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 avizmarlon/agent-skills --skill agent-flywheelgit clone --depth 1 https://github.com/avizmarlon/agent-skillsWrote 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/avizmarlon/agent-skills/agent-flywheel)<a href="https://agentmods.dev/skills/avizmarlon/agent-skills/agent-flywheel"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/agent-flywheel/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/avizmarlon/agent-skills/agent-flywheel"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/agent-flywheel.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 | $0.00094 | $0.01694 |
| Opus 5 | $0.00047 | $0.00847 |
| Sonnet 5 | $0.00019 | $0.00339 |
| Haiku 4.5 | $0.00009 | $0.00169 |
Grade A, and why
agent-flywheel 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 10d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Flywheel
Overview
A reusable, systematic method for planning and executing software projects with AI agents. Core principle: invest heavily in plan-space before code — that's where errors are cheapest to fix. Inspired by Jeffrey Emanuel's Agent Flywheel framework (agent-flywheel.com).
The "flywheel" metaphor: each iteration upgrades plan quality, artifact reusability, and execution speed. Rework caught in planning costs ~1× effort; in task definition ~5×; in code ~25×. This heuristic shapes where to push reasoning (upstream, to the plan).
When to activate / anti-triggers
Use this skill when:
- Starting a new project or significant feature
- Converting ideas, specs, or requirements into executable plans
- Planning architecture or major technical decisions
- Establishing roadmaps or multi-phase programs
- Coordinating work across multiple agents or sessions
- Explicitly asked to "plan this", "make it a task graph", or "design the build"
Do NOT use when:
- Fixing routine bugs (non-architectural)
- Responding to urgent incidents or ops crises
- Executing an already-approved, locked plan
- Handling customer support or one-off requests
- Re-planning is explicitly out of scope
Step 1: Pick the execution mode (match scope to complexity)
Don't default to heavy machinery. Choose the lightest mode that fits the scope.
| Mode | When to use | Substrate | Complexity |
|---|---|---|---|
| A: Plan-only | Small/sequential projects; solo or tight-knit teams; coherent slice | Markdown plan + test-driven development + standard PR workflow | Minimal |
| B: Plan + task graph | Multiple interconnected tasks; multi-session risk; dependency chains | Markdown plan + structured task graph (JSONL format) + single-agent execution | Medium |
| C: Task-graph + routable beads | Plan B + need resumability across context loss; durable task store | Structured beads + dependency routing + PR-per-cluster | Medium–High |
| D: Full multi-agent swarm | Large, highly parallel work; amortized coordination cost | Only after passing swarm-readiness.md — full coordination layer |
High |
What ships with it
4 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.
- 10d ago First seen · 118 lines · 94 tokens per session scan A e1154c4c80f1
agent-flywheel is a skill published in the GitHub repository avizmarlon/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 94 tokens to every session and 1,694 once invoked, about $0.0005 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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taiyi-ui-design
A design-planning guide for describing how an application's user interface should look and behave. It produces a UI-DESIGN.md document covering layouts, components, interactions, accessibility, and error states.
taiyi-evolve
A workflow skill that compares the implemented code with the frozen design after development and testing. It records architecture changes and proposes updates to DESIGN.md, the document describing the intended system structure.
taiyi-diagram-c4
A code-scanning tool that builds C4 architecture documents from a repository. It separates facts observed in the code from conclusions inferred about the design and uses Mermaid diagrams as the source format.