version: "1.0.1" name: han-feedback description: > Capture structured feedback on the Han skills and agents used in the current session and optionally post it as a GitHub issue to testdouble/han. Use at the end of any session where one or more han- skills or agents ran, to rate a run, log what worked and what didn't, or submit observations for maintainers. Does not review code, investigate bugs, or research options; use code-review, investigate, or research for those. Does not provide feedback on skills or agents from non-Han plugins. allowed-tools: Read, Write, Bash(ls ), Bash(mkdir ), Bash(gh ), Bash(date *)

Project Context

• Today's date: !date +%Y-%m-%d
• personal config directory: !echo "${CLAUDE_CONFIG_DIR:-$HOME/.claude}"
• project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Capture Feedback

Operating Principles

• _The whole han- family is in scope._* Capture skills and agents from every Han plugin (han-core, han-planning,

han-coding, 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, updated in place. There is one feedback file per day per set of components. A file that already

exists for today is updatable, not closed: when the session continued past it, or the user asks for a compiled report, read it and update it in place rather than skipping. Never overwrite what is there. Skip only when nothing new has happened since that file was written.

• 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-planning:, han-coding:, 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-planning:plan-a-feature), messages showing a skill launching (like "Launching skill: han-planning: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, plan-synthesizer, 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 file already covering a component used this session is the file this run updates, not a reason to skip.

Read each matching file, then decide between two paths:

Nothing new has happened. Every component in this session is already covered, and the session produced nothing the existing file does not record: no further runs of those components, no new problems, no correction you have not already written down. Report the existing file paths, say plainly that nothing new happened since they were written, and stop.

Something new has happened. The session continued past that file, a covered component ran again, a new problem surfaced, or the user asked for a compiled report. Update the file in place. Add the new material and preserve everything already there; never overwrite it. Then state the update: name the file you updated and what you added to it, so the user is not left guessing whether their newer session was captured.

A run that continued past an existing file and then skipped is the failure this step exists to prevent. The default is to update.

Step 4: Determine the filename

When Step 3 found a file to update, that file's name is the filename. Keep it as it is, even when this run covers a component the name does not mention; renaming it would break the path already reported to the user.

Otherwise 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-planning: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-planning:plan-a-feature and han-coding: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 each dimension on a 1-to-5 scale. When the reference file from Step 5 exists, reuse its dimensions so ratings stay comparable across runs. When no reference file exists, use this default set, and add or drop a dimension only when the skill type clearly calls for it:

• Output accuracy. Was the produced artifact factually correct and internally consistent?
• Evidence discipline. Did the skill ground its claims in evidence and resolve questions before asking you?
• Finding signal-to-noise. Were the dispatched agents' findings real and worth the turns they cost?
• Output length vs. decision count. Was the artifact proportionate to the decisions it captured?
• Turn efficiency. Did the skill converge without unnecessary rounds or escalations the evidence could have settled?

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

Step 7: Write or update the feedback file

When Step 3 found a file to update, edit that file in place rather than rewriting it. Keep its existing structure and every point already recorded, and fold the new material into the sections it belongs in: new points onto the existing lists, any newly-used skill or agent onto the **Skills used:** and **Agents used:** lines, and a re-scored dimension only where this session's evidence actually changed it. Add an ## Update {TIME} heading before the new material when the session's story changed rather than merely lengthened, so a reader can see what arrived later. Then continue to Step 8.

Otherwise 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 |
| -------------------------------- | ----- |
| Output accuracy                  | {N}/5 |
| Evidence discipline              | {N}/5 |
| Finding signal-to-noise          | {N}/5 |
| Output length vs. decision count | {N}/5 |
| Turn efficiency                  | {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.

When this run updated an existing file, say so in the same message that reports the path: name the file and name what you added to it. "Updated 2026-07-29-plan-a-feature.md with the two escalation problems from this afternoon's run" tells the user their newer session was captured. Reporting only the path leaves them unable to tell an update from a skip.

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 confirmed or the response is ambiguous: confirm the file is saved at ~/.claude/han-feedback/{filename}, provide the ready-to-run command below for manual use after editing, and stop.

gh issue create --repo testdouble/han --title "Han Feedback: {skill-name} ({TODAY})" --body-file $HOME/.claude/han-feedback/{filename}

Step 10: Offer to post as a GitHub issue

Ask: "Ready to post this as a GitHub issue to testdouble/han?"

A clear affirmative is "yes", "go ahead", "post it", or a similar unqualified instruction. Anything else — including "maybe", "not yet", silence, or an ambiguous response — is treated as no.

If yes:

Build {skill-name} for the title from the **Skills used:** field with each plugin namespace stripped (everything up to and including the colon); join multiple short names with hyphens. When no Han skill ran, use the stripped names from the **Agents used:** field instead. Extract {TODAY} from the feedback filename's date component (not the current clock).

Run:

gh issue create --repo testdouble/han --title "Han Feedback: {skill-name} ({TODAY})" --body-file $HOME/.claude/han-feedback/{filename}

If the environment refuses to run the command (the tool call is denied, a permission or sandbox layer blocks it, the command is not permitted in this environment, or the run has no network access): say plainly that the environment refused the publish. Do not describe it as the run declining, choosing not to post, or deciding to skip. Those are three different statements and only the environment's refusal is true, so a user told the run declined goes looking for a decision nobody made.

Do not retry the identical command. A refusal that came from a permission or sandbox layer returns the same answer every time, and a second attempt spends a turn to learn nothing.

Then hand over the command to run by hand, filled in rather than templated, so it can be pasted as it stands:

gh issue create --repo testdouble/han --title "Han Feedback: {skill-name} ({TODAY})" --body-file $HOME/.claude/han-feedback/{filename}

Confirm the file is saved at ~/.claude/han-feedback/{filename} and stop.

If `gh` is not found (command not found or not installed): Report that the gh CLI is not installed. To post manually, visit https://github.com/testdouble/han/issues/new and paste the file contents.

If the command exits with a non-zero code: Display the error message without modification. Confirm the file is saved at ~/.claude/han-feedback/{filename}. Provide the posting command above. If the error contains "auth" or "login", add: "Run gh auth login and retry."

If the command exits successfully but no URL is parseable in the output: Say "The issue was likely created. Check https://github.com/testdouble/han/issues to confirm. Do not retry — running the command again would create a duplicate issue."

If no: Confirm the file is saved at ~/.claude/han-feedback/{filename}. Provide the posting command above for later use.