--- name: deepdive description: Full specialist analysis via parallel agent dispatch. Researcher, Architect, and PM produce a prioritized report of what to build next (30-60s). --- # 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` (or legacy `agenteam.yaml`) in the project root. If missing: - Create config dir: `mkdir -p .agenteam` - Copy the template: `cp /templates/agenteam.yaml.template .agenteam/config.yaml` - Set the team name to the project directory name - Generate agents: `python3 /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: ```bash python3 /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/-deepdive.md`) - `dispatch` -- list of `{role, agent}` objects for the three specialist roles (researcher, architect, pm) ### 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researcher with: - Read all files in `docs/research/` and assess staleness (anything older than 2 weeks is potentially outdated) - Search the web and GitHub for new trends, tools, and community discussions relevant to the project - Check for competitor moves, new releases in the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. ### 5. Synthesize Deepdive Report Combine all three outputs into a single report using this format: ``` # AgenTeam Deepdive: Date: ## 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/-deepdive.md`): ```bash 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: Completed in ~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: `/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 Expected completion: 30-60 seconds. The Researcher and Architect run in parallel (each ~15-30s), followed by the PM (~15-30s) which reads their outputs.