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 benrben/MPGA --skill plangit clone --depth 1 https://github.com/benrben/MPGAWrote 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/benrben/mpga/plan)<a href="https://agentmods.dev/skills/benrben/mpga/plan"><img src="https://agentmods.dev/badge/skills/benrben/mpga/plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benrben/mpga/plan"><img src="https://agentmods.dev/badge/skills/benrben/mpga/plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00022 | $0.02385 |
| Opus 5.5 | $0.00009 | $0.00954 |
| Sonnet 5.5 | $0.00004 | $0.00477 |
| Haiku 4.5 | $0.00002 | $0.00238 |
Grade A, and why
mpga-plan 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
plan
Trigger: User provides a goal or description to plan. Time to build the GREATEST plan. MPGA alone can fix it — but first we need a tremendous plan.
Protocol
Step 1: Create or find the milestone
If an active milestone already exists:
mpga board live --serve --open
mpga milestone list
cat MPGA/milestones/<id>/PLAN.md
cat MPGA/milestones/<id>/DESIGN.md # if exists
Ask the user: plan tasks under the existing milestone, or create a new one?
If no milestone exists (or user wants a new one): Create a milestone from the user's goal — every great achievement starts with a PLAN:
mpga board live --serve --open
mpga milestone new "<goal name>"
Then edit the generated PLAN.md with the user's objective and acceptance criteria.
Step 2: Read relevant scope documents for the work area
cat MPGA/INDEX.md
cat MPGA/scopes/<relevant-scope>.md
Know the territory BEFORE you plan the attack. Evidence First — always.
Step 3: Research if needed
If config.agents.researchBeforePlan is true:
- Spawn
researcherplus any needed read-onlyscoutagents in PARALLEL. - Use
researcherfor options and tradeoffs; usescoutfor exact code evidence and file boundaries. - Incorporate both into one evidence-based plan.
Step 4: Break work into tasks
Follow these rules — they're the BEST rules:
- Each task = 2-10 minutes of focused work. Small. Focused. POWERFUL.
- Each task must cite exact files to modify — no vague nonsense
- Each task must have checkable acceptance criteria — how do you know you WON?
- Dependencies must be explicit — know what blocks what
- Order tasks by dependency (blocking tasks first)
- Group tasks by scope so independent scopes can move in parallel.
- Mark whether each task is
serialorparallel. - One write lane per scope. That's how we go FAST without chaos.
Step 5: Risk assessment
For EVERY task, assess risk — because winners plan for the unexpected:
| Dimension | Scale | Question |
|---|---|---|
| Complexity | 1-5 | How hard is the implementation? (1 = trivial, 5 = dragon-slaying) |
| Uncertainty | 1-5 | How well do we understand the requirements? (1 = crystal clear, 5 = fog of war) |
| Impact | 1-5 | How much does this affect the rest of the system? (1 = isolated, 5 = everything breaks) |
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.
- 2mo ago First seen · 249 lines · 22 tokens per session scan A 8acc38827ad3
mpga-plan is a skill published in the GitHub repository benrben/MPGA (5 stars, last pushed 7mo ago), licensed MIT. It adds 22 tokens to every session and 2,385 once invoked, about $0.0001 per session on Opus 5.5. 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.
mpga-plan compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| mpga-planbenrben/MPGA | Skill | 5 | 7mo ago | MIT | A | 22 | Claude Code |
| imagegenopenai/codex | Skill | 135k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 316k | 8d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |