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 5.5 | $0.00038 | $0.00678 |
| Sonnet 5.5 | $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 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 — 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.
- 2mo 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 4mo ago), licensed MIT. It adds 94 tokens to every session and 1,694 once invoked, about $0.0004 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.
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