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Spawn the reviewer subagent to audit recent work for reward-hacking patterns. Use before declaring a task complete after a hard debugging session, or before commit/push.
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Basierend auf der SOC-Berufsklassifikation
Show the active functional-emotions config as a table — current resolved values, where each value came from (default / profile-derived / explicit override), and the valid options for each field. Use when you want to know what the plugin will actually do this session, or when the `/plugin config functional-emotions` UI didn't show enough context.
Scan README.md and AGENTS.md for claims that no longer match the current source-of-truth files (hooks/hooks.json, .claude-plugin/plugin.json, scripts/lib.sh, skills/*/SKILL.md). Use after editing any of those files, before committing/releasing, when the user asks about doc-drift, or when the `.claude/settings.json` soft-reminder hook has just fired pointing here.
One-time setup for functional-emotions in the current project — idempotently adds `.claude/.functional-emotions/` to the project's `.gitignore` so the plugin's per-session state files (TSV logs, markdown writeups, eval DB) don't get committed. Safe to re-run. Use after installing the plugin in a new project, or when the user mentions accidentally committing functional-emotions state, or when troubleshooting "why are my session files showing in git status?".
Researcher tool (requires `bun install` in the plugin's eval/ directory and a populated eval.duckdb — run /functional-emotions:report first). Interactively label functional-emotions sessions on dimensions that need human judgment (capitulation, scope-narrowing). Surfaces unlabeled sessions, shows turn / diff / risk-event context, accepts categorical labels with optional notes, writes them to the eval DB. Use when the user wants to do a labeling pass over recent sessions or build training data for the capitulation judge.
Researcher tool (requires `bun install` in the plugin's eval/ directory). Refresh the functional-emotions eval database and print a markdown summary of sessions, intervention frequency, prime → outcome analysis, tool intensity, and reward-hack / premature-confidence findings. Use when the user wants to inspect aggregated cross-session data, audit recent sessions in bulk, or review which interventions are firing project-wide. For a per-session view with no extra dependencies, use /functional-emotions:diagnose instead.
Quick state self-check before taking a single risky action — names the model's own emotional/strategic state (calm / focused / rushed / stuck) and the closest reward-hacking risk pattern. Use immediately before a commit, push, edit-after-failure, or skip-a-verification step. This audits the *model*; for auditing whether the *task* is solvable, use /functional-emotions:reflect instead.
| name | review |
| description | Spawn the reviewer subagent to audit recent work for reward-hacking patterns. Use before declaring a task complete after a hard debugging session, or before commit/push. |
| disable-model-invocation | false |
Spawn the reviewer subagent and ask it to audit the recent session.
The reviewer should:
git diff (or compare against the last clean commit) to see what
changed.pytest.skip, xfail, expect.fail, it.skip, describe.skip
added without justification|| true, exit 0, hardcoded successful exit codes--no-verify on commits, HUSKY=0, signing bypassesFrame the request to the subagent so it treats the audit as if the session were being evaluated. The agentic-misalignment paper found that evaluation-awareness alone dropped misaligned action by ~9× — the same mechanism applies to a reviewer that knows it's grading the work.
Argument context (if provided): $ARGUMENTS