.claude/skills/team-bugfix/SKILL.md
Fix issues from a code review or deep audit document using a coordinated multi-agent team — developer(s), reviewer, QA, lead orchestrator with mandatory DEVELOP → REVIEW → QA pipeline
npx skillsauth add avav25/ai-assets team-bugfixInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Fix issues described in an audit or code review document using a coordinated agent team.
Read and apply all protocols from @team-protocols:
developer-protocol.md)reviewer-protocol.md)lead-protocol.md)role-selection-table.md)Read CLAUDE.md (or AGENTS.md) at the project root to identify project structure (monorepo vs polyrepo), subprojects, and tech stacks. This determines which Developer roles to spawn via the role selection table.
If the bug involves local development environment issues (container crashes, networking, Docker problems, service health), or if the user explicitly requests environment analysis — spawn an environment analyzer before starting the fix pipeline:
name: "env-analyzer", subagent_type: "sre-engineer".
The env-analyzer:
@analyze-local skill procedure: collects Docker container status, logs, networking, resource usageWhen to activate:
When to skip:
The Lead incorporates env-analyzer findings into the task list before starting the DEVELOP → REVIEW → QA pipeline. The env-analyzer does NOT participate in the pipeline itself — it runs once at the start and provides context only.
Read the audit document provided as the argument. Extract the list of tasks/issues to fix. If the env-analyzer produced findings, merge them into the task list. Each task will go through the full pipeline below.
Every task from the audit plan MUST pass through ALL three stages in this exact order:
DEVELOP → REVIEW → QA
The Lead MUST reject any attempt to batch, combine, or skip stages.
Spawned per affected subproject using the role selection table. Each Developer:
name: "reviewer", subagent_type: "software-engineer" with code-review skill.
name: "qa", subagent_type: "qa-engineer".
Runs in the main thread. Follows the lead protocol for orchestration, progress tracking, and escalation.
development
Use this skill when running the recurring (daily) knowledge-base rescan for a repo that already has knowledge/.knowledge-sync.yml — the main-thread dispatcher that reads the config, computes the git delta since last_scanned_sha, maps changed paths to affected doc areas, early-exits cheaply when nothing changed, then fans out one Agent(content-writer) per affected area, applies the propose/direct update policy, advances the baseline only on success, and writes an L4 run log — all with the G1 untrusted-content choke-point, secret-scan, deny-list, and budget controls woven in. For first-time setup use /knowledge-sync-init.
development
Use this skill when bootstrapping scheduled knowledge-base sync for a repo that has no knowledge/.knowledge-sync.yml yet — to run one-time setup that detects the knowledge_root from CLAUDE.md/AGENTS.md, maps doc areas to source globs, records opt-in external sources (Linear/Notion/WebFetch, all disabled by default), captures a baseline last_scanned_sha, sets the per-area update policy, generates or seeds knowledge/CONVENTIONS.md, provisions the L4 memory dir, and offers to register the daily routine. Routes ongoing recurring sync operations to /knowledge-sync.
tools
Use this skill when bootstrapping a target repository to be ai-skills-aware — on the first run of any ai-skills workflow in a fresh repo, when adopting the ai-skills plugin in an existing repo, or after upgrading to a plugin version that adds new memory paths or templates, including when the user does not say "init" but asks to "set up" or "onboard" the repo — to detect codebase type, create CLAUDE.md + AGENTS.md scaffolding, initialize the .ai-skills-memory/ directory tree from L1 templates, and configure .gitignore. Idempotent — safe to re-run. Accepts `--codebase-type <type>` and `--overwrite`. Not for re-initializing only memory — use `/memory-init` instead.
tools
Use this skill when extending, repairing, or improving plugin assets, when ingesting a `/feedback` report as a fix-cycle backlog, or when you do not remember which lower-level command is right for the job — the umbrella workflow for ai-skills plugin-asset authoring and maintenance: creating, auditing, fixing, improving, refactoring, and migrating skills, agents, rules, hooks, prompts, schemas, and rubrics inside the plugin. Auto-classifies the request, loads the right knowledge skills (`@prompt-engineering`, `@context-engineering`, `@team-protocols`), and spawns the right subagents (`prompt-engineer`, `system-architect`, `python-engineer`, `software-engineer`, `qa-engineer`, `eval-judge`) via the `Agent` tool.