TORCH: Task Orchestration via Relay-Coordinated Handoff

TORCH is a portable Nostr-based task locking toolkit for multi-agent coordination.

Installation

npm install https://github.com/PR0M3TH3AN/TORCH/archive/refs/heads/main.tar.gz

TORCH is distributed from GitHub tarballs and is not currently published to the npm registry.

Usage

CLI

The torch-lock CLI allows you to check, lock, and list tasks.

# Check locks for a specific cadence
npx --no-install torch-lock check --cadence daily

# Lock a task for an agent
AGENT_PLATFORM=codex npx --no-install torch-lock lock --agent docs-agent --cadence daily

# List all active locks
npx --no-install torch-lock list

# Mark a task as completed (permanent)
AGENT_PLATFORM=codex npx --no-install torch-lock complete --agent docs-agent --cadence daily

# Initialize TORCH in a new repository
npx --no-install torch-lock init

# Update TORCH configuration and scripts
npx --no-install torch-lock update

# Run the Dashboard
npx --no-install torch-lock dashboard

Memory CLI

The torch-lock CLI also supports memory operations:

# List memories (optional filters: --agent, --type, --tags, --pinned)
npx --no-install torch-lock list-memories --limit 5

# Inspect a specific memory
npx --no-install torch-lock inspect-memory --id <memory-id>

# Pin/Unpin a memory
npx --no-install torch-lock pin-memory --id <memory-id>
npx --no-install torch-lock unpin-memory --id <memory-id>

# Check memory stats
npx --no-install torch-lock memory-stats

Configuration (torch-config.json)

Create a torch-config.json file in your project root to configure TORCH for your repository. You can use the included torch-config.example.json as a template.

Configuration options:

• nostrLock.namespace (d-tag/tag namespace)
• nostrLock.relays (array of relay URLs)
• nostrLock.ttlSeconds (lock duration in seconds)
• nostrLock.queryTimeoutMs (timeout for relay queries)
• nostrLock.dailyRoster / nostrLock.weeklyRoster (explicit rosters)
• dashboard.defaultCadenceView (daily, weekly, or all)
• dashboard.defaultStatusView (active or all)
• scheduler.firstPromptByCadence.daily / .weekly (first prompt to run)
• scheduler.paused.daily / .weekly (array of agent names to pause locally)

Dashboard

The dashboard provides a live view of lock events.

npx --no-install torch-lock dashboard --port 4173
# Open http://localhost:4173/dashboard/

Environment variables

You can override configuration using environment variables:

• NOSTR_LOCK_NAMESPACE
• NOSTR_LOCK_RELAYS (comma-separated)
• NOSTR_LOCK_TTL
• NOSTR_LOCK_QUERY_TIMEOUT_MS
• NOSTR_LOCK_DAILY_ROSTER (comma-separated list of agents)
• NOSTR_LOCK_WEEKLY_ROSTER (comma-separated list of agents)
• TORCH_CONFIG_PATH (path to config file)
• AGENT_PLATFORM (platform identifier, e.g. codex)
• TORCH_MEMORY_ENABLED (true/false; global memory kill switch, defaults to enabled)
• TORCH_MEMORY_INGEST_ENABLED (true/false or comma-separated canary agent_id allow list)
• TORCH_MEMORY_RETRIEVAL_ENABLED (true/false or comma-separated canary agent_id allow list)
• TORCH_MEMORY_PRUNE_ENABLED (true, false, or dry-run)

Roster precedence

The lock CLI resolves roster names in this order:

1. NOSTR_LOCK_DAILY_ROSTER / NOSTR_LOCK_WEEKLY_ROSTER (comma-separated env overrides).
2. torch-config.json (nostrLock.dailyRoster / nostrLock.weeklyRoster).
3. src/prompts/roster.json (canonical scheduler roster included in the package).
4. Built-in fallback roster.

Included Resources

• src/lib.mjs — Core library logic (can be imported in scripts)
• src/docs/TORCH.md — Protocol summary and usage
• src/prompts/ — Generic scheduler prompts and flow
• skills/ — Repository-local skill guides for agent onboarding and repeatable workflows
• dashboard/ — Static lock dashboard assets

NPM Scripts (for development)

If you are developing torch-lock itself:

• npm run lock:check:daily
• npm run lock:check:weekly
• npm run lock:list
• npm run dashboard:serve
• npm test (run unit tests)
• npm run lint (run linter)

Memory rollout plan

1. Deploy memory schema changes with scheduler jobs disabled (TORCH_MEMORY_ENABLED=false or subsystem flags set to false).
2. Enable ingest for one canary agent_id via TORCH_MEMORY_INGEST_ENABLED=<agent_id>.
3. Validate retrieval quality and storage growth metrics before expanding scope.
4. Enable broader retrieval (TORCH_MEMORY_RETRIEVAL_ENABLED=<allow-list> then true).
5. Enable pruning in dry-run mode first, then switch to active pruning after revalidation.

Memory rollback plan

1. Disable memory flags (TORCH_MEMORY_ENABLED=false and/or set ingest/retrieval/prune flags to false).
2. Stop memory maintenance scheduler processes.
3. Preserve database state for post-incident analysis; do not drop or rewrite memory tables during rollback.
4. Keep prune actions in dry-run (or disabled) until lifecycle policy and data integrity are revalidated.