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Analyze skill gaps, career alignment, and market fit using ChromaDB knowledge.
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Basado en la clasificación ocupacional SOC
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-analyze |
| description | Analyze skill gaps, career alignment, and market fit using ChromaDB knowledge. |
| compatibility | opencode |
| when_to_use | When the user wants career analysis, skill gap assessment, or market positioning. |
| model | high |
| user-invocable | false |
| hub-skill-ids | ["career-intelligence","analysis"] |
| allowed-tools | ["Bash","Read","WebFetch"] |
Analyze career data from the knowledge base against target roles and market data.
Career data must be gathered first. Check knowledge base:
from fu7ur3pr00f.memory.knowledge import get_knowledge_store
store = get_knowledge_store()
stats = store.get_stats()
If stats["total_chunks"] == 0, tell the user to run /gather first.
from fu7ur3pr00f.utils.data_loader import load_career_data_for_analysisfrom fu7ur3pr00f.memory.profile import load_profile## Skill Gap Analysis: [Target Role]
### Current Skills
[from profile + knowledge base]
### Required Skills
[from market research]
### Gaps
- Missing: [list]
- Emerging: [list]
### Recommendations
1. [actionable step]
2. [actionable step]