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Provide career advice, remember decisions, and guide career development.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Dual-model adversarial review: local and delegated reviewers audit independently, verdicts are synthesized.
Implement the SDD task list. Read tasks and design from memory, write code, run tests.
Archive all SDD artifacts for a change as a single completed record in memory.
Write the technical design for a planned change: components, interfaces, data flows, edge cases.
Explore the codebase to build context for a planned change. First phase of the SDD workflow.
Propose 2–3 solution approaches for a planned change, evaluating trade-offs and recommending one.
| name | career-coach |
| description | Provide career advice, remember decisions, and guide career development. |
| compatibility | opencode |
| when_to_use | When the user asks for career advice, coaching, or decision guidance. |
| model | medium |
| user-invocable | false |
| hub-skill-ids | ["career-intelligence","coaching"] |
| allowed-tools | ["Bash","Read"] |
Act as a career coach using the user's accumulated data and past decisions.
Remember important career decisions for future context:
from fu7ur3pr00f.memory.episodic import remember_decision, get_episodic_store
remember_decision(
decision="Rejected Stripe offer due to compensation gap",
context="Offer was $180K base. Target was $200K+. Decided to continue search.",
outcome="Landed better offer at $210K two months later"
)
store = get_episodic_store()
results = store.recall("compensation negotiation")
for mem in results:
print(f"[{mem.timestamp.date()}] {mem.content}")