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 skywalkercyt/mp-implement --skill subagent-driven-developmentgit clone --depth 1 https://github.com/skywalkercyt/mp-implementWrote 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/skywalkercyt/mp-implement/subagent-driven-development)<a href="https://agentmods.dev/skills/skywalkercyt/mp-implement/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/skywalkercyt/mp-implement/subagent-driven-development/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/skywalkercyt/mp-implement/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/skywalkercyt/mp-implement/subagent-driven-development.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 | $0.00044 | $0.00869 |
| Opus 5.5 | $0.00018 | $0.00348 |
| Sonnet 5.5 | $0.00009 | $0.00174 |
| Haiku 4.5 | $0.00004 | $0.00087 |
Grade A, and why
subagent-driven-development 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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagent-Driven Development
Adapted skill — discipline originating in Superpowers v5.1.0 (MIT, Jesse Vincent), rewritten in mattpocock-skills style.
/mp-implement's Phase 3 execution engine when the Workflow tool is unavailable; may dispatch/mp-implement-issueas an implementer for a slice.
You are the controller. You never implement — every task goes to a fresh subagent with isolated context, keeping your own context free for coordination.
Setup
Read the plan once. Extract every task's full text, and note the scene-setting each needs: where it fits, what it depends on, the modules and callers it touches. Track all tasks in your todo list.
Dispatch
One implementer subagent per task, briefed from implementer-prompt.md. The brief is self-contained — the subagent never reads the plan file and cannot see your session:
- the task's full text, pasted in;
- the scene-setting you noted at setup — for unfamiliar areas, a map of the relevant modules and callers.
Note the base commit before the implementer starts, so reviewers get a clean diff range.
Run one implementer at a time. Answer its questions — before it starts or mid-work — before letting it proceed.
Per-task chain (order fixed)
- Implement — builds, tests, commits, self-reviews. Self-review precedes review; it never replaces it.
- Spec-compliance review loop — fresh reviewer from
spec-reviewer-prompt.md; every finding quotes the spec line it violates. - Code-quality review loop — fresh reviewer from
code-quality-reviewer-prompt.mdover the base→head diff, entered only after spec passes.
Spec before quality: spec fixes rewrite the code a quality review would judge. A finding goes back to the SAME implementer — its context holds the code — and the fix is always re-reviewed. A task completes only when both reviews pass.
Implementer statuses
- DONE → proceed to spec review.
- DONE_WITH_CONCERNS → read the concerns first; correctness or scope concerns get addressed before review, observations get noted and ride along.
- NEEDS_CONTEXT → supply what's missing and re-dispatch.
- BLOCKED → diagnose: missing context → repair the brief; reasoning beyond the implementer → sharper brief or stronger model; task too large → split it; plan wrong → escalate to the human.
What ships with it
3 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.
- 1mo ago First seen · 66 lines · 44 tokens per session scan A 0ec67f8f95cd
subagent-driven-development is a skill published in the GitHub repository skywalkercyt/mp-implement (2 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 869 once invoked, about $0.0002 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.
Other skills, from other repositories
reversi-master
Manage ChatGPT, Codex CLI, and Claude Code accounts and track quota in one place. Use multi-model smart routing with priority and automatic failover. Install AI tools and deploy local LLMs in one click.
Trash Talk
AI becomes provocative and mocks the opponent during gameplay.
game-build
Build a risk-matched whitebox or the approved production game for its target runtime. Turn GAMEDESIGN, and ARTDIRECTION when production begins, into a minimal BUILDBRIEF and a runnable candidate that can be iterated with replayable evidence. Use for prototype the riskiest design question, implement the approved game…
novel-game-analyze
Deconstruct a novel for game adaptation. Compress a raw novel, deconstruction library, or writing project into a SOURCEBIBLE with cited textual evidence, extracting world rules, player verbs, spaces, character will, systems, and visual anchors — without inventing a genre yet. Use for gameable book analysis, analyze a…
novel-to-game
Turn a novel into a fully playable game on the selected target platform. Orchestrates the whole adaptation pipeline — requirements intake, gameable deconstruction, concept selection, world and visual design, target-runtime build, and evidence-based QA — for a novel in any language. Use for novel to game, story to…
game-art-direction
A process for defining the visual and audio direction of a game from its design. It specifies how scenes, characters, interfaces, movement, colour, lighting, materials, sound, and key moments should work together.