PR Design Doc
OpenHands/OpenHands
For a non-trivial pull request, write a self-contained HTML design doc under the temporary .pr/ directory and link a visibility-appropriate preview in the PR description, so maintainers grasp the…
Choose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design.
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install openai/openai-agents-python implementation-strategy --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/implementation-strategy .claude/skills/implementation-strategy && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .claude/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install openai/openai-agents-python implementation-strategy --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/implementation-strategy .agents/skills/implementation-strategy && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .agents/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install openai/openai-agents-python implementation-strategy --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/implementation-strategy .cursor/skills/implementation-strategy && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .cursor/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/openai/openai-agents-python.git --path .agents/skills/implementation-strategy--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install openai/openai-agents-python implementation-strategy --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/implementation-strategy .gemini/skills/implementation-strategy && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .gemini/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install openai/openai-agents-python implementation-strategyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/implementation-strategy .github/skills/implementation-strategy && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .github/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openai/openai-agents-python --skill implementation-strategy -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install openai/openai-agents-python implementation-strategy --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/implementation-strategy .opencode/skills/implementation-strategy && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "implementation-strategy" agent skill from https://github.com/openai/openai-agents-python/tree/main/.agents/skills/implementation-strategy into .opencode/skills/implementation-strategy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementation-strategy", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
implementation-strategyChoose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design.
Implementation Strategy is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Choose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
It sits in Development. It works with OpenAI. The repository describes itself as: A lightweight, powerful framework for multi-agent workflows. The licence is MIT.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 26345c1. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Implementation Strategy loads about 3.7k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 1,878 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from openai/openai-agents-python at commit 26345c1, republished under its MIT licence (© openai). 1,878 words, ~3,685 tokens.
.claude/skills/implementation-strategy/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.origin first, falling back to local tags only when remote tags are unavailable. Reuse an already verified baseline within the task unless new release evidence or changed scope makes it stale:BASE_TAG="$(.agents/skills/final-release-review/scripts/find_latest_release_tag.sh origin 'v*' 2>/dev/null || git tag -l 'v*' --sort=-v:refname | head -n1)"
echo "$BASE_TAG"Record these four items in the existing plan or working notes, and update them before widening or narrowing the implementation. A local fix can use one concise paragraph; mark unaffected dimensions as not applicable rather than inventing unsupported cases or creating another document:
none when absent.If the intentionally unsupported cases cannot be stated clearly, do not start by adding a general resolver. First define a narrower behavior contract. If no adequate supported alternative exists, add one only when the task requires it; do not invent one speculatively.
A released-version reproducer proves reachability, not support. Treat the exact shape as a compatibility requirement only when intentionally covered by public documentation, examples, tests, or typing; required by a durable boundary; or backed by concrete user reliance or maintainer intent. Otherwise record the risk and prefer early rejection with an existing supported alternative.
Use this checkpoint only when feedback changes the scope contract or implementation shape. Reuse unchanged evidence instead of reconstructing it:
Review checkpoint:
- Root cause and required behavior:
- Compatibility evidence and unsupported cases:
- Source of truth:
- Behavior-space change: narrows / unchanged / widens
- Action: focused patch / complexity reset / reject as unsupportedClassify each finding as a required-behavior defect, supported compatibility requirement, another combination of the same implementation dimensions, or unrelated issue. Widening the behavior space requires new contract evidence.
If a second related finding would add another condition, protocol hop, compatibility case, or test permutation to the same abstraction, stop patching and run the complexity reset. Continue only when concrete evidence puts the exact case in the required or supported contract.
After a reset spec is frozen, classify each later finding as a violation of that spec, an evidence-backed reason to revise it, an intentionally unsupported case, or an unrelated issue. Do not resume incremental patching merely because the new finding is locally fixable.
Example: if successive findings require traversing a direct wrapper, partial, nested wrapper, descriptor, and bound method, do not add another hop. Unless arbitrary wrapper graphs are supported, retain the required plain callable behavior and reject ambiguous wrappers before invocation.
main directly unless they already define a supported durable boundary.Stop extending the current design when:
When a trigger fires:
retain, replace, or delete.Use this compact reset spec in the plan or working notes:
Finding-derived reset spec:
- Original required outcome:
- Supported release or durable boundaries:
- Grouped findings and common root cause:
- Invariants across affected entry points:
- Allowed states and behavior:
- Rejected states, failure timing, and side-effect boundary:
- Trusted and untrusted boundaries:
- Single sources of truth:
- Persistence, resume, cleanup, or other lifecycle semantics:
- Non-goals and supported alternatives:
- Representative test categories:
- Diff reset: retain / replace / delete:The candidate spec is a falsifiable design hypothesis, not a record of the current implementation. The audit may correct it before it is frozen. Once frozen, require explicit evidence to revise it and re-run the complete diff mapping after any revision.
Do not wait for the user or reviewer to request this reset when the signals are already present.
Before declaring the design complete, answer all of these with concrete evidence:
RunState, session persistence, and other explicitly durable serialized state as compatibility-sensitive across commits, processes, and machines.strict_feature_validation=True, while keeping the default path compatible through warning, ignoring unsupported data, or a clearly non-empty placeholder.model_settings parameter. Preserve released constructor arguments, typed-object behavior, and provider request payloads when adding dictionary support.None semantics deliberately for public configuration. For example, use separate meanings for "feature disabled or no SDK limit", "use SDK default limits", and "disable only this specific limit" rather than relying on implicit truthiness checks.Confirm a consequential choice below only when it is not already resolved by the user's request or an approved scope contract. Ordinary remediation within that contract continues through review and verification.
When this skill materially affects the implementation approach, state the decision briefly in your reasoning or handoff, for example:
Compatibility boundary: latest release tag v0.x.y; branch-local interface rewrite, no shim needed.Implementation scope contract: support X; preserve Y; reject Z before side effects; use supported alternative W, or none exists.Complexity reset: repeated edge-case combinations show the approach is too broad; redesign from the original requirement instead of adding another branch.Finding-derived reset spec: findings F1-F3 expose invariant X across entry points A-C; freeze that contract, delete unmapped machinery, and review later findings against it.© openai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in .agents/skills/implementation-strategy of openai/openai-agents-python.
Open the folder on GitHubat commit 26345c1
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in openai/openai-agents-python, which our catalogue first saw on October 7, 2026.
Implementation Strategy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Implementation Strategy this skillopenai/openai-agents-python | 30k | — | ~3.7k | Automated safety check: Pass | MIT | |
| PR Design DocOpenHands/OpenHands | 90k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Get API Docs with chubandrewyng/context-hub | 14k | 2 repos | ~775 | Automated safety check: Pass | MIT | |
| Open Code Review CLIalibaba/open-code-review | 44k | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Codexskills-directory/skill-codex | 1.5k | 3 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Changeset Validationopenai/openai-agents-js | 3.9k | — | ~607 | Automated safety check: Pass | MIT |
OpenHands/OpenHands
For a non-trivial pull request, write a self-contained HTML design doc under the temporary .pr/ directory and link a visibility-appropriate preview in the PR description, so maintainers grasp the…
andrewyng/context-hub
Fetches current documentation for third-party APIs and SDKs with the chub CLI before the agent writes code against them, instead of relying on remembered API shapes.
alibaba/open-code-review
Runs the ocr command-line tool to review Git changes, a commit or a branch comparison with an AI model, returning line-level comments and optionally applying fixes.
skills-directory/skill-codex
A skill your agent uses when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
openai/openai-agents-js
Validate changesets in openai-agents-js using LLM judgment against git diffs (including uncommitted local changes).
Waishnav/devspace
Prepares the current DevSpace checkout or worktree for isolated local manual QA, covering QA state seeding, UI asset builds and snapshot resets.
openai/openai-agents-python
Review completed implementation changes before final verification.
openai/openai-agents-python
Audit or fix sensitive-data exposure in Python SDK diagnostics, exceptions, logging, and telemetry.
openai/openai-agents-python
Assess a Python SDK release candidate or release plan against the previous release and recommend ship or block.
openai/openai-agents-python
Prepare a local Python SDK release candidate in a dedicated worktree.
openai/openai-agents-python
Run the required final formatting, lint, type, and test checks after eligible SDK changes pass review.
openai/openai-agents-python
Analyze logs and source from a completed manual examples run.
Works with
Categories
Choose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design. Implementation Strategy is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Choose supported scope and compatibility boundaries for SDK behavior changes; revisit when feedback changes the design.
Implementation Strategy fits situations like: development work in your project.
Run `npx skills add openai/openai-agents-python --skill implementation-strategy -a claude-code`. Or copy the skill folder (.agents/skills/implementation-strategy in openai/openai-agents-python) into .claude/skills/implementation-strategy in your project. Claude Code loads it when a task matches its description.
Run `npx skills add openai/openai-agents-python --skill implementation-strategy -a codex`. Or copy the skill folder (.agents/skills/implementation-strategy in openai/openai-agents-python) into .agents/skills/implementation-strategy in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openai/openai-agents-python --skill implementation-strategy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/implementation-strategy, .gemini/skills/implementation-strategy, .github/skills/implementation-strategy and .opencode/skills/implementation-strategy in your project.
Going by SKILL.md and its folder, Implementation Strategy needs the command-line tools its instructions call (git). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Implementation Strategy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Implementation Strategy: PR Design Doc (OpenHands/OpenHands, 90k stars), Get API Docs with chub (andrewyng/context-hub, 14k stars), Open Code Review CLI (alibaba/open-code-review, 44k stars) and Codex (skills-directory/skill-codex, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
openai (a GitHub organization, an official publisher) maintains it in openai/openai-agents-python, which has 29,896 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 8, 2026.
Source: openai/openai-agents-python on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.