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Provide career advice, remember decisions, and guide career development.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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}")