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 vaayne/agent-kit --skill ambitiongit clone --depth 1 https://github.com/vaayne/agent-kitWrote 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/vaayne/agent-kit/ambition)<a href="https://agentmods.dev/skills/vaayne/agent-kit/ambition"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/ambition/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/vaayne/agent-kit/ambition"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/ambition.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 | $0.00139 | $0.01239 |
| Opus 5.5 | $0.00056 | $0.00496 |
| Sonnet 5.5 | $0.00028 | $0.00248 |
| Haiku 4.5 | $0.00014 | $0.00124 |
Grade A, and why
ambition 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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ambition
Distilled from Felix Rieseberg's How to be more ambitious: A tutorial for humans with models.
Premise: model capability grows faster than the tasks people give it. People ask for what they believe is possible. This skill finds a higher level of the same task and lets the user choose it. It changes how deep the thinking goes, not the type of task.
This skill proposes; it does not expand scope on its own. Do not start the upgraded work until the user picks a version. After the user picks one, that version is the scope, and the normal rules (simplest approach, no speculative extras) apply inside it.
Inputs
Get the task as the user would give it to an agent. If the user's goal, data, or constraints are not clear, ask one question at a time before you continue. Read the available context (repository, documents, data) first, so the proposals use real sources and tools, not generic ones.
Three directions
Look at the task in each direction. Skip a direction that adds nothing, and say why.
Altitude: the goal behind the goal
Ask "what does the user want to happen next?" Move from the task to the outcome it serves, one level at a time. Example: "fix this crash" → "keep the app free of crashes" → "find crashes before users report them".
- At each level, name what the agent needs to close the loop without a human: data access, tools, machines, permissions, MCP connectors. A higher goal without these is a wish. List the missing parts as setup steps.
- Altitude helps most for outcome work. A signal: without the agent, people would hold meetings about this task.
- Stop at the level the current model and setup can reach. "Go make money" is too high.
Depth: the expert version, not the bigger version
Answer these four questions before you rewrite the task:
- What would a top professional do here that the user did not ask for?
- What is the rawest source material they would start from, instead of summaries and pre-processed inputs?
- Which specific methods and tools would they use? Use the tools, not only the words: a database and a statistical model, not a Markdown table; a profiler and a benchmark, not a guess.
- What, other than the user or the model, would show that the result is correct?
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 · 115 lines · 139 tokens per session scan A 6ee5d19ae63b
ambition is a skill published in the GitHub repository vaayne/agent-kit (53 stars, last pushed 4d ago), licensed MIT. It adds 139 tokens to every session and 1,239 once invoked, about $0.0006 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 2026-10-18.
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