Run the mandatory verification stack when changes affect runtime code, tests, examples, or build and test behavior in the OpenAI Guardrails Python repository.
日本語の概要は準備中です。原文の説明を表示しています。
Choose compatibility-aware scope for runtime and API changes in openai-guardrails-python. Use before initial implementation and each review-feedback batch to decide whether to patch, reset the design, preserve compatibility, or reject unsupported cases.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
origin first, and only fall back to local tags when remote tags are unavailable:
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"
Report a local-tag fallback as potentially stale.Record these four items in the plan or working notes, and update them before widening or narrowing the implementation:
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.
Repeat this gate before editing each new feedback batch:
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 unsupported
Classify 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:
guardrails and caller-visible OpenAI client behavior intentionally proxied by the wrapper.GuardrailTripwireTriggered, Agents SDK tripwire behavior, and suppression semantics aligned with their released contracts.None semantics.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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Run the mandatory verification stack when changes affect runtime code, tests, examples, or build and test behavior in the OpenAI Guardrails Python repository.
日本語の概要は準備中です。原文の説明を表示しています。
Review an openai-guardrails-python release plan or final release candidate against the previous remote tag, determine the compatible release type, audit runtime and packaging risk, inspect current CI, and produce an English ship-or-block report. Use for pre-release readiness checks, not ordinary PR review or implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Perform the repository's risk-tiered independent final review before implementation completion. Use only when explicitly invoked or when repository instructions require it after behavior-impacting implementation work; audit the complete task diff, supported contracts, lifecycle and security boundaries, complexity, and tests before final verification.
日本語の概要は準備中です。原文の説明を表示しています。
Start and carry an explicitly invoked openai-guardrails-python implementation through a fresh isolated worktree and a local PR-ready handoff. Fetch the latest origin/main, keep task changes uncommitted, replay them onto the latest main before final review, run applicable verification and $implementation-final-review, use $pr-draft-summary to generate the complete PR draft and branch name, then create one clean local commit with takeover provenance when applicable. Use only when the user explicitly invokes this skill; never push, open a PR, or mutate GitHub.
日本語の概要は準備中です。原文の説明を表示しています。
Assess an openai-guardrails-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence, changes, or closure should be requested.
日本語の概要は準備中です。原文の説明を表示しています。
Create the required PR-ready summary block, branch suggestion, title, and draft description for openai-guardrails-python after runtime, tests, examples, build/test configuration, or behavior-impacting docs change.
日本語の概要は準備中です。原文の説明を表示しています。