Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add budagov-lab/DreamTeam --skill meta-planner-optimizationgit clone --depth 1 https://github.com/budagov-lab/DreamTeamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/budagov-lab/dreamteam/meta-planner-optimization)<a href="https://agentmods.dev/skills/budagov-lab/dreamteam/meta-planner-optimization"><img src="https://agentmods.dev/badge/skills/budagov-lab/dreamteam/meta-planner-optimization.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00261 |
| Opus 5 | $0.00019 | $0.00130 |
| Sonnet 5 | $0.00008 | $0.00052 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
meta-planner-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Meta Planner Optimization
When to Use
- When
python -m dreamteam update-task <id> doneprintsTRIGGER_META_PLANNER(source of truth) - After every 50 completed tasks
- When DAG has bottlenecks or tech debt accumulates
Workflow
- Read from DB: Terminal →
python -m dreamteam dag-state,python -m dreamteam memory-get summaries,python -m dreamteam memory-get architecture - Analyze: Review DAG, task distribution, tech debt
- Identify: Bottlenecks, oversized tasks, missing tasks
- Optimize: Resplit tasks, add refactor tasks, adjust priorities
- Output: New task files in .dreamteam/tasks/, architecture recommendations for Researcher
Output
- New task files
- Refactor tasks added to DAG
- Architecture change recommendations for Researcher
Rules
- Do not break existing dependencies
- Refactor tasks should have clear scope
- Document rationale for changes
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 33 lines · 39 tokens per session scan A 51e5802536be
meta-planner-optimization is a skill published in the GitHub repository budagov-lab/DreamTeam (1 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 261 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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