Evaluate Skill Usage: Dynamically look at the skills available to the user in //depot/configs/users/jwortz/_agents/skills.json, ~/.gemini/skills/, and ~/my-skills/ to see what the agent has access to. Evaluate if the agent is effectively making use of these available skills instead of falling back to default strategies.
Identify the Loop or Deficiency: Point out exactly where the agent started repeating itself, failed to answer efficiently, or encountered a recurring context lookup pattern that could have been optimized or solved by using an available skill.
Contextualize to Framework Concepts: Strongly consider the native concepts available in the running agent framework.
- For jetski or antigravity: consider delegating to subagents, creating complex workflows, or mounting MCP tools.
- For claude_code or gemini_cli (Python): consider native configuration hooks, bash scripts, or packaging workflows as formal extensions (see geminicli.com/extensions).
- Did the agent lack a tool? Did it misinterpret a command?
Propose the Fix or Pattern:
- If an existing skill can be improved, propose an exact addition to that skill's SKILL.md file.
- If a brand new skill is warranted based on a repeating pattern, use the skill-creator patterns to formulate it. When doing so, embed a JSON block formatted exactly like this:
```
{"new_skill_name": "my-new-skill", "new_skill_content": "YAML frontmatter and markdown instructions..."}
```
- Propose an evaluation (an evals.yaml entry) to ensure this new behavior works in the future.
Verify Your Suggestion: To ensure your proposed fix and evaluation can be run, keep this guideline in mind: "Read learning/gemini/agents/skills/run_skill_eval/SKILL.md to learn how to run a skill evaluation".

Keep your response concise. Output your findings as a structured JSON object or a clear Markdown summary containing:

root_cause
wasted_turns
skill_to_update (or proposed_new_skill)
proposed_skill_markdown
proposed_eval_yaml

dream-analyzer