han-feedback

Project Context

• Today's date: !date +%Y-%m-%d

Capture Feedback

Operating Principles

• **The whole han.* family is in scope.** Capture skills and agents from every Han plugin (han.core, han.github, han.reporting, han.feedback, and any future han.* plugin). Skills and agents from non-Han plugins are out of scope.
• Invocations count, not completions. A skill or agent is considered used if it appeared in the session, regardless of whether it finished or was cancelled. Feedback on a partial run is still feedback.
• Agents count even when a skill dispatched them. Most Han agents run because a skill dispatched them. Those agents are still in scope; record which ones contributed so the feedback names where specialist value came from.
• Conservative defaults on posting. The feedback directory is user-space. The posting target is a public GitHub repository. Ambiguous confirmation is treated as a stop, not a go.
• One file per day per run. Do not overwrite existing feedback for today. If a skill or agent is already covered by a file for today, skip it.
• Compacted sessions limit visibility. The skill can only see turns present in the context window. If the session was compacted before running this skill, earlier invocations may not be visible.

Step 1: Identify Han skills and agents used this session

Look back through the conversation for any use of a Han plugin component. A component counts as used if it was invoked, regardless of whether it completed or was cancelled.

Han skills. Look for invocations of skills namespaced to any han. plugin. The namespace is the plugin name followed by a colon: han.core:, han.github:, han.reporting:, han.feedback:, and the same shape for any future han. plugin (treat a bare han: prefix as Han too). Watch for slash-command invocations (like /han.core:plan-a-feature), messages showing a skill launching (like "Launching skill: han.core:plan-a-feature"), and any output that identifies a specific Han skill ran.

Han agents. Look for dispatches of agents from any han.* plugin. For example, an Agent tool call whose subagent_type is han.core:adversarial-security-analyst, or skill output naming a Han agent it launched (evidence-based-investigator, project-manager, risk-analyst, and so on). Record each distinct Han agent that ran, whether a skill dispatched it or it was invoked directly.

Build one list of the Han skills used and one list of the Han agents used. Deduplicate each.

If no Han skill or agent invocations are visible in the current context window, ask the user before stopping: "No Han skill or agent invocations are visible in this context window. If you ran Han skills or agents earlier but the session was compacted, list what you used and I will generate feedback for them." If the user confirms none were used, stop without writing any file.

Step 2: Create the feedback directory if it does not exist

Check whether ~/.claude/han-feedback/ exists by running ls ~/.claude/han-feedback/ 2>/dev/null. If the command fails (directory absent), run mkdir -p ~/.claude/han-feedback/ before proceeding.

Step 3: Check for existing feedback today

Run ls ~/.claude/han-feedback/ 2>/dev/null and identify any files whose name begins with today's date (from Project Context). A skill or agent that already has a feedback file for today is skipped in this run.

If everything used in this session already has a feedback file for today, report the existing file paths and stop.

Step 4: Determine the filename

Compute the filename as {TODAY}-{short-names}.md, where:

• Each component's short name is its plugin namespace stripped (everything up to and including the colon). For example han.core:plan-a-feature becomes plan-a-feature, han.github:post-code-review-to-pr becomes post-code-review-to-pr, and the agent han.core:risk-analyst becomes risk-analyst.
• Join the short names of the skills being processed in this run with hyphens. Skills name the file because they are the unit of work; the agents are recorded inside the file.
• When a session used Han agents directly with no Han skill, use the agent short names instead.
• {TODAY} is today's date from Project Context.

Example: a session with han.core:plan-a-feature and han.core:code-review on 2026-05-29 produces 2026-05-29-plan-a-feature-code-review.md.

Step 5: Read the format reference

Run ls -t ~/.claude/han-feedback/ 2>/dev/null | grep '\.md$' | head -1 to identify the feedback file with the most recent modification time.

If a file is found, read it to confirm the current output structure before writing. If no .md files exist in the directory, skip this step and use the embedded template in Step 7.

Step 6: Gather feedback

Think through the session for each qualifying skill and assess the following.

What worked well: Where did the skill do something noticeably better than doing it manually? Which dispatched Han agents added value, and how? Which findings or decisions from the skill or its agents changed the outcome?

What didn't work: Where did the skill or one of its agents ask a question the evidence could have answered? Where was the output disproportionately long for the decision at hand? Where did you redirect or correct the skill or an agent mid-run?

Overall: One paragraph summarizing the fit for this use case.

Rating: Score across the dimensions used in the reference file from Step 5, or adjust dimensions to fit the skill type when no reference file exists.

For a session that used Han agents directly (no skill), assess the agents the same way.

Step 7: Write the feedback file

Write the file to ~/.claude/han-feedback/{filename} using this structure:

# Han Feedback — {TODAY}

**Skills used:** `han.core:{skill-name}`
**Agents used:** `han.core:{agent-name}`
**Context:** {one sentence describing what you were doing}
**Outcome:** {one sentence describing what was produced}

---

## What worked well

- {point}
- {point}

---

## What didn't work

- {point}
- {point}

---

## Overall

{one paragraph}

---

## Rating

| Dimension | Score |
|---|---|
| {dimension} | {N}/5 |

List every Han skill used on the Skills used: line and every Han agent used on the Agents used: line, each with its full plugin namespace (for example han.github:update-pr-description, han.core:risk-analyst). If no Han agents ran, write Agents used: none.

Keep it honest and specific. Generic praise or criticism is not useful. Cite concrete moments from the session.

If the write fails, tell the user: "The write failed. The file was being written to $HOME/.claude/han-feedback/{filename}. Run ls ~/.claude/han-feedback/ and delete any file at that path before retrying." Do not proceed to the checklist or posting steps.

Step 8: Verify the file is non-empty

Check that the written file contains content beyond whitespace. If the file is empty or whitespace-only, notify the user and stop. Do not proceed to the sensitive-content checklist.

Step 9: Review for sensitive content

Display the full content of the written file. Then present this checklist and ask the user to confirm, in a single response, that the content contains none of the following:

• Personal identifiers (names, emails, personal details)
• Internal operational details (team structure, business processes, or organization-specific internal systems — Han skill and agent names are fine, they are publicly documented open-source tools)
• Client-specific information (project names, client work content, proprietary context)

A clear affirmative is "yes", "correct", "looks clean", or a similar unqualified confirmation. A response like "I think so", "probably", "seems fine", or any ambiguous answer is not a clear affirmative — treat it as sensitive content present.

If the response is a clear affirmative: proceed to Step 10.

**If sensitive content is