implementation-kickoff

A workflow for starting an implementation in the openai-agents-python repository from a separate Git worktree, which is an isolated working copy of a repository.

In plain words
What is it for?
Use it after the implementation scope is agreed to create an isolated branch, replay changes onto updated main, run checks, and make a local PR-ready commit.
Why use it?
It keeps the original checkout and existing branches unchanged while preparing task changes for review against the latest main branch.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/openai/openai-agents-python/implementation-kickoff
Any agent
npx skills add openai/openai-agents-python --skill implementation-kickoff
Clone the repo
git clone --depth 1 https://github.com/openai/openai-agents-python

Made for: Claude Code, Codex.

Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,372 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00113 $0.02372
Opus 5 $0.00056 $0.01186
Sonnet 5 $0.00023 $0.00474
Haiku 4.5 $0.00011 $0.00237

Measured 2d ago against content hash 8e89a16a21d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/test_validate_handoff.py, scripts/validate_handoff.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/implementation-kickoff/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 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

  1. Verify the source checkout's raw status without modifying it.
  2. Fetch origin main. If the fetch fails, stop rather than claiming a stale ref is current.
  3. Record the fetched origin/main commit.
  4. 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.
  5. Run git worktree add --detach <worktree> origin/main and perform all subsequent implementation work there.
  6. Confirm the new worktree is detached at the recorded commit and initially clean.

Read the full file on GitHub · 112 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 112 lines · 113 tokens per session scan A 8e89a16a21d0

Subscribe to this mod's changes

implementation-kickoff is a skill published in the GitHub repository openai/openai-agents-python (29,075 stars, last pushed 4d ago), licensed MIT. It adds 113 tokens to every session and 2,372 once invoked, about $0.0006 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 2026-08-30.