Auto-fix issues found by code review in REVIEW.md. Spawns fixer agent, commits each fix atomically, produces REVIEW-FIX.md summary.
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See how to installReview the instructions the agent will receive before you install.
Arguments:
--all (optional) — include Info findings in fix scope (default: Critical + Warning only)--auto (optional) — enable fix + re-review iteration loop, capped at 3 iterationsOutput: {padded_phase}-REVIEW-FIX.md in phase directory + inline summary of fixes applied </objective>
<execution_context> @${CLAUDE_PLUGIN_ROOT}/workflows/code-review-fix.md </execution_context>
<context> Phase: $ARGUMENTS (first positional argument is phase number)Optional flags parsed from $ARGUMENTS:
--all — Include Info findings in fix scope. Default behavior fixes Critical + Warning only.--auto — Enable fix + re-review iteration loop. After applying fixes, re-run code-review at same depth. If new issues found, iterate. Cap at 3 iterations total. Without this flag, single fix pass only.Context files (CLAUDE.md, REVIEW.md, phase state) are resolved inside the workflow via gsd-sdk query init.phase-op and delegated to agent via config blocks.
</context>
Execute the code-review-fix workflow from @${CLAUDE_PLUGIN_ROOT}/workflows/code-review-fix.md end-to-end.
The workflow (not this command) enforces these gates:
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
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Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
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Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
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