- Delete 21 old team skill directories using CSV-wave pipeline pattern (~100+ files) - Delete old team-lifecycle (v3) and team-planex-v2 - Create generic team-worker.toml and team-supervisor.toml (replacing tlv4-specific TOMLs) - Convert 19 team skills from Claude Code format (Agent/SendMessage/TaskCreate) to Codex format (spawn_agent/wait_agent/tasks.json/request_user_input) - Update team-lifecycle-v4 to use generic agent types (team_worker/team_supervisor) - Convert all coordinator role files: dispatch.md, monitor.md, role.md - Convert all worker role files: remove run_in_background, fix Bash syntax - Convert all specs/pipelines.md references - Final state: 20 team skills, 217 .md files, zero Claude Code API residuals Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
3.4 KiB
role, prefix, inner_loop, discuss_rounds, message_types
| role | prefix | inner_loop | discuss_rounds | message_types | |||||
|---|---|---|---|---|---|---|---|---|---|
| strategist | STRATEGY | false |
|
|
Optimization Strategist
Analyze bottleneck reports and baseline metrics to design a prioritized optimization plan with concrete strategies, expected improvements, and risk assessments.
Phase 2: Analysis Loading
| Input | Source | Required |
|---|---|---|
| Bottleneck report | /artifacts/bottleneck-report.md | Yes |
| Baseline metrics | /artifacts/baseline-metrics.json | Yes |
| .msg/meta.json | /.msg/meta.json | Yes |
| Wisdom files | /wisdom/patterns.md | No |
- Extract session path from task description
- Read bottleneck report -- extract ranked bottleneck list with severities
- Read baseline metrics -- extract current performance numbers
- Load .msg/meta.json for profiler findings (project_type, scope)
- Assess overall optimization complexity:
| Bottleneck Count | Severity Mix | Complexity |
|---|---|---|
| 1-2 | All Medium | Low |
| 2-3 | Mix of High/Medium | Medium |
| 3+ or any Critical | Any Critical present | High |
Phase 3: Strategy Formulation
For each bottleneck, select optimization approach by type:
| Bottleneck Type | Strategies | Risk Level |
|---|---|---|
| CPU hotspot | Algorithm optimization, memoization, caching, worker threads | Medium |
| Memory leak/bloat | Pool reuse, lazy initialization, WeakRef, scope cleanup | High |
| I/O bound | Batching, async pipelines, streaming, connection pooling | Medium |
| Network latency | Request coalescing, compression, CDN, prefetching | Low |
| Rendering | Virtualization, memoization, CSS containment, code splitting | Medium |
| Database | Index optimization, query rewriting, caching layer, denormalization | High |
Prioritize optimizations by impact/effort ratio:
| Priority | Criteria |
|---|---|
| P0 (Critical) | High impact + Low effort -- quick wins |
| P1 (High) | High impact + Medium effort |
| P2 (Medium) | Medium impact + Low effort |
| P3 (Low) | Low impact or High effort -- defer |
If complexity is High, use CLI tools for multi-perspective analysis (DISCUSS-OPT round) to evaluate trade-offs between competing strategies before finalizing the plan.
Define measurable success criteria per optimization (target metric value or improvement %).
Phase 4: Plan Output
-
Write optimization plan to
<session>/artifacts/optimization-plan.md:Each optimization MUST have a unique OPT-ID and self-contained detail block:
### OPT-001: <title> - Priority: P0 - Target bottleneck: <bottleneck from report> - Target files: <file-list> - Strategy: <selected approach> - Expected improvement: <metric> by <X%> - Risk level: <Low/Medium/High> - Success criteria: <specific threshold to verify> - Implementation guidance: 1. <step 1> 2. <step 2> 3. <step 3> ### OPT-002: <title> ...Requirements:
- Each OPT-ID is sequentially numbered (OPT-001, OPT-002, ...)
- Each optimization must be non-overlapping in target files
- Implementation guidance must be self-contained
-
Update
<session>/.msg/meta.jsonunderstrategistnamespace:- Read existing -> merge -> write back with optimization metadata
-
If DISCUSS-OPT was triggered, record discussion summary in
<session>/discussions/DISCUSS-OPT.md