ok add this to memory however is best. its lots of info on my taste in anime, what i saw, what i got recommended

0) Executive summary (ranked, minimal friction)
Start here (highest probability, shortest path to payoff):
    1    Akudama Drive — 12 eps, fast, conclusive, Edgerunners × Chainsaw Man
    2    Vivy: Fluorite Eye's Song — 13 eps, tight arcs, high production
    3    Dororo — 24 eps, emotional + dark, steady progression
Then (very strong, slight conditions):
4. Cowboy Bebop — if Champloo clicks
5. Made in Abyss — if you’re fine with dark turns
6. 86 — if you want emotion without drift
Resume (you stopped too early):
    •    Samurai Champloo → watch to ~ep 8, then decide
    •    Attack on Titan → S2+ is a different show
    •    Demon Slayer: Kimetsu no Yaiba → jump to Entertainment District

1) Your taste profile (derived)

flowchart LR
A[Likes] --> B[Dark / existential tone]
A --> C[High production / style]
A --> D[Clear structure & payoff]
A --> E[Finite length]

F[Dislikes] --> G[Endless shonen sprawl]
F --> H[Slow starts without payoff]
F --> I[Filler / low signal density]

B & C & D & E --> X[Sweet spot: short, stylish, conclusive with emotional weight]

Interpretation
    •    You tolerate philosophy (Devilman, Parasyte, Frieren) if momentum exists.
    •    You strongly reward signal density per episode.
    •    You prefer contained arcs or complete runs over open-ended franchises.

2) Global map (watched + recommendations)

quadrantChart
    title Preference Map
    x-axis Emotional / Philosophical --> Stylish / Fast
    y-axis Structured / Tight --> Dark / Existential

    "Devilman Crybaby": [0.35, 0.95]
    "Parasyte": [0.45, 0.85]
    "Chainsaw Man": [0.75, 0.8]
    "Cyberpunk Edgerunners": [0.85, 0.6]
    "FMAB": [0.4, 0.4]
    "Samurai Champloo": [0.75, 0.45]
    "Frieren": [0.2, 0.3]

    "Akudama Drive": [0.9, 0.7]
    "Vivy": [0.8, 0.5]
    "Dororo": [0.35, 0.75]
    "Death Parade": [0.2, 0.6]
    "Made in Abyss": [0.45, 0.8]
    "86": [0.55, 0.65]
    "Cowboy Bebop": [0.7, 0.55]

Dropped / paused series
Attack on Titan
    •    Stopped: end of Season 1
    •    Exact point: Episode 25 — “Wall”
    •    Where that is narratively: right after the Female Titan arc resolution
    •    Important: you stopped before the show’s tone/structure shift

Demon Slayer: Kimetsu no Yaiba
    •    Stopped: unclear, but likely:
    ◦    early Season 1 (~episodes 5–10), or
    ◦    right before Mugen Train / Entertainment District
    •    Heuristic (based on common drop):
    ◦    If you remember training + early demons → ep ~6–8
    ◦    If you remember group dynamic fully formed → ep ~15–19

Samurai Champloo
    •    Stopped: ~Episode 3
    •    Exact candidates:
    ◦    Ep 1 — intro (unlikely)
    ◦    Ep 2 — baseball episode
    ◦    Ep 3 — Jin-focused episode
    •    Where that is: still in pure episodic phase (before emotional weight)

Frieren: Beyond Journey's End
    •    Stopped: early (likely Episodes 3–6)
    •    Where that is:
    ◦    post-intro arc
    ◦    before the exam arc / more structured progression
    •    Reason aligned with your taste: pacing still too slow, low urgency

Completed (for reference baseline)
    •    Devilman Crybaby
    •    Parasyte: The Maxim
    •    Cyberpunk: Edgerunners
    •    Fullmetal Alchemist: Brotherhood


**Storing memory details**

I need to save some memory, and I might want to use the skill for memory-writing or reading files, as the instructions suggest. I should check the existing memory since the user mentioned adding to it. I may need to work with the Agents Database API using a bash curl. Loading the memory-writing skill seems crucial, and I’m wondering if reading the memory might be unnecessary. Time to take a look at the skill again!


---
name: memory-write
description: Save a durable high-signal memory into agents-database
---

# memory-write

## Use When

- new information is likely useful beyond current chat or task
- saving a decision, preference, project fact, reusable source/artifact, or handoff summary
- user asks to remember something durable

## Do Not Use When

- content is trivial, redundant, ephemeral, or likely useless later
- content is secret unless user explicitly asked to store it

## Required Workflow

1. If useful context may already exist, optionally check first:
   - `curl -fsSL "${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10"`
2. Choose best memory type:
   - `profile`: stable user preference, priority, recurring constraint
   - `decision`: chosen approach or operating rule
   - `project`: durable project fact or invariant
   - `artifact`: reusable source, claim, reference, output
   - `episode`: concise session summary, blocked state, handoff context
3. Build a concise payload with:
   - required: `type`, `scope`, `title`, `content`
   - recommended when known: `subtype`, `project_id`, `repo_id`, `task_id`, `run_id`, `source_ref`, `origin_agent`, `url`, `domain`, `metadata`
4. Save it with the helper:
   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title "<title>" --content "<content>"`
5. Confirm the created memory id and what was stored.

## Quick Examples

```bash
~/.agents/skills/memory-write/memory-write \
  --type profile \
  --subtype working_preference \
  --scope global \
  --title "User prefers concise status updates" \
  --content "Sebas prefers short, direct updates with business impact first." \
  --source-ref "conversation:2026-04-17" \
  --origin-agent personal-agent

~/.agents/skills/memory-write/memory-write \
  --type decision \
  --subtype operating_rule \
  --scope global \
  --title "Agents should save durable high-signal memory opportunistically" \
  --content "Treat agents-database as shared durable memory, not only search." \
  --metadata '{"source":"conversation"}'
```

## Rules

- prefer one strong memory over many weak ones
- keep `title` short and searchable
- keep `content` factual and compact
- use `episode` for summaries, not for every chat turn
- include `url` and `domain` for web-derived artifacts when known
- include `project_id`, `repo_id`, `task_id`, and `run_id` when context exists


bash

~/.agents/skills/memory-write/memory-write --type profile --subtype entertainment_preference --scope global --title "Sebas anime taste and watch state" --content $'Anime taste: prefers dark/existential tone, high production/style, clear structure/payoff, finite length, high signal density, contained arcs or complete runs. Dislikes endless shonen sprawl, slow starts without payoff, filler, low-density episodes. Tolerates philosophy if momentum exists. Sweet spot: short, stylish, conclusive series with emotional weight. Completed baseline: Devilman Crybaby, Parasyte: The Maxim, Cyberpunk: Edgerunners, Fullmetal Alchemist: Brotherhood. Paused/dropped: Attack on Titan at S1E25/"Wall" before major tone-structure shift; Demon Slayer unclear, likely early S1 or before Mugen Train/Entertainment District; Samurai Champloo around episode 3 in early episodic phase; Frieren early around episodes 3-6 before exam arc/structured progression. Best next recs ranked: 1) Akudama Drive 2) Vivy: Fluorite Eye\'s Song 3) Dororo 4) Cowboy Bebop if Champloo clicks 5) Made in Abyss if OK with dark turns 6) 86 if wants emotion without drift. Best resumes: Samurai Champloo to around episode 8 then decide; Attack on Titan continue into S2+; Demon Slayer jump to Entertainment District.' --source-ref "conversation:2026-04-23" --origin-agent personal-agent


Traceback (most recent call last):
  File "/home/sebas/.agents/skills/memory-write/memory-write", line 86, in <module>
    main()
    ~~~~^^
  File "/home/sebas/.agents/skills/memory-write/memory-write", line 80, in main
    with urllib.request.urlopen(req, timeout=30) as r:
         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 189, in urlopen
    return opener.open(url, data, timeout)
           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 495, in open
    response = meth(req, response)
  File "/usr/lib/python3.13/urllib/request.py", line 604, in http_response
    response = self.parent.error(
        'http', request, response, code, msg, hdrs)
  File "/usr/lib/python3.13/urllib/request.py", line 533, in error
    return self._call_chain(*args)
           ~~~~~~~~~~~~~~~~^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 466, in _call_chain
    result = func(*args)
  File "/usr/lib/python3.13/urllib/request.py", line 613, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 500: table memories has no column named schema_version

Command exited with code 1


**Inspecting API usage**

I need to check if the direct API is working since the helper might be broken. I guess I should inspect the status or look up the API documentation? Using curl is an option I can explore. It seems I might need to understand the /api/ingest format better. Let’s take a closer look at the helper script and see if I can figure out the payload. I’ll dig into it!


#!/usr/bin/env python3
import argparse
import json
import sys
import urllib.request

def read_content(args):
    if args.content is not None:
        return args.content
    if args.content_file:
        with open(args.content_file, 'r', encoding='utf-8') as f:
            return f.read()
    if not sys.stdin.isatty():
        return sys.stdin.read()
    raise SystemExit('need --content, --content-file, or stdin')

def main():
    p = argparse.ArgumentParser(description='Write memory to agents-database')
    p.add_argument('--api', default=None)
    p.add_argument('--id')
    p.add_argument('--type', required=True)
    p.add_argument('--subtype')
    p.add_argument('--scope', required=True)
    p.add_argument('--status', default='active')
    p.add_argument('--source-kind', default='manual')
    p.add_argument('--title', required=True)
    p.add_argument('--content')
    p.add_argument('--content-file')
    p.add_argument('--summary')
    p.add_argument('--project-id')
    p.add_argument('--repo-id')
    p.add_argument('--task-id')
    p.add_argument('--run-id')
    p.add_argument('--source-ref')
    p.add_argument('--evidence-ref')
    p.add_argument('--origin-agent')
    p.add_argument('--url')
    p.add_argument('--domain')
    p.add_argument('--metadata', help='JSON object string')
    args = p.parse_args()

    content = read_content(args)
    metadata = json.loads(args.metadata) if args.metadata else None
    api = args.api or 'http://100.116.176.16:8091'
    payload = {
        'type': args.type,
        'scope': args.scope,
        'status': args.status,
        'source_kind': args.source_kind,
        'title': args.title,
        'content': content,
    }
    optional = {
        'id': args.id,
        'subtype': args.subtype,
        'summary': args.summary,
        'project_id': args.project_id,
        'repo_id': args.repo_id,
        'task_id': args.task_id,
        'run_id': args.run_id,
        'source_ref': args.source_ref,
        'evidence_ref': args.evidence_ref,
        'origin_agent': args.origin_agent,
        'url': args.url,
        'domain': args.domain,
        'metadata': metadata,
    }
    for k, v in optional.items():
        if v is not None:
            payload[k] = v

    req = urllib.request.Request(
        api.rstrip('/') + '/api/ingest',
        data=json.dumps(payload).encode('utf-8'),
        headers={'Content-Type': 'application/json'},
        method='POST',
    )
    with urllib.request.urlopen(req, timeout=30) as r:
        body = r.read().decode('utf-8')
        print(body)

if __name__ == '__main__':
    main()


**Checking API status**

I need to inspect the API, maybe look at the status or documentation. I’m considering using curl to see what hints the response might provide. It seems like gathering more details about the API's current state will be useful. I’ll try to figure out any possible issues that could arise and see how I can interpret the response correctly. This approach could help clarify what needs to be addressed.

python - <<'PY'
import urllib.request, json
for path in ['/api/status','/api/search?q=anime&limit=1']:
    try:
        with urllib.request.urlopen('http://100.116.176.16:8091'+path, timeout=10) as r:
            print(path, r.status, r.read().decode()[:500])
    except Exception as e:
        print(path, 'ERR', e)
PY


/api/status 200 {"ok": true, "db_path": "/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3", "dashboard": {"active_tasks": [{"id": "task_33d854e72d094660a2fb293a8723f5c5", "title": "Fix GitHub credential automation so repo pushes and PR creation use the intended account reliably", "intent": "The current credential helper flow can select the wrong GitHub identity during multi-repo push and PR automation. Investigate the root cause across gh auth git credential helper and repo remote handling, then design
/api/search?q=anime&limit=1 200 {"retrieval_id": "ret_d66ad6cfb6164c2fa6297e7d64a04f96", "results": [{"memory": {"id": "legacy_claim_73698c1a-612d-456d-b178-6286ae9ab9cd_153d4bcd9fffe7e9", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Research claim: When Sebas shares notable facts about himself or his projects, persist them in l", "content": "When Sebas shares notable facts about himself or his projects, persist them in loca


**Investigating ingestion issues**

It seems there might be a problem with the ingestion process due to a database schema mismatch. I think I should inspect the API, particularly if the /api/ingest endpoint expects data without schema_version but still adds it automatically. Actually, the error for the memories table not having a column named schema_version implies that the server code is incorrectly sending that information to the database. I could look into using a legacy endpoint or checking the service and repository locally with grep.

bash -lc 'grep -R "schema_version" -n /mnt/rpi 2>/dev/null | head -50'


Command timed out after 20 seconds


find /mnt/rpi -maxdepth 4 -type d -iname '*agents-database*' -o -type f -iname '*agents-database*' | head -50


find: ‘/mnt/rpi/lost+found’: Permission denied
find: ‘/mnt/rpi/apt-cache/archives/partial’: Permission denied
/mnt/rpi/shared/agents-database-context-bloat.html
/mnt/rpi/agents-database
/mnt/rpi/agents-database/docs/launchd/agents-database-maintenance.plist
/mnt/rpi/agents-database/systemd/user/agents-database-http.service
/mnt/rpi/service-backups/agents-database
/mnt/rpi/service-backups/agents-database/agents-database-20260422-112632.tar.gz.sha256
/mnt/rpi/service-backups/agents-database/agents-database-20260422-112632.tar.gz
/mnt/rpi/pi-config/docs/agents-database-memory-contract.md
/mnt/rpi/pi-config/sessions/agents-database-knowledge-brief-2026-04-23.md
/mnt/rpi/pi-config/sessions/agents-database-snapshot-2026-04-23.html
/mnt/rpi/pi-config/sessions/agents-database-knowledge-brief-2026-04-23.html


# Agents Database memory contract

This repo uses `agents-database` as the canonical memory store.

## Canonical behavior

- `MemoryService` / `MemoryEngine` own the memory pipeline.
- `ingest()` writes memory records.
- `capture_session()` and `capture_conversation()` create raw source material.
- `process_pending()` extracts episode memories from pending ingestion events.
- `consolidate_candidates(config)` performs dedup, conflict detection, promotion, and optional synthesis.
- `maintenance_jobs` and `maintenance_runs` schedule and audit periodic consolidation.
- `episode` and `inbox` records are candidates, not truth, until consolidated or explicitly promoted.
- `memory_conflicts` and `memory_links` are the right place for contradiction and relation semantics.

## Pi-side rule

`pi-config` may:

- capture session context
- queue analyzer jobs
- ask for promotion candidates
- emit inspection reports

`pi-config` should not:

- reimplement the canonical consolidation pipeline
- invent a parallel truth store
- silently promote memories outside the shared DB contracts


**Exploring API options**

I'm thinking about possibly using SQLite directly if the API is broken. However, the instructions say to use the agents-database HTTP API, particularly the /ingest endpoint. Yet it seems the helper is down due to a broken server. I might need to inspect the documentation or the session to find a workaround. Maybe I should search the ingest endpoint documentation for any schema version issues that could be causing this.

grep -R "schema_version" -n /mnt/rpi/agents-database | head -50


grep: /mnt/rpi/agents-database/tests/__pycache__/test_memory_service.cpython-313-pytest-9.0.2.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/store.cpython-311.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/schema.cpython-313.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/schema.cpython-311.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/service.cpython-313.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/store.cpython-313.pyc: binary file matches
grep: /mnt/rpi/agents-database/src/shared_agent_memory/__pycache__/service.cpython-311.pyc: binary file matches
/mnt/rpi/agents-database/tests/test_memory_service.py:45:    assert results["results"][0]["memory"]["schema_version"] == SCHEMA_VERSION
/mnt/rpi/agents-database/src/shared_agent_memory/schema.py:51:    schema_version TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}',
/mnt/rpi/agents-database/src/shared_agent_memory/schema.py:150:    schema_version TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}',
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:82:            "schema_version": memory_input.get("schema_version", SCHEMA_VERSION),
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:113:                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:117:                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:654:            "schema_version": SCHEMA_VERSION,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:677:                    id, schema_version, run_id, title, intent, kind, status, priority, project_id, repo_id,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:681:                    :id, :schema_version, :run_id, :title, :intent, :kind, :status, :priority, :project_id, :repo_id,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:945:                WHERE schema_version != ?
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:955:                WHERE schema_version != ?
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:965:            "schema_version": SCHEMA_VERSION,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:987:                    SET schema_version = ?, type = ?, subtype = ?, origin_agent = ?, run_id = ?, task_id = ?,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:992:                        record["schema_version"],
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1015:                    SET schema_version = ?, run_id = ?, metadata_json = ?, updated_at = ?
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1018:                    (record["schema_version"], record["run_id"], record["metadata_json"], utcnow(), record["id"]),
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1194:        if row.get("schema_version") == SCHEMA_VERSION and not metadata.get("legacy_kind") and not metadata.get("legacy_system"):
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1217:            "schema_version": SCHEMA_VERSION,
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1231:        if row.get("schema_version") == SCHEMA_VERSION and not metadata.get("legacy_kind") and not metadata.get("legacy_system"):
/mnt/rpi/agents-database/src/shared_agent_memory/service.py:1238:            "schema_version": SCHEMA_VERSION,
/mnt/rpi/agents-database/src/shared_agent_memory/store.py:19:            self._ensure_column(conn, "memories", "schema_version", f"TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}'")
/mnt/rpi/agents-database/src/shared_agent_memory/store.py:26:            self._ensure_column(conn, "tasks", "schema_version", f"TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}'")


from __future__ import annotations

import json
import sqlite3
from contextlib import contextmanager
from pathlib import Path
from typing import Iterator

from .schema import SCHEMA_SQL, SCHEMA_VERSION

class SQLiteStore:
    def __init__(self, db_path: str | Path) -> None:
        self.db_path = str(db_path)

    def initialize(self) -> None:
        with self.connection() as conn:
            conn.executescript(SCHEMA_SQL)
            self._ensure_column(conn, "memories", "schema_version", f"TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}'")
            self._ensure_column(conn, "memories", "subtype", "TEXT")
            self._ensure_column(conn, "memories", "run_id", "TEXT")
            self._ensure_column(conn, "memories", "task_id", "TEXT")
            self._ensure_column(conn, "memories", "url", "TEXT")
            self._ensure_column(conn, "memories", "domain", "TEXT")
            self._ensure_column(conn, "memories", "origin_agent", "TEXT")
            self._ensure_column(conn, "tasks", "schema_version", f"TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}'")
            self._ensure_column(conn, "tasks", "run_id", "TEXT")

    @contextmanager
    def connection(self) -> Iterator[sqlite3.Connection]:
        conn = sqlite3.connect(self.db_path)
        conn.row_factory = sqlite3.Row
        conn.execute("PRAGMA foreign_keys = ON")
        try:
            yield conn
            conn.commit()
        finally:
            conn.close()

    @staticmethod
    def dumps(value: object) -> str:
        return json.dumps(value, ensure_ascii=True, sort_keys=True)

    @staticmethod
    def loads(value: str | None) -> object:
        if not value:
            return None
        return json.loads(value)

    @staticmethod
    def _ensure_column(conn: sqlite3.Connection, table: str, column: str, definition: str) -> None:
        existing = {row["name"] for row in conn.execute(f"PRAGMA table_info({table})")}
        if column in existing:
            return
        conn.execute(f"ALTER TABLE {table} ADD COLUMN {column} {definition}")


**Exploring database options**

I'm thinking about whether I should run an initialize or alter command. There's a chance I might not need the sqlite3 command line interface at all. Instead, it might be more efficient to use a Python script. I definitely need to inspect the schema version value to see where things stand. It seems like there's a bit of work ahead to make sure everything is set up correctly!


SCHEMA_VERSION = "shared-agent-memory-v2"

SCHEMA_SQL = f"""
PRAGMA foreign_keys = ON;

CREATE TABLE IF NOT EXISTS projects (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    description TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL
);

CREATE TABLE IF NOT EXISTS repos (
    id TEXT PRIMARY KEY,
    project_id TEXT,
    name TEXT NOT NULL,
    path TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    FOREIGN KEY(project_id) REFERENCES projects(id)
);

CREATE TABLE IF NOT EXISTS sources (
    id TEXT PRIMARY KEY,
    source_kind TEXT NOT NULL,
    title TEXT,
    content TEXT NOT NULL,
    source_ref TEXT,
    metadata_json TEXT NOT NULL,
    created_at TEXT NOT NULL
);

CREATE TABLE IF NOT EXISTS ingestion_events (
    id TEXT PRIMARY KEY,
    trigger_type TEXT NOT NULL,
    source_id TEXT NOT NULL,
    requested_scope TEXT NOT NULL,
    job_state TEXT NOT NULL,
    processor_outcome TEXT,
    retry_count INTEGER NOT NULL DEFAULT 0,
    error_message TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    processed_at TEXT,
    FOREIGN KEY(source_id) REFERENCES sources(id)
);

CREATE TABLE IF NOT EXISTS memories (
    id TEXT PRIMARY KEY,
    schema_version TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}',
    type TEXT NOT NULL,
    subtype TEXT,
    scope TEXT NOT NULL,
    status TEXT NOT NULL,
    project_id TEXT,
    repo_id TEXT,
    agent_id TEXT,
    origin_agent TEXT,
    run_id TEXT,
    task_id TEXT,
    url TEXT,
    domain TEXT,
    source_kind TEXT NOT NULL,
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    summary TEXT NOT NULL,
    confidence REAL NOT NULL,
    freshness REAL NOT NULL,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    observed_at TEXT NOT NULL,
    source_ref TEXT,
    evidence_ref TEXT,
    embedding_json TEXT,
    metadata_json TEXT NOT NULL,
    FOREIGN KEY(project_id) REFERENCES projects(id),
    FOREIGN KEY(repo_id) REFERENCES repos(id)
);

[153 more lines in file. Use offset=81 to continue.]


**Considering initialization in Python**

I’m thinking that it might be easier to use store.initialize through the Python package import. This should efficiently add any missing columns. I also want to check if any tasks are missing, as that might not be relevant. Running the initialization seems low risk, so I can go ahead with that. After that, I can save some memory.

python - <<'PY'
import sys
sys.path.insert(0, '/mnt/rpi/agents-database/src')
from shared_agent_memory.store import SQLiteStore
store = SQLiteStore('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
store.initialize()
print('initialized')
PY


Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  File "/mnt/rpi/agents-database/src/shared_agent_memory/store.py", line 18, in initialize
    conn.executescript(SCHEMA_SQL)
    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
sqlite3.OperationalError: no such column: subtype

Command exited with code 1


**Inspecting table schema**

I'm noticing that the executescript is failing, and it could be due to an index or trigger referencing a subtype in an existing old table. I should take a closer look at the schema, especially around the indexes. It might be a good idea to use an ALTER statement to make adjustments. In the meantime, ensuring that the columns are accurate could help, and I think I'll use PRAGMA table_info to check that.

python - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
for table in ['memories','tasks']:
    print('TABLE',table)
    for row in conn.execute(f'PRAGMA table_info({table})'):
        print(dict(row))
PY


TABLE memories
{'cid': 0, 'name': 'id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 1}
{'cid': 1, 'name': 'type', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 2, 'name': 'scope', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 3, 'name': 'status', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 4, 'name': 'project_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 5, 'name': 'repo_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 6, 'name': 'agent_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 7, 'name': 'source_kind', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 8, 'name': 'title', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 9, 'name': 'content', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 10, 'name': 'summary', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 11, 'name': 'confidence', 'type': 'REAL', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 12, 'name': 'freshness', 'type': 'REAL', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 13, 'name': 'created_at', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 14, 'name': 'updated_at', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 15, 'name': 'observed_at', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 16, 'name': 'source_ref', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 17, 'name': 'evidence_ref', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 18, 'name': 'embedding_json', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 19, 'name': 'metadata_json', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
TABLE tasks
{'cid': 0, 'name': 'id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 1}
{'cid': 1, 'name': 'title', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 2, 'name': 'intent', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 3, 'name': 'kind', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 4, 'name': 'status', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 5, 'name': 'priority', 'type': 'INTEGER', 'notnull': 1, 'dflt_value': '3', 'pk': 0}
{'cid': 6, 'name': 'project_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 7, 'name': 'repo_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 8, 'name': 'parent_task_id', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 9, 'name': 'origin', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 10, 'name': 'owner_agent', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 11, 'name': 'blocked_reason', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 12, 'name': 'requires_human_input', 'type': 'INTEGER', 'notnull': 1, 'dflt_value': '0', 'pk': 0}
{'cid': 13, 'name': 'due_at', 'type': 'TEXT', 'notnull': 0, 'dflt_value': None, 'pk': 0}
{'cid': 14, 'name': 'created_at', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 15, 'name': 'updated_at', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}
{'cid': 16, 'name': 'metadata_json', 'type': 'TEXT', 'notnull': 1, 'dflt_value': None, 'pk': 0}


**Inspecting database schema**

I'm considering how to manually alter the table to add the necessary columns. It seems like I might need to check the indexes since there could be references to a subtype causing an initialization failure if the index exists on a missing column. So, my plan is to inspect the schema regarding the indexes and search for any relevant information about the subtype. This will help clear things up!


CREATE TABLE IF NOT EXISTS profiles (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    profile_json TEXT NOT NULL,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL
);

CREATE TABLE IF NOT EXISTS tasks (
    id TEXT PRIMARY KEY,
    schema_version TEXT NOT NULL DEFAULT '{SCHEMA_VERSION}',
    run_id TEXT,
    title TEXT NOT NULL,
    intent TEXT NOT NULL,
    kind TEXT NOT NULL,
    status TEXT NOT NULL,
    priority INTEGER NOT NULL DEFAULT 3,
    project_id TEXT,
    repo_id TEXT,
    parent_task_id TEXT,
    origin TEXT,
    owner_agent TEXT,
    blocked_reason TEXT,
    requires_human_input INTEGER NOT NULL DEFAULT 0,
    due_at TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    metadata_json TEXT NOT NULL,
    FOREIGN KEY(project_id) REFERENCES projects(id),
    FOREIGN KEY(repo_id) REFERENCES repos(id),
    FOREIGN KEY(parent_task_id) REFERENCES tasks(id)
);

CREATE TABLE IF NOT EXISTS task_runs (
    id TEXT PRIMARY KEY,
    task_id TEXT NOT NULL,
    agent_id TEXT NOT NULL,
    status TEXT NOT NULL,
    input_payload_json TEXT NOT NULL,
    result_summary TEXT,
    error_message TEXT,
    started_at TEXT NOT NULL,
    completed_at TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    metadata_json TEXT NOT NULL,
    FOREIGN KEY(task_id) REFERENCES tasks(id) ON DELETE CASCADE
);

CREATE TABLE IF NOT EXISTS artifacts (
    id TEXT PRIMARY KEY,
    task_id TEXT NOT NULL,
    artifact_type TEXT NOT NULL,
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    format TEXT NOT NULL,
    status TEXT NOT NULL,
    source_ref TEXT,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    metadata_json TEXT NOT NULL,
    FOREIGN KEY(task_id) REFERENCES tasks(id) ON DELETE CASCADE
);

CREATE INDEX IF NOT EXISTS idx_memories_scope ON memories(scope);
CREATE INDEX IF NOT EXISTS idx_memories_status ON memories(status);
CREATE INDEX IF NOT EXISTS idx_memories_project ON memories(project_id);
CREATE INDEX IF NOT EXISTS idx_memories_repo ON memories(repo_id);
CREATE INDEX IF NOT EXISTS idx_memories_type ON memories(type);
CREATE INDEX IF NOT EXISTS idx_memories_subtype ON memories(subtype);
CREATE INDEX IF NOT EXISTS idx_memories_run ON memories(run_id);
CREATE INDEX IF NOT EXISTS idx_memories_task ON memories(task_id);
CREATE INDEX IF NOT EXISTS idx_memories_url ON memories(url);
CREATE INDEX IF NOT EXISTS idx_memories_domain ON memories(domain);
CREATE INDEX IF NOT EXISTS idx_memories_origin_agent ON memories(origin_agent);
CREATE INDEX IF NOT EXISTS idx_ingestion_state ON ingestion_events(job_state);
CREATE INDEX IF NOT EXISTS idx_tasks_status ON tasks(status);
CREATE INDEX IF NOT EXISTS idx_tasks_owner ON tasks(owner_agent);
CREATE INDEX IF NOT EXISTS idx_tasks_run ON tasks(run_id);
CREATE INDEX IF NOT EXISTS idx_task_runs_task ON task_runs(task_id);

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**Executing schema modifications**

I think starting with adding the subtype first might be helpful. It seems like a good strategy to execute the schema effectively. I plan to manually add all the required columns, and then perhaps the helper function will work correctly afterward. If any columns are missing, I can use Python to alter them. This feels like a solid approach to ensure everything is in place!

python - <<'PY'
import sqlite3
DB='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'
conn=sqlite3.connect(DB)
conn.row_factory=sqlite3.Row
needed={
 'memories':[
   ('schema_version', "TEXT NOT NULL DEFAULT 'shared-agent-memory-v2'"),
   ('subtype', 'TEXT'),
   ('run_id', 'TEXT'),
   ('task_id', 'TEXT'),
   ('url', 'TEXT'),
   ('domain', 'TEXT'),
   ('origin_agent', 'TEXT'),
 ],
 'tasks':[
   ('schema_version', "TEXT NOT NULL DEFAULT 'shared-agent-memory-v2'"),
   ('run_id', 'TEXT'),
 ],
}
for table, cols in needed.items():
    existing={row['name'] for row in conn.execute(f'PRAGMA table_info({table})')}
    for name, ddl in cols:
        if name not in existing:
            conn.execute(f'ALTER TABLE {table} ADD COLUMN {name} {ddl}')
            print(f'added {table}.{name}')
conn.commit()
conn.close()
print('done')
PY


added memories.schema_version
added memories.subtype
added memories.run_id
added memories.task_id
added memories.url
added memories.domain
added memories.origin_agent
added tasks.schema_version
added tasks.run_id
done


Sebas anime taste and watch state

Anime taste: prefers dark/existential tone, high production/style, clear structure/payoff, finite length, high signal density, contained arcs or complete runs. Dislikes endless shonen sprawl, slow starts without payoff, filler, low-density episodes. Tolerates philosophy if momentum exists. Sweet spot: short, stylish, conclusive series with emotional weight. Completed baseline: Devilman Crybaby, Parasyte: The Maxim, Cyberpunk: Edgerunners, Fullmetal Alchemist: Brotherhood. Paused/dropped: Attack on Titan at S1E25/"Wall" before major tone-structure shift; Demon Slayer unclear, likely early S1 or before Mugen Train/Entertainment District; Samurai Champloo around episode 3 in early episodic phase; Frieren early around episodes 3-6 before exam arc/structured progression. Best next recs ranked: 1) Akudama Drive 2) Vivy: Fluorite Eye's Song 3) Dororo 4) Cowboy Bebop if Champloo clicks 5) Made in Abyss if OK with dark turns 6) 86 if wants emotion without drift. Best resumes: Samurai Champloo to around episode 8 then decide; Attack on Titan continue into S2+; Demon Slayer jump to Entertainment District.

Summary: Anime taste: prefers dark/existential tone, high production/style, clear structure/payoff, finite length, high signal density, contained arcs or complete runs. Dislikes endless sh…


**Mentioning memory fixes**

I think it's important to mention the schema fix since the user asked about adding memory. I should also include the memory ID to make it clear. While I'm at it, I want to point out that I fixed the agents-database schema issue during the save process too. It seems relevant and might help provide a clearer picture of what I've done.

Saved.

Memory:
- `mem_07bec891ac99453388c2570402bbd256`
- type: `profile`
- title: `Sebas anime taste and watch state`

Stored:
- taste profile
- completed baseline
- paused/dropped points
- ranked recs
- best resume paths

Also hit a DB schema mismatch on save and fixed the missing memory columns so writes work again.