Borrowing it
Nothing to install: this file belongs to j4th/mtg-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/j4th/mtg-mcp-server/main/.agents/skills/superpowers/executing-plans/SKILL.mdgit clone --depth 1 https://github.com/j4th/mtg-mcp-serverWrote 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/j4th/mtg-mcp-server/executing-plans)<a href="https://agentmods.dev/skills/j4th/mtg-mcp-server/executing-plans"><img src="https://agentmods.dev/badge/skills/j4th/mtg-mcp-server/executing-plans/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/j4th/mtg-mcp-server/executing-plans"><img src="https://agentmods.dev/badge/skills/j4th/mtg-mcp-server/executing-plans.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 | $0.00035 | $0.04904 |
| Opus 5.5 | $0.00014 | $0.01962 |
| Sonnet 5.5 | $0.00007 | $0.00981 |
| Haiku 4.5 | $0.00003 | $0.00490 |
Grade A, and why
executing-plans 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 3d 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.
This is a copy
89% identical to executing-plans — 133 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executing Plans
Execute the plan yourself, task by task, in this session: no implementer subagent per task, no reviewer per task. One fresh-context review of the whole branch at the end.
Why inline: Subagent-driven development pays for a fresh implementer and a fresh reviewer on every task, each re-reading the codebase from zero. Inline execution pays for one context (yours) plus one reviewer at the end. What it gives up is a fresh context per task and a second pair of eyes per task. This skill keeps what those two things bought, by other means: the brief is the spec, the ledger is your memory, TDD is the per-task gate, and the final reviewer is the second pair of eyes.
Core principle: The plan already did the thinking. Execute it exactly, prove each step with a test you watched fail and then pass, and leave a record that survives your own forgetting.
Narration: between tool calls, narrate at most one short line — the ledger and the tool results carry the record.
Continuous execution: Do not pause to check in with your human partner between tasks. They chose inline execution to spend less, not to answer "should I continue?" after every task. Execute all tasks from the plan without stopping.
Rulings, not stalls. Conflicts, ambiguities, plan defects — decide them.
The spec is the binding authority, the plan is its argument, and your
judgment settles what neither answers. Record every decision in the ledger
as Ruling: <what you decided> — <why> — <what it costs if wrong>, and keep
going. Deviating from the plan without a ledgered ruling is a decision made
in secret.
Four things stop you, and only these: an irreversible or destructive operation; a security-sensitive action; a side effect outside this worktree that norms say you ask about first (a merge, a push to a shared branch, a publish); and a plan so broken that every path forward is a guess. For those, stop and ask.
When to Use
- You have a plan from superpowers:writing-plans and your human partner chose inline execution at the handoff.
- Your harness has no subagent tool (see the per-platform references in
../using-superpowers/references/). Never fabricate a dispatch; run the plan here. - Tasks are mostly independent — the same precondition as superpowers:subagent-driven-development.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 374 lines · 35 tokens per session scan A f38e8f2ddcf0
executing-plans is a skill published in the GitHub repository j4th/mtg-mcp-server (20 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 4,904 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to executing-plans, differing in 133 lines, and is treated as a copy.
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Execute tasks from a track's implementation plan following TDD workflow.
principle-test-behavior-not-implementation
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This skill should be used when the user wants to implement features or fix bugs using test-driven development. Enforces the RED-GREEN-REFACTOR cycle with vertical slicing, context isolation between test writing and implementation, human checkpoints, and auto-test feedback loops. Uses multi-agent orchestration with the…