本文へ移動
cccskills
無料GitHub で公開

plugin-submission

Govern discovery, eligibility, and submission of a plugin or skill to official directories, registries, marketplaces, and curated GitHub lists. Use when preparing or sending plugin listings, marketplace forms, directory PRs, or repository recommendations; require current policy evidence and a final human confirmation before every external submission.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md4.1 KB
  • agents/openai.yaml200 B

SKILL.md(原文)

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。

Plugin Submission

Treat every public listing as an external representation of the project. Submit only an artifact that the destination's current policy accepts, and retain a receipt for every send.

Workflow

  1. Classify the destination and artifact. Determine whether it accepts a full plugin, a repository, or one standalone SKILL.md. Do not describe a multi-skill plugin as one skill, or split a skill bundle without an explicit portable artifact.
  2. Read the current primary policy. Use the destination's official submission instructions or CONTRIBUTING.md; record its URL, access requirements, licence and validation rules, and any limits on automated or AI-assisted submissions.
  3. Validate eligibility. Check the public candidate URL, release/tag, licence as detected by the hosting platform, manifest/schema validity, documentation, and the destination-specific validator. Treat a mismatch or unmet requirement as blocked, not as an invitation to bypass it.
  4. Prepare one destination matrix. For each target, record: destination, artifact, policy evidence, status (ready, blocked, deferred, or human-only), exact payload, and rollback action. Reuse the canonical repository description; minimise personal data and never add an email without explicit permission.
  5. Stage, then confirm. Fill or draft public data only after the operator has approved the destination. Immediately before clicking a submit control, opening a PR, or sending a recommendation, restate the exact target and payload and obtain a final human confirmation. A single confirmation may cover multiple named sends only when the full set and payloads are shown together.
  6. Send through the permitted route. Use a form, PR, issue, or API only when the policy permits that route. Never act where a policy requires a human-authored recommendation, prohibits AI-assisted submissions, or requires credentials/access the operator has not supplied.
  7. Verify and record the receipt. Capture the submission URL/identifier and status. If a listing is rejected or stale, record the reason and next eligible action. Roll back a repository contribution by closing its PR/issue when appropriate; external directory records require the destination's own removal/update path.

Submission Gate

Do not send until all applicable checks pass:

  • The target policy is current and supports the proposed artifact.
  • The public repository and exact release are accessible.
  • Project licence intent and platform-detected licence agree.
  • Required validation has passed, including the destination's validator where available.
  • The project has not overstated supported platforms, usage, security review, or affiliation.
  • The operator has given final confirmation for the exact outgoing entries.

Outputs

  • A destination matrix with evidence URLs and eligibility verdicts.
  • A staged payload for every ready target.
  • Submission receipts, or an explicit blocked/deferred reason and owner.

Anti-rationalisation

TemptationGate
“It is close enough to call the bundle one skill.”Submit the artifact type the directory actually accepts, or create a deliberate portable export first.
“The README says Apache, so platform metadata does not matter.”Directories evaluate the public repository; resolve detected-licence mismatches before broad listing.
“The user asked to submit, so the final click is implied.”A final confirmation binds authority to the exact external payload.
“A PR is harmless discovery.”A public PR is a durable representation of the project and must follow the contributor policy.
“We can automate a human-only form.”Human-only and no-AI policies are hard stops.

レビュー

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。

同じリポジトリのスキル

概要と使いどころ

Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Decompose an epic into atomic parallelizable tasks, route each to the right skill, and keep the four delivery records straight — issues, STATUS, ROADMAP, CHANGELOG. Use as a meta-router when several skills could apply, and as the baseline for how delivery state is recorded. Trigger at the start of any multi-track epic, when the skill count exceeds ~12, or when the records have drifted from reality.

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。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

jpantsjoha のスキルをすべて見る

このスキルの問題を報告する