OpenAI Agents SDK is a Python framework for building workflows in which multiple AI agents use tools, delegate tasks, follow safety checks, and involve people when needed. Developers use it to create and monitor agent, sandbox, realtime, and voice applications.
Borrowing it
Nothing to install: this file belongs to openai/openai-agents-python. 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/openai/openai-agents-python/main/.agents/skills/implementation-kickoff/SKILL.mdgit clone --depth 1 https://github.com/openai/openai-agents-pythonWrote 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/openai/openai-agents-python/implementation-kickoff)<a href="https://agentmods.dev/skills/openai/openai-agents-python/implementation-kickoff"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/implementation-kickoff/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/openai/openai-agents-python/implementation-kickoff"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/implementation-kickoff.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00026 | $0.02219 |
| Opus 5.5 | $0.00010 | $0.00888 |
| Sonnet 5.5 | $0.00005 | $0.00444 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
implementation-kickoff 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Kickoff
Use this skill as the explicit transition from an agreed implementation scope to isolated execution. Keep the user's original checkout and existing branches unchanged, and finish with a clean local branch that is ready for the user to push.
Non-negotiable boundaries
- Treat explicit invocation of this skill as authorization to fetch, create one dedicated worktree, rebase or replay task-owned changes, create the final local branch, stage task-owned files, and create one local commit. It never authorizes push, pull-request creation, or any GitHub mutation.
- Do not start during an investigation-only phase or before a required user approval. Finish planning and any required implementation scope contract first.
- Use read-only GitHub access when remote PR evidence is required.
- Preserve unrelated and user-owned changes. Do not remove an existing worktree or rewrite an existing branch to make room for this workflow.
1. Establish the task boundary
Record the original requirement, success criteria, intended target (origin/main unless the user states otherwise), task-owned paths, compatibility boundary, intentionally unsupported cases, and required repository skills. For a multi-step task, create and maintain the repository's required ExecPlan. An ExecPlan, review packet, ledger, trace, or temporary report is operational-only by default even when repository policy requires creating it; do not add it to the shipped-path manifest unless the original requirement or repository policy explicitly makes that exact path a committed deliverable.
If the current directory is a worktree previously created for this same task in the current conversation, resume it. Otherwise, continue from the user's current checkout only long enough to create a new worktree.
2. Create a detached worktree from current main
- Verify the source checkout's raw status without modifying it.
- Fetch
origin main. If the fetch fails, stop rather than claiming a stale ref is current. - Record the fetched
origin/maincommit. - Choose a unique task-oriented path under the configured Codex worktree root. Check both the filesystem and
git worktree list; never reuse or delete a collision. - Run
git worktree add --detach <worktree> origin/mainand perform all subsequent implementation work there. - Confirm the new worktree is detached at the recorded commit and initially clean.
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 Changed · -2 lines · -87 tokens per session ea2de87c562e
- 1mo ago First seen · 112 lines · 113 tokens per session scan A 8e89a16a21d0
implementation-kickoff is a skill published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,219 once invoked, about $0.0001 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 2026-08-30.
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