Deepdive

AgenTeam Deepdive

Run a full specialist analysis by dispatching three roles in parallel. Unlike the standup skill (which reads state locally), deepdive launches Codex subagents to investigate external signals, internal code health, and strategic priorities. Expect 30-60 seconds for completion.

Process

1. Auto-Init Guard

Check for .agenteam/config.yaml, .agenteam.team/config.yaml, or legacy agenteam.yaml in the project root. If all are missing:

• Create config dir: mkdir -p .agenteam
• Copy the template: cp <plugin-dir>/templates/agenteam.yaml.template .agenteam/config.yaml
• Set the team name to the project directory name
• Generate agents: python3 <runtime>/agenteam_rt.py generate
• Tell the user: "AgenTeam auto-initialized with default roles. Edit .agenteam/config.yaml to customize."

2. Gather State with Dispatch Flag

Call the runtime with the --dispatch flag to get state and dispatch plans for the three specialist roles:

python3 <runtime>/agenteam_rt.py standup --dispatch

Capture the JSON output. Expected fields (same as standup, plus dispatch info):

• health -- on-track, at-risk, off-track, or no-active-run
• run_id -- current run identifier (may be null)
• task -- task description
• stages -- stage statuses
• artifact_paths -- map of role name to artifact directory
• output_path -- where to write the final report (e.g., docs/meetings/<timestamp>-deepdive.md)
• dispatch -- list of {role, agent} objects for the three specialist roles (researcher, architect, pm)

Create a durable checkpoint at .agenteam/deepdive/<run_id>.json before dispatch. Record max_elapsed_minutes (default 60), max_agents (default 2 concurrent specialists), each role's attempt/thread ID, last heartbeat, output artifact, and stop reason. On restart, validate completed artifacts and resume only missing or interrupted roles; never repeat a completed specialist solely because the controller restarted.

3. Dispatch Specialist Agents in Parallel

Launch three Codex subagents in parallel. Each role has a focused mandate:

Researcher (@Researcher)

Agent file: .codex/agents/researcher.toml

Prompt the researcher with:

• Read all files in docs/research/ and assess staleness (anything older than 2 weeks is potentially outdated)
• Search the web and GitHub for new trends, tools, and community discussions relevant to the project
• Check for competitor moves, new releases in the dependency ecosystem, and community feedback on similar tools
• Produce a structured report of external signals

Expected output format:

## External Signals

### Trends
- [signal] description and relevance to this project

### Ecosystem
- [dependency/tool] notable updates or risks

### Community
- [source] feedback, requests, or discussions relevant to our work

Architect (@Architect)

Agent file: .codex/agents/architect.toml

Prompt the architect with:

• Read all files in docs/designs/ and compare against the current codebase -- identify design drift (where the implementation diverges from the documented design)
• Check for tech debt signals: duplicated logic, overly complex modules, missing error handling, dead code
• Review dependency health: outdated packages, known vulnerabilities, abandoned upstream projects
• Produce a structured report of internal health

Expected output format:

## Internal Health

### Design Drift
- [area] how implementation differs from design doc

### Tech Debt
- [area] description and severity (low/medium/high)

### Dependencies
- [package] status and risk level

PM (@Pm)

Agent file: .codex/agents/pm.toml

Prompt the PM with:

• Wait for and read the Researcher and Architect outputs (passed as context once they complete)
• Read docs/strategies/ for current roadmap and strategic priorities
• Cross-reference external signals (Researcher) with internal health (Architect) and existing strategy
• Produce a prioritized list of recommendations for what to build next, with rationale for each item

Expected output format:

## Recommendations

1. **[title]** -- rationale based on research + architecture analysis
   Priority: [high/medium/low]
   Effort: [small/medium/large]

2. **[title]** -- rationale
   Priority: ...
   Effort: ...

Dispatch order: Researcher and Architect run in parallel. PM runs after both complete (it needs their outputs as input).

4. Collect Outputs

Gather the outputs from all three subagents:

• Researcher report (external signals)
• Architect report (internal health)
• PM report (prioritized recommendations)

If any agent fails, include an error note in that section and continue with the available outputs.

Update the checkpoint and emit a heartbeat whenever a specialist reports progress. Enforce the elapsed and agent budgets. A timed-out specialist is recorded as interrupted; if it has a resumable thread, resume it before considering a fresh attempt. Synthesis is its own checkpoint and starts only after the Researcher/Architect terminal states are durable.

5. Synthesize Deepdive Report

Combine all three outputs into a single report using this format:

# AgenTeam Deepdive: <project-name>
Date: <YYYY-MM-DD HH:MM>

## Health: [ON TRACK | AT RISK | OFF TRACK]

## External Signals (Researcher)
- trending approaches, competitor moves, community feedback

## Internal Health (Architect)
- design drift, tech debt, dependency risks

## Recommendations (PM)
- prioritized list of what to build next, with rationale

## Action Items
- specific next steps with owners

Rules for the report:

• Health is derived from the runtime JSON, same as the standup skill.
• External Signals comes directly from the Researcher output. Trim to the most actionable items (no more than 5-7 bullets).
• Internal Health comes from the Architect output. Group by severity, high-severity items first.
• Recommendations comes from the PM output. Keep prioritization and effort estimates. Limit to the top 5-7 items.
• Action Items is a synthesis step you perform: extract the most concrete next steps from all three reports, assign an owner (role name) to each, and list them in priority order.
• Omit any section where the corresponding agent produced no output (e.g., if the Researcher found nothing notable, omit External Signals).

6. Write Report

Write the synthesized report to the output_path from the runtime JSON (typically docs/meetings/<timestamp>-deepdive.md):

mkdir -p "$(dirname "$output_path")"

Write the report content to that file.

7. Display to User

Show the full deepdive report to the user in the conversation. Include a timing note:

AgenTeam Deepdive: <project-name>
Completed in ~<elapsed>s (3 specialists dispatched)

Runtime Path Resolution

Resolve the AgenTeam runtime:

1. If running from the plugin directory: ./runtime/agenteam_rt.py
2. If installed as a Codex plugin: <plugin-install-path>/runtime/agenteam_rt.py

Error Handling

• If a subagent fails, include a note in the corresponding section: "[role] analysis unavailable -- [error reason]"
• If the runtime command fails, fall back to a best-effort report using direct file reads (same approach as the standup skill)
• Never let one agent's failure block the entire report

Performance Target

Fast runs may finish in 30-60 seconds, but correctness does not depend on that estimate. The durable max_elapsed_minutes budget and heartbeat/checkpoint state govern long-running analysis. Researcher and Architect run in parallel; PM starts after both terminal outputs are checkpointed.

Source: hashgraph-online/awesome-codex-plugins → plugins/yimwoo/codex-agenteam/skills/deepdive/SKILL.md