through shared DB scope
SKILL_PGSHARED_DB=shared-memory \
SKILL_PGSCHEMA=sk_scope \
SKILL_VISITOR_ROLE=visitor_sk_coder \
python3 scripts/mem.py search "router policy"

# Hermes explicit all-scopes audit across the shared DB
SKILL_PGSHARED_DB=shared-memory \
SKILL_PGSCHEMA=sk_scope \
SKILL_VISITOR_ROLE=visitor_hermes \
python3 scripts/mem.py search "friction skill-system-comms" --all-scopes

Cross-DB Visibility Contract

• visibility=private (default): searchable only inside the DB where the memory lives; legacy rows without visibility read as private
• visibility=shared: searchable across configured memory DBs when federation is explicit
• visibility=global: searchable across configured memory DBs and eligible for cross-DB auto context injection
• Cross-DB results must include source_db
• Cross-DB ranking uses normalized fusion (RRF-style), not raw per-DB score comparison
• Known DB fan-out targets are configured in config/insight.yaml under memory.cross_db_targets
• All mutation paths (store, update, delete, purge) stay local to the active SKILL_PGDATABASE target even when read-side cross-DB federation is enabled

Auto-detection (preferred)

When no env vars are set, the DB name is auto-derived from the project directory name:

• Plugin (JS): uses directory passed by OpenCode host → last path segment → <dir>-memory
• mem.py CLI: uses script's root directory or cwd → <dir>-memory
• MCP server: uses Path.cwd() at startup → <dir>-memory

Do NOT set SKILL_PGDATABASE globally. It overrides auto-detection for ALL projects.

# WRONG — breaks project isolation
export SKILL_PGDATABASE=skills-memory   # every project now hits skills-memory

# RIGHT — let auto-detection work (no env var needed)
cd /path/to/FraudDetect && opencode      # → FraudDetect-memory
cd /path/to/skills && opencode           # → skills-memory

Per-project override (when needed)

If you must override, set SKILL_PGDATABASE at project level, not globally:

• In project's .opencode config or launch script
• In project's .env file (if your host reads it)
• As a prefix to the command: SKILL_PGDATABASE=FraudDetect-memory opencode

Fail-closed policy

If PGDATABASE is set but SKILL_PGDATABASE is not, all entry points refuse to connect. This prevents accidental cross-project memory pollution.

MCP server path for multi-project setups

When skills are globally installed at ~/.agents/skills/, configure MCP with the absolute path to the global install:

{
  "skill-system-memory": {
    "command": "python3",
    "args": ["~/.agents/skills/skill-system-memory/mcp/server.py"]
  }
}

Do NOT use relative paths like skills/skill-system-memory/mcp/server.py — they break in projects that don't have a local skills/ directory.

Quick Start

Database <project>-memory and all functions are created by init.sql in this skill directory. The current supported-now runtime surface covers core memory, typed context reads, the typed evolution ledger, and doctor/compaction support. Session/project/context projection tables and behavior graph refresh remain optional capability-gated surfaces, not unconditional runtime guarantees.

# Linux — replace 'postgres' with your PostgreSQL superuser if different (e.g. your system username)
psql -U postgres -c "CREATE DATABASE <project>-memory;"
psql -U postgres -d <project>-memory -f init.sql

# Windows (adjust path to your psql.exe; replace 'postgres' with your PG superuser if needed)
& "C:\Program Files\PostgreSQL\18\bin\psql.exe" -U postgres -c "CREATE DATABASE <project>-memory;"
& "C:\Program Files\PostgreSQL\18\bin\psql.exe" -U postgres -d <project>-memory -f init.sql

Note: If your PostgreSQL installation does not have a postgres role, use your actual PostgreSQL superuser name. On many Linux distros this matches your OS username. You can override at any time by setting PGUSER before running scripts: export PGUSER=your_pg_username (Linux/macOS) or $env:PGUSER = "your_pg_username" (PowerShell).

Verify: SELECT * FROM memory_health_check();

MCP Server (Phase 6.1)

This skill includes an MCP (Model Context Protocol) server that exposes memory operations as agent tools.

MCP Server Setup

# Install dependencies
cd skills/skill-system-memory/mcp
pip install -r requirements.txt

# Verify installation
python server.py --help

# Run with stdio transport (default)
python server.py

# Run with SSE transport (for remote access)
MCP_TRANSPORT=sse MCP_PORT=8000 python server.py

# Or via MCP CLI
mcp dev server.py

MCP Environment Variables

MCP Tools

MCP Resources

MCP Agent Integration

To use the MCP server with an agent, configure the agent's MCP settings to include:

{
  "mcpServers": {
    "skill-system": {
      "command": "python",
      "args": ["skills/skill-system-memory/mcp/server.py"],
      "env": {
        "SKILL_PGDATABASE": "<project>-memory"
      }
    }
  }
}

Shared-DB MCP example:

{
  "mcpServers": {
    "skill-system": {
      "command": "python",
      "args": ["skills/skill-system-memory/mcp/server.py"],
      "env": {
        "SKILL_PGSHARED_DB": "shared-memory",
        "SKILL_PGSCHEMA": "sk_scope",
        "SKILL_VISITOR_ROLE": "visitor_sk_coder"
      }
    }
  }
}

MCP Tool Examples

# Search memories
mcp_tool: memory_search
args: { "query": "pgvector installation", "limit": 5 }

# Explicit cross-DB search
mcp_tool: memory_search
args: { "query": "reviewer feedback", "cross_db": true, "limit": 5 }

# Hermes all-scopes override in shared-DB mode
mcp_tool: memory_search
args: { "query": "cross-project audit", "all_scopes": true, "limit": 10 }

# Store a memory
mcp_tool: memory_store
args: { "type": "semantic", "category": "troubleshooting", "title": "Fixed pgvector on Windows", "content": "Build steps...", "importance": 8 }

# Update a memory locally
mcp_tool: memory_update
args: { "id": 42, "fields": { "title": "Updated title", "visibility": "shared" } }

# Soft-delete a memory locally
mcp_tool: memory_delete
args: { "id": 42 }

# Hard-delete a memory locally only with explicit authorization
mcp_tool: memory_purge
args: { "id": 42, "hard": true, "authorized": true }

# Auto-match memories (KEY TOOL for near-automatic surfacing)
mcp_tool: memory_auto_match
args: { "text": "I'm seeing an error with database connections", "threshold": 0.8, "limit": 5 }

# Get user context
mcp_tool: memory_context
args: { "user": "default" }

# Check system health
mcp_tool: memory_status
args: {}

For existing installations, also run:

psql -U postgres -d <project>-memory -v -f migrate-v2-postgres-first.sql

Pure Skill Mode (default)

This skill works without installing any plugin. In pure skill mode:

• you manually run scripts when you want (progressive disclosure)
• no global OpenCode config is modified automatically

Optional bootstrap (asks + records choices + tries to install)

Notes:

• Interactive mode defaults to NOT installing heavy optional components.
• Use -InstallAll / --install-all only when you're ready to install everything.

Run the bootstrap script to choose optional components (pgpass, local embeddings, pgvector) and record decisions.

Bootstrap can also optionally install the OpenCode compaction logging plugin (it will copy the plugin into your OpenCode plugins directory).

Windows:

# run from the skill directory
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "scripts\bootstrap.ps1"

Linux/macOS:

# run from the skill directory
bash "scripts/bootstrap.sh"

The selection record is stored at:

• ~/.config/opencode/skill-system-memory/setup.json

Agent rule:

• If this file does not exist, ask the user if they want to enable optional components.
• Recommended: run bootstrap with all options enabled (then fix any failures it reports).

Runtime Capability Model

Current live capability classifications are intentionally narrow:

When the durable evolution proposal model is present, doctor also reports:

• canonical accepted store: evolution_nodes
• canonical rejected store: evolution_rejections
• canonical task materialization: evolution_tasks -> agent_tasks
• role of agent_memories: searchable summary/backref surface with explicit legacy compatibility rows
• approve/reject path status: transaction-safe at the CLI boundary
• migration/backfill idempotence: rerun-safe
• decision replay status: replay-idempotent by proposal_id
• coordination mechanism: advisory lock by proposal + semantic fingerprint on canonical accepted/rejected rows
• payload mismatch policy: blocked with no mutation
• conflicting terminal policy: blocked with no mutation
• task authority model: agent_tasks lifecycle authority, evolution_tasks mapping only

In that state, evolution_snapshots is treated as LEGACY_COMPAT_SURFACE for proposal durability, while remaining active for snapshot/version history.

On Windows, pgvector installation follows the official pgvector instructions (Visual Studio C++ + nmake /F Makefile.win). The bootstrap will attempt to install prerequisites via winget.

Optional automation: compaction logging (OpenCode plugin)

If you want automatic compaction logging, install the OpenCode plugin template shipped with this skill.

Option A (recommended): run bootstrap and choose the plugin option.

1. Copy plugins/skill-system-memory.js and plugins/runtime_sync.js to ~/.config/opencode/plugins/
2. Restart OpenCode

Safety / rollback (if OpenCode gets stuck on startup):

• Remove or rename ~/.config/opencode/plugins/skill-system-memory.js
• Remove or rename ~/.config/opencode/plugins/runtime_sync.js
• Restart OpenCode
• Check logs:
  • macOS/Linux: ~/.local/share/opencode/log/
  • Windows: %USERPROFILE%\.local\share\opencode\log

Plugin behavior notes:

• The plugin is designed to be a no-op unless you explicitly enabled it via bootstrap (setup.json sets selected.opencode_plugin=true).
• It only attempts a Postgres write if selected.pgpass=true (avoids hanging on auth prompts).
• On compaction, it always keeps local JSONL logging and the core store_memory(...) path available when the DB target is valid.
• Runtime projection upserts (session_summaries, project_summaries, context_rollups) are capability-gated: if those tables are absent, the plugin skips them with an explicit surfaced reason instead of attempting writes.
• Behavior graph refresh into behavior_* tables is also capability-gated and is skipped explicitly when the required tables are absent.

Uninstall:

• Remove ~/.config/opencode/plugins/skill-system-memory.js
• Remove ~/.config/opencode/plugins/runtime_sync.js
• Restart OpenCode

Credentials (psql)

Do NOT hardcode passwords in scripts, skill docs, or config files.

Recommended options for non-interactive psql:

• .pgpass / pgpass.conf (recommended)
  • Linux/macOS: ~/.pgpass (must be chmod 0600 ~/.pgpass or libpq will ignore it)
  • Windows: %APPDATA%\postgresql\pgpass.conf (example: C:\Users\<you>\AppData\Roaming\postgresql\pgpass.conf)
  • Format: hostname:port:database:username:password
  • Docs: https://www.postgresql.org/docs/current/libpq-pgpass.html
• PGPASSFILE (optional override): point to a custom location for the password file
• PGPASSWORD (not recommended): only for quick local testing; environment variables can leak on some systems
  • Docs: https://www.postgresql.org/docs/current/libpq-envars.html

Tip: set connection defaults once (per shell) to shorten commands:

export PGHOST=localhost
export PGPORT=5432
export SKILL_PGDATABASE=<project>-memory
export PGUSER=postgres   # change to your PG superuser if postgres role does not exist

Shell copy/paste safety:

• Avoid copying inline markdown backticks (e.g. semantic) into your shell. In zsh, backticks are command substitution.
• Prefer using the wrapper scripts (scripts/mem.sh, scripts/mem.ps1) or copy from fenced code blocks.

One-time setup helper scripts

This skill ships helper scripts (relative paths):

• scripts/setup-pgpass.ps1
• scripts/setup-pgpass.sh

OpenCode usage: run them from the skill directory.

Windows run:

powershell.exe -NoProfile -ExecutionPolicy Bypass -File "scripts\setup-pgpass.ps1"

Linux/macOS run:

bash "scripts/setup-pgpass.sh"

Memory Types

Core Functions

store_memory(type, category, tags[], title, content, metadata, agent_id, session_id, importance)

Auto-deduplicates by content hash. Duplicate inserts bump access_count and importance_score.

SELECT store_memory(
    'semantic',
    'windows-networking',
    ARRAY['ssh', 'tunnel', 'port-conflict'],
    'SSH Tunnel Port Conflict Resolution',
    'Fix: 1) taskkill /F /IM ssh.exe  2) Use processId not pid  3) Wait 3s',
    '{"os": "Windows 11"}',
    'sisyphus',
    NULL,
    9.0
);

Wrapper: scripts/mem.py (推薦 — parameterized query，無 quoting 問題)

Requirements: pip install psycopg2-binary

# DB 連線狀態 + 記憶總數
python3 scripts/mem.py status

# 搜尋記憶（含特殊字元也安全）
python3 scripts/mem.py search "pgvector windows install" --limit 5
python3 scripts/mem.py search "fraud features" --scope project --limit 10
python3 scripts/mem.py search "reviewer feedback" --cross-db --limit 10
python3 scripts/mem.py search "shared debugging notes" --scope all --limit 10

# 跨 5 維度診斷搜尋 (B-015 TKT-001)
python3 scripts/mem.py search "alert file" --diagnostic --limit 10

# Tag 前綴自動展開 (B-015 TKT-003)
python3 scripts/mem.py search "root-cause" --auto-expand

# 儲存記憶（--content flag）
python3 scripts/mem.py store --type semantic --category project --title "pgvector install" --tags "postgres,pgvector,windows" --importance 8 --scope project --content "Steps: ..."
python3 scripts/mem.py store --type semantic --category feedback --title "Reviewer note" --visibility shared --content "Cross-DB searchable note"
python3 scripts/mem.py update 42 --field title="Updated note" --field visibility=shared
python3 scripts/mem.py delete 42
python3 scripts/mem.py purge 42 --hard --authorized

# 儲存記憶（stdin）
printf '%s' "Steps: ..." | python3 scripts/mem.py store semantic project "pgvector install" "postgres,pgvector,windows" 8 --scope session

# 列出 / 匯出 / 壓縮
python3 scripts/mem.py list --scope project --limit 20
python3 scripts/mem.py export --format json --scope global
python3 scripts/mem.py compact --scope project

# session 開頭自動撈出相關記憶
python3 scripts/mem.py context "pgvector ssh tunnel"

# 列出所有已使用的 tags / categories
python3 scripts/mem.py tags
python3 scripts/mem.py categories

# DB 不可用時，將 pending fallback 寫回 DB
python3 scripts/mem.py flush

# 保守升級 visibility（只影響本地 DB）
python3 scripts/mem.py visibility-upgrade --visibility shared --category feedback --dry-run
python3 scripts/mem.py visibility-upgrade --visibility global --tags reviewer,decision

Fallback behavior:

• DB 連線失敗時，store 會降級寫入 .memory/pending/*.json，search / context 會讀本地 pending 檔案並印出警告
• list / export 也會從 pending fallback 讀取；compact 在 DB 不可用時只印 warning 不崩潰
• DB 可連線但 memory schema 缺失時，store 會 warning + no-op，search / context / tags / categories 會回空結果 + warning
• flush 會在 DB 恢復後將 .memory/pending/ 內容寫回 PostgreSQL

Scope model:

• session (default): 記錄在 metadata.scope=session，並盡量帶上 session_id
• project: 記錄在 metadata.scope=project + metadata.project_id=<repo>
• global: 記錄在 metadata.scope=global，仍需明確 federated read 才會跨 DB 查詢
• search / list / export 不帶 --scope 時，會回傳所有 scope 並在結果中標記來源 scope

Visibility is distinct from scope:

• private (default): local DB only
• shared: eligible for explicit cross-DB search
• global: eligible for explicit cross-DB search and auto context injection

Mutation audit rules:

• update writes selected field changes only, in the local DB only
• delete is soft-delete only (deleted_at), preserving backward-compatible reads
• purge is hard delete and must be explicitly authorized
• every mutation writes a memory_audit row with old/new values plus actor metadata

CLI mutation examples:

python3 scripts/mem.py update 42 --field title="Updated note" --field visibility=shared
python3 scripts/mem.py delete 42
python3 scripts/mem.py purge 42 --hard --authorized

memory_audit is the canonical mutation trail for these operations. Each row records:

• memory_id
• action (update, delete, purge)
• old_value
• new_value
• actor_id
• session_id
• changed_at

Warning: purge is destructive and bypasses soft-delete recovery. It stays local-only and requires explicit authorization flags; do not use it for routine cleanup.

Known federated read targets are configured in config/insight.yaml:

memory:
  cross_db_targets:
    - Work-memory
    - skills-memory
    - ExperimentPipeline-memory
    - FraudDetect-memory
  cross_db_context_visibility:
    - global

This list controls explicit read fan-out only. Write paths still target the local SKILL_PGDATABASE database even if a caller passes target_db-style data.

Wrapper: scripts/mem.sh / scripts/mem.ps1 (shell fallback)

# 連線狀態
bash "scripts/mem.sh" status

# 搜尋
bash "scripts/mem.sh" search "pgvector windows install" 5

# 儲存 (content via stdin)
printf '%s' "Steps: ..." | bash "scripts/mem.sh" store semantic project "pgvector install" "postgres,pgvector,windows" 8

# 列出 tags / categories
bash "scripts/mem.sh" tags
bash "scripts/mem.sh" categories

powershell.exe -NoProfile -ExecutionPolicy Bypass -File "scripts\mem.ps1" types
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "scripts\mem.ps1" search "pgvector windows install" 5
"Steps: ..." | powershell.exe -NoProfile -ExecutionPolicy Bypass -File "scripts\mem.ps1" store semantic project "pgvector install" "postgres,pgvector,windows" 8

Router Integration (optional)

If you use a Router skill that executes pinned pipelines, it can read a manifest embedded in this SKILL.md.

For portability, the manifest block is fenced as YAML but the content is JSON (valid YAML). The Router parses it.

{
  "schema_version": "2.0",
  "id": "skill-system-memory",
  "version": "0.7.0",
  "capabilities": ["memory-search", "memory-search-cross-db", "memory-role-scope", "memory-store", "memory-update", "memory-delete", "memory-purge", "memory-visibility-upgrade", "memory-list", "memory-compact", "memory-export", "memory-export-md", "memory-flush", "memory-health", "memory-types", "memory-auto-write", "memory-doctor", "plugin-debug", "plugin-eda", "plugin-gate", "memory-rebuild"],
  "effects": ["proc.exec", "db.read", "db.write"],
  "operations": {
    "search": {
      "description": "Search memories by natural language query. Returns ranked results with relevance scores.",
      "input": {
        "query": { "type": "string", "required": true, "description": "Natural language search query" },
        "limit": { "type": "integer", "required": false, "default": 5, "description": "Max results" },
        "scope": { "type": "string", "required": false, "description": "global | project | session filter; all enables explicit cross-DB federation" },
        "cross_db": { "type": "boolean", "required": false, "description": "Explicitly fan out reads across configured memory DBs" },
        "all_scopes": { "type": "boolean", "required": false, "description": "Hermes/admin override to bypass visitor search_path scoping for cross-project audit" }
      },
      "output": {
        "description": "Array of memory matches with local scope labels and cross-DB source attribution when federation is enabled",
        "fields": { "status": "ok | error", "data": "array of {id, title, content, relevance_score, scope, source_db?}" }
      },
      "entrypoints": {
        "unix": ["bash", "scripts/router_mem.sh", "search", "{query}", "{limit}"],
        "windows": ["powershell.exe", "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", "scripts\\router_mem.ps1", "search", "{query}", "{limit}"]
      }
    },
    "store": {
      "description": "Store a new memory. Auto-deduplicates by content hash and always writes to the local memory DB.",
      "input": {
        "memory_type": { "type": "string", "required": true, "description": "One of: semantic, episodic, procedural, working" },
        "category": { "type": "string", "required": true, "description": "Category name" },
        "title": { "type": "string", "required": true, "description": "One-line summary" },
        "tags_csv": { "type": "string", "required": true, "description": "Comma-separated tags" },
        "importance": { "type": "integer", "required": true, "description": "1-10 importance score" },
        "scope": { "type": "string", "required": false, "description": "global | project | session" },
        "visibility": { "type": "string", "required": false, "description": "private | shared | global" }
      },
      "output": {
        "description": "Confirmation with stored memory id",
        "fields": { "status": "ok | error", "id": "integer" }
      },
      "entrypoints": {
        "unix": ["bash", "scripts/router_mem.sh", "store", "{memory_type}", "{category}", "{title}", "{tags_csv}", "{importance}"],
        "windows": ["powershell.exe", "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", "scripts\\router_mem.ps1", "store", "{memory_type}", "{category}", "{title}", "{tags_csv}", "{importance}"]
      }
    },
    "update": {
      "description": "Update selected fields of a local memory row by id with audit logging.",
      "input": {
        "id": { "type": "integer", "required": true },
        "fields": { "type": "object", "required": true }
      },
      "output": {
        "description": "Update summary",
        "fields": { "status": "ok | error", "id": "integer", "updated_fields": "array" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "update"],
        "windows": ["python", "scripts/mem.py", "update"]
      }
    },
    "delete": {
      "description": "Soft-delete a local memory row by id with audit logging.",
      "input": {
        "id": { "type": "integer", "required": true }
      },
      "output": {
        "description": "Soft-delete summary",
        "fields": { "status": "ok | error", "id": "integer", "deleted_at": "string" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "delete"],
        "windows": ["python", "scripts/mem.py", "delete"]
      }
    },
    "purge": {
      "description": "Hard-delete a local memory row only with explicit authorization.",
      "input": {
        "id": { "type": "integer", "required": true },
        "hard": { "type": "boolean", "required": true },
        "authorized": { "type": "boolean", "required": true }
      },
      "output": {
        "description": "Hard-delete summary",
        "fields": { "status": "ok | error", "id": "integer", "purged": "boolean" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "purge"],
        "windows": ["python", "scripts/mem.py", "purge"]
      }
    },
    "list": {
      "description": "List memories with optional scope/category filters.",
      "input": {
        "scope": { "type": "string", "required": false },
        "category": { "type": "string", "required": false },
        "limit": { "type": "integer", "required": false, "default": 20 }
      },
      "output": {
        "description": "Memory listing with scope labels",
        "fields": { "status": "ok | error", "results": "array" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "list"],
        "windows": ["python", "scripts/mem.py", "list"]
      }
    },
    "compact": {
      "description": "Compact duplicate memories within an optional scope.",
      "input": {
        "scope": { "type": "string", "required": false }
      },
      "output": {
        "description": "Compaction result",
        "fields": { "status": "ok | warn | error", "compacted_count": "integer" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "compact"],
        "windows": ["python", "scripts/mem.py", "compact"]
      }
    },
    "export": {
      "description": "Export memories in json or csv format.",
      "input": {
        "format": { "type": "string", "required": false },
        "scope": { "type": "string", "required": false },
        "limit": { "type": "integer", "required": false, "default": 1000 }
      },
      "output": {
        "description": "Export payload",
        "fields": { "status": "ok | error", "results": "array|string" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "export"],
        "windows": ["python", "scripts/mem.py", "export"]
      }
    },
    "export-md": {
      "description": "Export DB-only memories to markdown files that memory_sync can re-import.",
      "input": {
        "folder": { "type": "string", "required": true, "description": "Target folder for markdown exports" }
      },
      "output": {
        "description": "Markdown export result",
        "fields": { "status": "ok | error", "created": "integer", "skipped": "integer", "folder": "string" }
      },
      "entrypoints": {
        "agent": "Use the memory_export MCP tool from skills/skill-system-memory/mcp/server.py"
      }
    },
    "visibility-upgrade": {
      "description": "Batch-upgrade local memory visibility conservatively by category and/or tag selectors.",
      "input": {
        "visibility": { "type": "string", "required": true, "description": "shared | global | private" },
        "category": { "type": "string", "required": false },
        "tags": { "type": "string", "required": false },
        "dry_run": { "type": "boolean", "required": false, "default": false }
      },
      "output": {
        "description": "Upgrade summary",
        "fields": { "status": "ok | error", "matched": "integer", "updated": "integer" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "visibility-upgrade"],
        "windows": ["python", "scripts/mem.py", "visibility-upgrade"]
      }
    },
    "flush": {
      "description": "Flush local pending fallback files into PostgreSQL when the DB is back.",
      "input": {},
      "output": {
        "description": "Confirmation that pending fallback files were replayed",
        "fields": { "status": "ok | warn | error", "flushed_count": "integer" }
      },
      "entrypoints": {
        "unix": ["python3", "scripts/mem.py", "flush"],
        "windows": ["python", "scripts/mem.py", "flush"]
      }
    },
    "health": {
      "description": "Check memory system health: total count, average importance, stale count.",
      "input": {},
      "output": {
        "description": "Health metrics",
        "fields": { "status": "ok | error", "data": "array of {metric, value, status}" }
      },
      "entrypoints": {
        "unix": ["bash", "scripts/router_mem.sh", "health"],
        "windows": ["powershell.exe", "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", "scripts\\router_mem.ps1", "health"]
      }
    },
    "types": {
      "description": "List available memory types and their descriptions.",
      "input": {},
      "output": {
        "description": "Memory type definitions",
        "fields": { "status": "ok | error", "data": "array of {type, lifespan, description}" }
      },
      "entrypoints": {
        "unix": ["bash", "scripts/router_mem.sh", "types"],
        "windows": ["powershell.exe", "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", "scripts\\router_mem.ps1", "types"]
      }
    },
    "auto-write": {
      "description": "Procedure template for automatically storing a memory after solving a non-obvious problem.",
      "input": {},
      "output": {
        "description": "Proposed memory fields to store",
        "fields": {"memory_type": "string", "category": "string", "title": "string", "tags_csv": "string", "importance": "integer", "content": "string"}
      },
      "entrypoints": {
        "agent": "Follow scripts/auto-write-template.md"
      }
    },
    "doctor": {
      "description": "Report the current runtime capability map, memory plugin drift, memory DB target, OMO resolution, canonical evolution stores, table/routine readiness, and evolution ledger status.",
      "input": {},
      "output": {
        "description": "Doctor report for the current runtime state",
        "fields": {"status": "ok | warn | error", "doctor_report": "object"}
      },
      "entrypoints": {
        "unix": ["python3", "scripts/runtime_doctor.py", "--format", "json"],
        "windows": ["python", "scripts/runtime_doctor.py", "--format", "json"]
      }
    },
    "debug-diagnose": {
      "description": "Expose skill-system-debug diagnose through the unified MCP server.",
      "input": {
        "symptom": { "type": "string", "required": true },
        "context": { "type": "string", "required": false }
      },
      "output": {
        "description": "Structured diagnosis payload",
        "fields": { "status": "ok | error", "result": "object" }
      },
      "entrypoints": {
        "agent": "Use the debug_diagnose MCP tool from skills/skill-system-memory/mcp/server.py"
      }
    },
    "eda-profile": {
      "description": "Expose skill-system-eda dataset profiling through the unified MCP server.",
      "input": {
        "input_path": { "type": "string", "required": true },
        "sample": { "type": "integer", "required": false },
        "target": { "type": "string", "required": false }
      },
      "output": {
        "description": "EDA profile output",
        "fields": { "status": "ok | error", "profile_path": "string", "report_path": "string" }
      },
      "entrypoints": {
        "agent": "Use the eda_profile MCP tool from skills/skill-system-memory/mcp/server.py"
      }
    },
    "gate-validate": {
      "description": "Expose skill-system-gate validation through the unified MCP server.",
      "input": {
        "rules": { "type": "string", "required": false },
        "artifact_root": { "type": "string", "required": false }
      },
      "output": {
        "description": "Gate validation summary",
        "fields": { "status": "ok | error", "passed": "boolean", "results": "array" }
      },
      "entrypoints": {
        "agent": "Use the gate_validate MCP tool from skills/skill-system-memory/mcp/server.py"
      }
    },
    "rebuild": {
      "description": "Rebuild full agent context after compact or memory loss.",
      "input": {
        "user": { "type": "string", "required": false },
        "recent_limit": { "type": "integer", "required": false }
      },
      "output": {
        "description": "Soul, preferences, recent memories, and pending decisions",
        "fields": { "status": "ok | error", "rebuild_context": "object" }
      },
      "entrypoints": {
        "agent": "Use the memory_rebuild MCP tool from skills/skill-system-memory/mcp/server.py"
      }
    }
  },
  "stdout_contract": {
    "last_line_json": true
  },
  "mcp": {
    "server": {
      "name": "skill-system",
      "description": "Unified skill-system MCP server exposing memory, debug, eda, gate, and memory_rebuild tools",
      "entrypoint": "python skills/skill-system-memory/mcp/server.py",
      "transport": "stdio",
      "tools": ["memory_search", "memory_store", "memory_auto_match", "memory_context", "memory_status", "memory_sync", "memory_export", "debug_diagnose", "eda_profile", "gate_validate", "memory_rebuild"],
      "resources": ["memory://schema", "memory://status"]
    }
  }
}

Notes:

• The Router expects each step to print last-line JSON.
• These Router adapter scripts are separate from mem.sh / mem.ps1 to avoid breaking existing workflows.

Visualize Memories (Markdown export)

If querying PostgreSQL is too inconvenient for daily use, you can export memories into markdown files under ./Memory/ (current directory by default):

bash "<skill-dir>/scripts/sync_memory_to_md.sh" --out-dir "./Memory"

Outputs:

• Memory/Long.md (semantic + procedural)
• Memory/Procedural.md (procedural only)
• Memory/Short.md (friction + compaction-daily + procedural highlights)
• Memory/Episodic.md (episodic)

Backups:

• Backups are stored under Memory/.backups/ to avoid noisy git status.
• Use --no-backup to disable.

The sync script will also create Memory/.gitignore if it doesn't exist (ignores .backups/ and SYNC_STATUS.txt).

Long index:

• Memory/Long.md includes an Index section (top categories + tags) to make the export browsable.

search_memories(query, types[], categories[], tags[], agent_id, min_importance, limit)

Hybrid search: full-text (tsvector) + trigram similarity (pg_trgm) + tag filtering. Accepts plain English queries — no tsquery syntax needed. Relevance scoring: text_score * decay * recency * importance.

-- Natural language
SELECT * FROM search_memories('ssh tunnel port conflict', NULL, NULL, NULL, NULL, 7.0, 5);

-- Filter by type + tags
SELECT * FROM search_memories(
    'troubleshooting steps',
    ARRAY['procedural']::memory_type[],
    NULL,
    ARRAY['ssh'],
    NULL, 0.0, 5
);

Returns: id, memory_type, category, title, content, importance_score, relevance_score, match_type Where match_type is one of: fulltext, trigram_title, trigram_content, metadata.

memory_health_check()

Returns: metric | value | status for total_memories, avg_importance, stale_count.

apply_memory_decay()

Decays episodic memories by 0.9999^days_since_access. Run daily.

prune_stale_memories(age_days, max_importance, max_access_count)

Soft-deletes old episodic memories below thresholds. Default: 180 days, importance <= 3, never accessed.

Agent Workflow

Auto-Write Template

After fixing a bug or solving a non-obvious problem, store a memory using the standard template:

• Procedure: scripts/auto-write-template.md

最佳實踐：優先使用 mem.py wrapper，避免 shell quoting 與認證問題。 Raw SQL 範例請見 Appendix: Raw SQL。

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