Calibrate: In-Session Self-Improvement

What This Does

Reviews the current conversation and suggests specific updates to skills, CLAUDE.md rules, memory, or workflows based on what just happened. You pick which suggestions to apply.

When to Use

• End of a work session
• After a task with multiple corrections
• When you say "calibrate", "what can you improve", "tune up", "update your skills"

Process

Step 1: Scan the Conversation

Review everything in the current session. Look for:

• Corrections — where the user said "no", "wrong", "not that", "I meant X"
• Preferences revealed — format choices, tone adjustments, workflow preferences
• Process gaps — steps you missed that a skill should encode
• Repeated patterns — same type of correction more than once = systemic issue
• Things that worked — approaches the user confirmed or accepted without pushback

Step 2: Match to Updateable Targets

For each finding, identify WHERE the fix belongs:

• Skill file — if the issue is about how a specific task is executed
• CLAUDE.md — if the issue is about general agent behavior or rules
• Memory — if it's a preference or context that should persist across sessions
• Workflow/cron — if it's about when or how automated tasks run
• SOUL.md / persona file — if it's about tone or writing style (rare, check with the user)

Step 3: Present Suggestions

Format as a numbered list. Each suggestion should be:

[number]. [TARGET: filename] — [what to change]
Brief why: [one sentence explaining the pattern that triggered this]

Example:

1. SKILL: thumbnail-skill — Add rule to always generate 3 thumbnail variations, not 1
Brief why: You asked for alternatives twice this session after I gave a single option.

2. CLAUDE.md — Add "never suggest routing to other agents" to red lines
Brief why: You corrected me when I suggested handing off to another agent.

3. MEMORY — Save preference: user prefers kebab-case for all file names
Brief why: You renamed two files I created in camelCase.

Rules for suggestions:

• Max 7 suggestions. If you find more, prioritize by impact (repeated corrections > one-offs).
• Be specific. Not "improve writing quality" but "add rule: no sentences over 20 words in community posts."
• Only suggest changes the data supports. Don't pad with generic improvements.
• If nothing meaningful to update, say so. "Clean session — nothing to calibrate" is a valid output.

Step 4: Apply

Wait for the user to respond with which numbers to apply (e.g. "do 1 and 3", "all", "skip 2").

Then:

1. Make the changes to the target files
2. Confirm each change with the file path and a one-line summary
3. If updating a skill, re-read it after editing to verify it's coherent
4. If updating memory, follow the memory system rules (write file + update MEMORY.md index)

Step 5: Done

No follow-up needed. Changes are live for the next time the skill/rule is invoked.

What NOT to Suggest

• Changes that are already in the relevant skill or CLAUDE.md (check first)
• Generic "best practices" not grounded in this session's data
• Temporary fixes for one-off situations
• Changes to other agents' files unless this agent has write access