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open-tag-admin

Agents & models

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Updated 10-16 08:11

About this skill

Open Tag (admin)

This skill is the control console for an Open Tag deployment. Use it for the first-time setup and for ongoing operation alike: inspect the live bot, change the backend or permitted scopes, add a new data source, move the bot to another Slack channel, or debug a run.

Keep the architecture generic:

  • Brain: the selected CLI agent backend — claude -p (Claude Code) or codex exec (Codex).
  • Memory: MFS-indexed, operator-authorized context such as Slack history, repositories, docs, issues, databases, or object stores.
  • Tools: MFS connectors for external read/search plus any explicit tools the backend is allowed to use in the workspace.

The user-facing flow is:

  1. Configure MFS sources and allowed scopes.
  2. Configure a Slack app with Socket Mode and invite it to a sandbox channel.
  3. Start the Open Tag bridge.
  4. Move to Slack and tag the bot in a thread.
  5. Let the bridge invoke the selected backend with thread context and scoped MFS helper scripts for permitted external context.

This skill does not call a model API directly. Model access, tool access, and write permissions come from the selected CLI agent backend.

Prerequisites

Open Tag is a thin layer on top of a running MFS server with at least one indexed source. Confirm these before any Slack work:

  1. MFS server installed and running. uv tool install mfs-server, then mfs-server run (binds 127.0.0.1:13619). Check with curl -s 127.0.0.1:13619/healthz.
  2. At least one data source indexed. Open Tag only consumes already-indexed scopes as Memory — it does not configure connectors itself. Use the mfs-ingest skill (Codex: $mfs-ingest) to add a source; see "Adding data sources" below.

opentag_doctor.py fails fast with a hint if either is missing.

Bot name convention

The Slack display name is whatever you call the Slack app — Open Tag's code strips the mention regardless. Recommended convention, so it reads like the official @Claude tag:

Backend Suggested Slack app name In Slack
claude OpenClaude @OpenClaude <task>
codex OpenCodex @OpenCodex <task>

Name the Slack app accordingly when you create it (step 3). Set OPENTAG_BOT_NAME if you want the startup summary to print a different label.

Setup Workflow

  1. Satisfy Prerequisites above (MFS running + at least one indexed source).
  2. Read references/slack-adapter.md and follow its end-to-end checklist.
  3. Confirm or create a private or otherwise isolated Slack channel.
  4. Create or reuse a Slack app named per the convention above, enable Socket Mode, subscribe to app_mention, add the required bot scopes, install it, and invite the bot to the channel.
  5. Configure MFS memory sources and set MFS_ALLOWED_SCOPES to the exact source roots the runtime agent may use.
  6. Choose OPENTAG_BACKEND explicitly: claude or codex.
  7. Run python scripts/opentag_doctor.py --channel-id <channel-id> and fix any failed check.
  8. Start the bridge with uv run --with slack-bolt python scripts/slack_socket_agent.py --backend <backend>. It prints a "what's live now" summary — read it, then validate thread context, permitted-context retrieval, and task execution with a realistic delegated task.

Adding data sources

Open Tag's reach is exactly what MFS has indexed and what you list in MFS_ALLOWED_SCOPES. To add a source, use the mfs-ingest skill — it handles credentials and writes the connector config; Open Tag never duplicates that.

Representative sources (each is mfs add <uri> --config <toml> once, then add its root to MFS_ALLOWED_SCOPES):

  • Local repo / docs: mfs add /path/to/repo → file://local/path/to/repo
  • Slack history: slack://team-memory (own token + channel allowlist)
  • GitHub (code + issues): github://your-org/your-repo
  • Linear (issues): linear://your-workspace
  • Postgres rows: postgres://prod

MFS supports 20+ connectors (databases, object stores, trackers, chat, web). For the full list and per-connector credentials, point users at the mfs-ingest skill and docs/connectors/. This breadth of Memory — including raw data layers, all self-hosted — is Open Tag's main edge over a hosted tag bot; it does not add hosted governance, audit, or approval flows.

What The Python Scripts Do

Keep the Python scripts as deterministic glue:

  • slack_socket_agent.py: receive Slack app_mention, read the thread, post progress, call the backend, and post the final answer.
  • opentag_agent.py: build a non-interactive prompt and invoke the selected CLI backend.
  • mfs_search.py and mfs_cat.py: call the MFS HTTP API with scoped search and reads.
  • opentag_memory.py: maintain optional local seed notes and re-index them in MFS for deterministic demos.
  • opentag_doctor.py: preflight environment variables, Slack bot access, MFS reachability, allowed scopes, and backend availability.

Shell scripts can wrap these commands, but Python is less brittle for Slack Web API calls, JSON handling, temporary files, subprocess timeouts, and cross-agent backend selection.

Runtime Contract

The Slack bridge invokes a fresh CLI agent per mention. That runtime agent must follow references/runtime-agent.md. Keep runtime behavior there, not in this admin skill.

Thread context is short-term state. Durable context should come from permitted MFS scopes such as indexed Slack history, repos, docs, issues, databases, object stores, or optional local seed notes. See references/memory.md for the optional helper's file shape.

Never hard-code real workspace names, channel IDs, user IDs, local absolute paths, or customer/project details into this skill. Use placeholders in documentation and environment examples.

References

  • Read references/slack-adapter.md when configuring or running Slack.
  • Read references/backends.md when changing backend selection or command invocation.
  • Read references/runtime-agent.md when changing per-mention behavior.
  • Read references/memory.md only when using optional local seed notes.