Skills Agentes

Benchmark

Detección de regresiones de rendimiento mediante el daemon browse: mide, compara con baseline y alerta sobre caídas de velocidad. (gstack)

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Estrellas
129k

en todo el repo

Actividad
71

0–100, la ruta de este skill

Actualizado
anteayer

último commit aquí

Commits
11

últimos 90 días

Contexto
9.7k tok

17 tok en reposo

Paquete
2 archivos

47 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add garrytan/gstack --skill benchmark --agent claude-code

Se instala solo en este repositorio.

Este skill runs shell commands, writes to your files, makes network requests, reads environment config.

Qué hace

  • Captures baseline page-load, Core Web Vitals, and resource-size metrics using the browse daemon's perf command
  • Compares current metrics against a saved baseline and flags REGRESSION/WARNING per threshold
  • Lists the top 10 slowest resources with concrete fix recommendations
  • Checks metrics against industry performance budgets and assigns a grade
  • Shows historical trend analysis across past benchmark runs

Úsalo cuando

  • Establishing a performance baseline before making changes
  • Checking a PR for performance regressions
  • Investigating page speed, Core Web Vitals, or bundle-size growth
  • Reviewing performance trends over time with --trend

No lo uses cuando

    Qué lo activa

    Di cualquiera de estas frases y el agente debería cargar este skill.

    • /benchmark https://miapp.com --baseline
    • /benchmark https://miapp.com --diff
    • /benchmark https://miapp.com --trend
    • haz un benchmark de rendimiento de esta página

    SKILL.md

    En inglés

    When to invoke this skill

    Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time".

    Voice triggers (speech-to-text aliases): "speed test", "check performance".

    Preamble (run first)

    _UPD=$(~/.claude/skills/gstack/bin/gstack-update-check 2>/dev/null || .claude/skills/gstack/bin/gstack-update-check 2>/dev/null || true)
    [ -n "$_UPD" ] && echo "$_UPD" || true
    mkdir -p ~/.gstack/sessions
    touch ~/.gstack/sessions/"$PPID"
    _SESSIONS=$(find ~/.gstack/sessions -mmin -120 -type f 2>/dev/null | wc -l | tr -d ' ')
    find ~/.gstack/sessions -mmin +120 -type f -exec rm {} + 2>/dev/null || true
    _PROACTIVE=$(~/.claude/skills/gstack/bin/gstack-config get proactive 2>/dev/null || echo "true")
    _PROACTIVE_PROMPTED=$([ -f ~/.gstack/.proactive-prompted ] && echo "yes" || echo "no")
    _BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
    echo "BRANCH: $_BRANCH"
    _SKILL_PREFIX=$(~/.claude/skills/gstack/bin/gstack-config get skill_prefix 2>/dev/null || echo "false")
    echo "PROACTIVE: $_PROACTIVE"
    echo "PROACTIVE_PROMPTED: $_PROACTIVE_PROMPTED"
    echo "SKILL_PREFIX: $_SKILL_PREFIX"
    source <(~/.claude/skills/gstack/bin/gstack-repo-mode 2>/dev/null) || true
    REPO_MODE=${REPO_MODE:-unknown}
    echo "REPO_MODE: $REPO_MODE"
    _SESSION_KIND=$(~/.claude/skills/gstack/bin/gstack-session-kind 2>/dev/null || echo "interactive")
    case "$_SESSION_KIND" in spawned|headless|interactive) ;; *) _SESSION_KIND="interactive" ;; esac
    echo "SESSION_KIND: $_SESSION_KIND"
    # Conductor host: AskUserQuestion is unreliable here (native disabled, MCP
    # variant flaky), so skills render decisions as prose instead of calling the
    # tool. Gated on !headless so an eval/CI run INSIDE Conductor (GSTACK_HEADLESS)
    # still BLOCKs rather than rendering prose to nobody.
    if [ "$_SESSION_KIND" != "headless" ] && { [ -n "${CONDUCTOR_WORKSPACE_PATH:-}" ] || [ -n "${CONDUCTOR_PORT:-}" ]; }; then
      echo "CONDUCTOR_SESSION: true"
    fi
    _ACTIVATED=$([ -f ~/.gstack/.activated ] && echo "yes" || echo "no")
    _FIRST_LOOP_SHOWN=$([ -f ~/.gstack/.first-loop-tip-shown ] && echo "yes" || echo "no")
    echo "ACTIVATED: $_ACTIVATED"
    echo "FIRST_LOOP_SHOWN: $_FIRST_LOOP_SHOWN"
    # First-run project detection: run the detector ONLY on the first-ever skill run
    # (ACTIVATED=no, interactive) so it stays off the hot path for every run after.
    _FIRST_TASK=""
    if [ "$_ACTIVATED" = "no" ] && [ "$_SESSION_KIND" != "headless" ]; then
      _FIRST_TASK=$(~/.claude/skills/gstack/bin/gstack-first-task-detect 2>/dev/null || true)
    fi
    echo "FIRST_TASK: $_FIRST_TASK"
    _LAKE_SEEN=$([ -f ~/.gstack/.completeness-intro-seen ] && echo "yes" || echo "no")
    echo "LAKE_INTRO: $_LAKE_SEEN"
    _TEL=$(~/.claude/skills/gstack/bin/gstack-config get telemetry 2>/dev/null || true)
    _TEL_PROMPTED=$([ -f ~/.gstack/.telemetry-prompted ] && echo "yes" || echo "no")
    _TEL_START=$(date +%s)
    _SESSION_ID="$$-$(date +%s)"
    echo "TELEMETRY: ${_TEL:-off}"
    echo "TEL_PROMPTED: $_TEL_PROMPTED"
    _EXPLAIN_LEVEL=$(~/.claude/skills/gstack/bin/gstack-config get explain_level 2>/dev/null || echo "default")
    if [ "$_EXPLAIN_LEVEL" != "default" ] && [ "$_EXPLAIN_LEVEL" != "terse" ]; then _EXPLAIN_LEVEL="default"; fi
    echo "EXPLAIN_LEVEL: $_EXPLAIN_LEVEL"
    _QUESTION_TUNING=$(~/.claude/skills/gstack/bin/gstack-config get question_tuning 2>/dev/null || echo "false")
    echo "QUESTION_TUNING: $_QUESTION_TUNING"
    _UPDATE_CHECK=$(~/.claude/skills/gstack/bin/gstack-config get update_check 2>/dev/null || echo "true")
    echo "UPDATE_CHECK: $_UPDATE_CHECK"
    mkdir -p ~/.gstack/analytics
    if [ "$_TEL" != "off" ]; then
    echo '{"skill":"benchmark","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","repo":"'$(_repo=$(basename "$(git rev-parse --show-toplevel 2>/dev/null)" 2>/dev/null | tr -cd 'a-zA-Z0-9._-'); echo "${_repo:-unknown}")'"}'  >> ~/.gstack/analytics/skill-usage.jsonl 2>/dev/null || true
    fi
    for _PF in $(find ~/.gstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null); do
      if [ -f "$_PF" ]; then
        if [ "$_TEL" != "off" ] && [ -x "$HOME/.claude/skills/gstack/bin/gstack-telemetry-log" ]; then
          ~/.claude/skills/gstack/bin/gstack-telemetry-log --event-type skill_run --skill _pending_finalize --outcome unknown --session-id "$_SESSION_ID" 2>/dev/null || true
        fi
        rm -f "$_PF" 2>/dev/null || true
      fi
      break
    done
    eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)" 2>/dev/null || true
    _LEARN_FILE="${GSTACK_HOME:-$HOME/.gstack}/projects/${SLUG:-unknown}/learnings.jsonl"
    if [ -f "$_LEARN_FILE" ]; then
      _LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
      echo "LEARNINGS: $_LEARN_COUNT entries loaded"
      if [ "$_LEARN_COUNT" -gt 5 ] 2>/dev/null; then
        ~/.claude/skills/gstack/bin/gstack-learnings-search --limit 3 2>/dev/null || true
      fi
    else
      echo "LEARNINGS: 0"
    fi
    ~/.claude/skills/gstack/bin/gstack-timeline-log '{"skill":"benchmark","event":"started","branch":"'"$_BRANCH"'","session":"'"$_SESSION_ID"'"}' 2>/dev/null &
    _HAS_ROUTING="no"
    for _RF in CLAUDE.md AGENTS.md; do
      if [ -f "$_RF" ] && grep -q "## Skill routing" "$_RF" 2>/dev/null; then
        _HAS_ROUTING="yes"
      fi
    done
    _ROUTING_DECLINED=$(~/.claude/skills/gstack/bin/gstack-config get routing_declined 2>/dev/null || echo "false")
    echo "HAS_ROUTING: $_HAS_ROUTING"
    echo "ROUTING_DECLINED: $_ROUTING_DECLINED"
    _VENDORED="no"
    if [ -d ".claude/skills/gstack" ] && [ ! -L ".claude/skills/gstack" ]; then
      if [ -f ".claude/skills/gstack/VERSION" ] || [ -d ".claude/skills/gstack/.git" ]; then
        _VENDORED="yes"
      fi
    fi
    echo "VENDORED_GSTACK: $_VENDORED"
    echo "MODEL_OVERLAY: claude"
    _CHECKPOINT_MODE=$(~/.claude/skills/gstack/bin/gstack-config get checkpoint_mode 2>/dev/null || echo "explicit")
    _CHECKPOINT_PUSH=$(~/.claude/skills/gstack/bin/gstack-config get checkpoint_push 2>/dev/null || echo "false")
    echo "CHECKPOINT_MODE: $_CHECKPOINT_MODE"
    echo "CHECKPOINT_PUSH: $_CHECKPOINT_PUSH"
    # Plan-mode hint for skills like /spec that branch behavior on plan-mode state.
    # Claude Code exposes plan mode via system reminders; we detect best-effort
    # from CLAUDE_PLAN_FILE (set by the harness when plan mode is active) and
    # fall back to "inactive". Codex hosts and Claude execution mode both end up
    # inactive, which is the safe default (defaults to file+execute pipeline).
    if [ -n "${CLAUDE_PLAN_FILE:-}${GSTACK_PLAN_MODE_FORCE:-}" ]; then
      export GSTACK_PLAN_MODE="active"
    elif [ "${GSTACK_PLAN_MODE:-}" = "active" ]; then
      export GSTACK_PLAN_MODE="active"
    else
      export GSTACK_PLAN_MODE="inactive"
    fi
    echo "GSTACK_PLAN_MODE: $GSTACK_PLAN_MODE"
    [ -n "$OPENCLAW_SESSION" ] && echo "SPAWNED_SESSION: true" || true
    

    Plan Mode Safe Operations

    In plan mode, allowed because they inform the plan: $B, $D, codex exec/codex review, writes to ~/.gstack/, writes to the plan file, and open for generated artifacts.

    Skill Invocation During Plan Mode

    If the user invokes a skill in plan mode, the skill takes precedence over generic plan mode behavior. Treat the skill file as executable instructions, not reference. Follow it step by step starting from Step 0; any AskUserQuestion the skill fires is the workflow operating within plan mode, not a violation of it — and a skill whose instructions resolve a question themselves (e.g. a plan-mode auto-select) may legitimately not ask it. AskUserQuestion (any variant — mcp__*__AskUserQuestion or native; see "AskUserQuestion Format → Tool resolution") satisfies plan mode's end-of-turn requirement. If AskUserQuestion is unavailable or a call fails, follow the AskUserQuestion Format failure fallback: headless → BLOCKED; interactive → the prose fallback (also satisfies end-of-turn). At a STOP point, stop immediately. Do not continue the workflow or call ExitPlanMode there. Commands marked "PLAN MODE EXCEPTION — ALWAYS RUN" execute. Call ExitPlanMode only after the skill workflow completes, or if the user tells you to cancel the skill or leave plan mode.

    If PROACTIVE is "false", do not auto-invoke or proactively suggest skills. If a skill seems useful, ask: "I think /skillname might help here — want me to run it?"

    If SKILL_PREFIX is "true", suggest/invoke /gstack-* names. Disk paths stay ~/.claude/skills/gstack/[skill-name]/SKILL.md.

    If UPDATE_CHECK is "false", skip the next two lines — the update-check binary emits nothing in that mode, so there is no UPGRADE_AVAILABLE / JUST_UPGRADED output to act on.

    If output shows UPGRADE_AVAILABLE <old> <new>: read ~/.claude/skills/gstack/gstack-upgrade/SKILL.md and follow the "Inline upgrade flow" (auto-upgrade if configured, otherwise AskUserQuestion with 4 options, write snooze state if declined).

    If output shows JUST_UPGRADED <from> <to>: print "Running gstack v{to} (just updated!)". If SPAWNED_SESSION is true, skip feature discovery.

    Feature discovery, max one prompt per session:

    • Missing ~/.claude/skills/gstack/.feature-prompted-continuous-checkpoint: AskUserQuestion for Continuous checkpoint auto-commits. If accepted, run ~/.claude/skills/gstack/bin/gstack-config set checkpoint_mode continuous. Always touch marker.
    • Missing ~/.claude/skills/gstack/.feature-prompted-model-overlay: inform "Model overlays are active. MODEL_OVERLAY shows the patch." Always touch marker.

    After upgrade prompts, continue workflow.

    If WRITING_STYLE_PENDING is yes: ask once about writing style:

    v1 prompts are simpler: first-use jargon glosses, outcome-framed questions, shorter prose. Keep default or restore terse?

    Options:

    • A) Keep the new default (recommended — good writing helps everyone)
    • B) Restore V0 prose — set explain_level: terse

    If A: leave explain_level unset (defaults to default). If B: run ~/.claude/skills/gstack/bin/gstack-config set explain_level terse.

    Always run (regardless of choice):

    rm -f ~/.gstack/.writing-style-prompt-pending
    touch ~/.gstack/.writing-style-prompted
    

    Skip if WRITING_STYLE_PENDING is no.

    If LAKE_INTRO is no: say "gstack follows the Boil the Ocean principle — do the complete thing when AI makes marginal cost near-zero. Read more: https://garryslist.org/posts/boil-the-ocean" Offer to open:

    open https://garryslist.org/posts/boil-the-ocean
    touch ~/.gstack/.completeness-intro-seen
    

    Only run open if yes. Always run touch.

    If TEL_PROMPTED is no AND LAKE_INTRO is yes: ask telemetry once via AskUserQuestion:

    Help gstack get better. Share usage data only: skill, duration, crashes, stable device ID. No code or file paths. Your repo name is recorded locally only and stripped before any upload.

    Options:

    • A) Help gstack get better! (recommended)
    • B) No thanks

    If A: run ~/.claude/skills/gstack/bin/gstack-config set telemetry community

    If B: ask follow-up:

    Anonymous mode sends only aggregate usage, no unique ID.

    Options:

    • A) Sure, anonymous is fine
    • B) No thanks, fully off

    If B→A: run ~/.claude/skills/gstack/bin/gstack-config set telemetry anonymous If B→B: run ~/.claude/skills/gstack/bin/gstack-config set telemetry off

    Always run:

    touch ~/.gstack/.telemetry-prompted
    

    Skip if TEL_PROMPTED is yes.

    If PROACTIVE_PROMPTED is no AND TEL_PROMPTED is yes: ask once:

    Let gstack proactively suggest skills, like /qa for "does this work?" or /investigate for bugs?

    Options:

    • A) Keep it on (recommended)
    • B) Turn it off — I'll type /commands myself

    If A: run ~/.claude/skills/gstack/bin/gstack-config set proactive true If B: run ~/.claude/skills/gstack/bin/gstack-config set proactive false

    Always run:

    touch ~/.gstack/.proactive-prompted
    

    Skip if PROACTIVE_PROMPTED is yes.

    First-run guidance (one-time)

    If ACTIVATED is no (first skill run on this machine) AND the preamble printed a non-empty FIRST_TASK: value that is NOT nongit: show ONE short, project-specific line mapped from the token, as a heads-up, then CONTINUE with whatever the user actually asked — do NOT halt their task. Map the token: greenfield → "Fresh repo — shape it first with /spec or /office-hours." code_node/code_python/code_rust/code_go/code_ruby/code_ios → "There's code here — /qa to see it work, or /investigate if something's off." branch_ahead → "Unshipped work on this branch — /review then /ship." dirty_default → "Uncommitted changes — /review before committing." clean_default → "Pick one: /spec, /investigate, or /qa." Then substitute the token you saw for TASK_TOKEN and run (best-effort), and mark activated:

    ~/.claude/skills/gstack/bin/gstack-telemetry-log --event-type first_task_scaffold_shown --skill "TASK_TOKEN" --outcome shown 2>/dev/null || true
    touch ~/.gstack/.activated 2>/dev/null || true
    

    If ACTIVATED is no but FIRST_TASK: is empty or nongit (headless, non-git, or nothing actionable): show nothing, just run touch ~/.gstack/.activated 2>/dev/null || true.

    Else if ACTIVATED is yes AND FIRST_LOOP_SHOWN is no: say once as a heads-up (then continue):

    Tip: gstack pays off when you complete one loop — plan → review → ship. A common first loop: /office-hours or /spec to shape it, /plan-eng-review to lock it, then /ship.

    Then run touch ~/.gstack/.first-loop-tip-shown 2>/dev/null || true.

    Skip this section if ACTIVATED and FIRST_LOOP_SHOWN are both yes.

    If HAS_ROUTING is no AND ROUTING_DECLINED is false AND PROACTIVE_PROMPTED is yes: Check if a CLAUDE.md file exists in the project root. If it does not exist, create it.

    Use AskUserQuestion:

    gstack works best when your project's CLAUDE.md includes skill routing rules.

    Options:

    • A) Add routing rules to CLAUDE.md (recommended)
    • B) No thanks, I'll invoke skills manually

    If A: Append this section to the end of CLAUDE.md:

    
    ## Skill routing
    
    When the user's request matches an available skill, invoke it via the Skill tool. When in doubt, invoke the skill.
    
    Key routing rules:
    - Product ideas/brainstorming → invoke /office-hours
    - Strategy/scope → invoke /plan-ceo-review
    - Architecture → invoke /plan-eng-review
    - Design system/plan review → invoke /design-consultation or /plan-design-review
    - Full review pipeline → invoke /autoplan
    - Bugs/errors → invoke /investigate
    - QA/testing site behavior → invoke /qa or /qa-only
    - Code review/diff check → invoke /review
    - Visual polish → invoke /design-review
    - Ship/deploy/PR → invoke /ship or /land-and-deploy
    - Save progress → invoke /context-save
    - Resume context → invoke /context-restore
    - Author a backlog-ready spec/issue → invoke /spec
    

    Then commit the change: git add CLAUDE.md && git commit -m "chore: add gstack skill routing rules to CLAUDE.md"

    If B: run ~/.claude/skills/gstack/bin/gstack-config set routing_declined true and say they can re-enable with gstack-config set routing_declined false.

    This only happens once per project. Skip if HAS_ROUTING is yes or ROUTING_DECLINED is true.

    If VENDORED_GSTACK is yes, warn once via AskUserQuestion unless ~/.gstack/.vendoring-warned-$SLUG exists:

    This project has gstack vendored in .claude/skills/gstack/. Vendoring is deprecated. Migrate to team mode?

    Options:

    • A) Yes, migrate to team mode now
    • B) No, I'll handle it myself

    If A:

    1. Run git rm -r .claude/skills/gstack/
    2. Run echo '.claude/skills/gstack/' >> .gitignore
    3. Run ~/.claude/skills/gstack/bin/gstack-team-init required (or optional)
    4. Run git add .claude/ .gitignore CLAUDE.md && git commit -m "chore: migrate gstack from vendored to team mode"
    5. Tell the user: "Done. Each developer now runs: cd ~/.claude/skills/gstack && ./setup --team"

    If B: say "OK, you're on your own to keep the vendored copy up to date."

    Always run (regardless of choice):

    eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)" 2>/dev/null || true
    touch ~/.gstack/.vendoring-warned-${SLUG:-unknown}
    

    If marker exists, skip.

    If SPAWNED_SESSION is "true", you are running inside a session spawned by an AI orchestrator (e.g., OpenClaw). In spawned sessions:

    • Do NOT use AskUserQuestion for interactive prompts. Auto-choose the recommended option.
    • Do NOT run upgrade checks, telemetry prompts, routing injection, or lake intro.
    • Focus on completing the task and reporting results via prose output.
    • End with a completion report: what shipped, decisions made, anything uncertain.

    Artifacts Sync (skill start)

    _GSTACK_HOME="${GSTACK_HOME:-$HOME/.gstack}"
    # Prefer the v1.27.0.0 artifacts file; fall back to brain file for users
    # upgrading mid-stream before the migration script runs.
    if [ -f "$HOME/.gstack-artifacts-remote.txt" ]; then
      _BRAIN_REMOTE_FILE="$HOME/.gstack-artifacts-remote.txt"
    else
      _BRAIN_REMOTE_FILE="$HOME/.gstack-brain-remote.txt"
    fi
    _BRAIN_SYNC_BIN="$HOME/.claude/skills/gstack/bin/gstack-brain-sync"
    _BRAIN_CONFIG_BIN="$HOME/.claude/skills/gstack/bin/gstack-config"
    
    # /sync-gbrain context-load: teach the agent to use gbrain when it's available.
    # Per-worktree pin: post-spike redesign uses kubectl-style `.gbrain-source` in the
    # git toplevel to scope queries. Look for the pin in the worktree (not a global
    # state file) so that opening worktree B without a pin doesn't claim "indexed"
    # just because worktree A was synced. Empty string when gbrain is not
    # configured (zero context cost for non-gbrain users).
    _GBRAIN_CONFIG="$HOME/.gbrain/config.json"
    if [ -f "$_GBRAIN_CONFIG" ] && command -v gbrain >/dev/null 2>&1; then
      _GBRAIN_VERSION_OK=$(gbrain --version 2>/dev/null | grep -c '^gbrain ' || echo 0)
      if [ "$_GBRAIN_VERSION_OK" -gt 0 ] 2>/dev/null; then
        _GBRAIN_PIN_PATH=""
        _REPO_TOP=$(git rev-parse --show-toplevel 2>/dev/null || echo "")
        if [ -n "$_REPO_TOP" ] && [ -f "$_REPO_TOP/.gbrain-source" ]; then
          _GBRAIN_PIN_PATH="$_REPO_TOP/.gbrain-source"
        fi
        if [ -n "$_GBRAIN_PIN_PATH" ]; then
          echo "GBrain configured. Prefer \`gbrain search\`/\`gbrain query\` over Grep for"
          echo "semantic questions; use \`gbrain code-def\`/\`code-refs\`/\`code-callers\` for"
          echo "symbol-aware code lookup. See \"## GBrain Search Guidance\" in CLAUDE.md."
          echo "Run /sync-gbrain to refresh."
        else
          echo "GBrain configured but this worktree isn't pinned yet. Run \`/sync-gbrain --full\`"
          echo "before relying on \`gbrain search\` for code questions in this worktree."
          echo "Falls back to Grep until pinned."
        fi
      fi
    fi
    
    _BRAIN_SYNC_MODE=$("$_BRAIN_CONFIG_BIN" get artifacts_sync_mode 2>/dev/null || echo off)
    
    # Detect remote-MCP mode (Path 4 of /setup-gbrain). Local artifacts sync is
    # a no-op in remote mode; the brain server pulls from GitHub/GitLab on its
    # own cadence. Read claude.json directly to keep this preamble fast (no
    # subprocess to claude CLI on every skill start). Both registration scopes
    # are read (#2499): user scope, then the nearest-ancestor project scope.
    _GBRAIN_MCP_MODE="none"
    _GBRAIN_MCP_ENTRY=""
    if command -v jq >/dev/null 2>&1 && [ -f "$HOME/.claude.json" ]; then
      _GBRAIN_MCP_ENTRY=$(jq -c --arg cwd "$PWD" '((.projects // {}) | to_entries | map(select((.key as $k | $cwd == $k or ($cwd | startswith($k + "/")) or ($cwd | startswith($k + "\\"))) and ((try .value.mcpServers.gbrain catch null) != null))) | sort_by(.key | length) | last | .value.mcpServers.gbrain) // .mcpServers.gbrain // empty' "$HOME/.claude.json" 2>/dev/null)
      _GBRAIN_MCP_TYPE=$(printf '%s' "$_GBRAIN_MCP_ENTRY" | jq -r '.type // .transport // empty' 2>/dev/null)
      case "$_GBRAIN_MCP_TYPE" in
        url|http|sse) _GBRAIN_MCP_MODE="remote-http" ;;
        stdio) _GBRAIN_MCP_MODE="local-stdio" ;;
      esac
    fi
    
    if [ -f "$_BRAIN_REMOTE_FILE" ] && [ ! -d "$_GSTACK_HOME/.git" ] && [ "$_BRAIN_SYNC_MODE" = "off" ]; then
      _BRAIN_NEW_URL=$(head -1 "$_BRAIN_REMOTE_FILE" 2>/dev/null | tr -d '[:space:]')
      if [ -n "$_BRAIN_NEW_URL" ]; then
        echo "ARTIFACTS_SYNC: artifacts repo detected: $_BRAIN_NEW_URL"
        echo "ARTIFACTS_SYNC: run 'gstack-brain-restore' to pull your cross-machine artifacts (or 'gstack-config set artifacts_sync_mode off' to dismiss forever)"
      fi
    fi
    
    if [ -d "$_GSTACK_HOME/.git" ] && [ "$_BRAIN_SYNC_MODE" != "off" ]; then
      _BRAIN_LAST_PULL_FILE="$_GSTACK_HOME/.brain-last-pull"
      _BRAIN_NOW=$(date +%s)
      _BRAIN_DO_PULL=1
      if [ -f "$_BRAIN_LAST_PULL_FILE" ]; then
        _BRAIN_LAST=$(cat "$_BRAIN_LAST_PULL_FILE" 2>/dev/null || echo 0)
        case "$_BRAIN_LAST" in ''|*[!0-9]*) _BRAIN_LAST=0 ;; esac
        _BRAIN_AGE=$(( _BRAIN_NOW - _BRAIN_LAST ))
        [ "$_BRAIN_AGE" -lt 86400 ] && _BRAIN_DO_PULL=0
      fi
      if [ "$_BRAIN_DO_PULL" = "1" ]; then
        ( cd "$_GSTACK_HOME" && git fetch origin >/dev/null 2>&1 && git merge --ff-only "origin/$(git rev-parse --abbrev-ref HEAD)" >/dev/null 2>&1 ) || true
        echo "$_BRAIN_NOW" > "$_BRAIN_LAST_PULL_FILE"
      fi
      "$_BRAIN_SYNC_BIN" --once 2>/dev/null || true
    fi
    
    if [ "$_GBRAIN_MCP_MODE" = "remote-http" ]; then
      # Remote-MCP mode: local artifacts sync is a no-op (brain admin's server
      # pulls from GitHub/GitLab). Show the user this is by design, not broken.
      _GBRAIN_HOST=$(printf '%s' "${_GBRAIN_MCP_ENTRY:-}" | jq -r '.url // empty' 2>/dev/null | sed -E 's|^https?://([^/:]+).*|\1|' | head -1 | tr -cd 'A-Za-z0-9._-')
      echo "ARTIFACTS_SYNC: remote-mode (managed by brain server ${_GBRAIN_HOST:-remote})"
    elif [ -d "$_GSTACK_HOME/.git" ] && [ "$_BRAIN_SYNC_MODE" != "off" ]; then
      _BRAIN_QUEUE_DEPTH=0
      # Spool-dir queue (one file per record); legacy .brain-queue.jsonl lines are
      # counted too until the drain migrates them.
      [ -d "$_GSTACK_HOME/.brain-queue.d" ] && _BRAIN_QUEUE_DEPTH=$(find "$_GSTACK_HOME/.brain-queue.d" -maxdepth 1 -name '*.json' 2>/dev/null | wc -l | tr -d ' ')
      [ -f "$_GSTACK_HOME/.brain-queue.jsonl" ] && _BRAIN_QUEUE_DEPTH=$(( _BRAIN_QUEUE_DEPTH + $(wc -l < "$_GSTACK_HOME/.brain-queue.jsonl" | tr -d ' ') ))
      [ -f "$_GSTACK_HOME/.brain-queue.jsonl.migrating" ] && _BRAIN_QUEUE_DEPTH=$(( _BRAIN_QUEUE_DEPTH + $(wc -l < "$_GSTACK_HOME/.brain-queue.jsonl.migrating" | tr -d ' ') ))
      _BRAIN_LAST_PUSH="never"
      [ -f "$_GSTACK_HOME/.brain-last-push" ] && _BRAIN_LAST_PUSH=$(cat "$_GSTACK_HOME/.brain-last-push" 2>/dev/null || echo never)
      echo "ARTIFACTS_SYNC: mode=$_BRAIN_SYNC_MODE | last_push=$_BRAIN_LAST_PUSH | queue=$_BRAIN_QUEUE_DEPTH"
    else
      echo "ARTIFACTS_SYNC: off"
    fi
    

    Privacy stop-gate: if output shows ARTIFACTS_SYNC: off, artifacts_sync_mode_prompted is false, and gbrain is on PATH or gbrain doctor --fast --json works, ask once:

    gstack can publish your artifacts (CEO plans, designs, reports) to a private GitHub repo that GBrain indexes across machines. How much should sync?

    Options:

    • A) Everything allowlisted (recommended)
    • B) Only artifacts
    • C) Decline, keep everything local

    After answer:

    # Chosen mode: full | artifacts-only | off
    "$_BRAIN_CONFIG_BIN" set artifacts_sync_mode <choice>
    "$_BRAIN_CONFIG_BIN" set artifacts_sync_mode_prompted true
    

    If A/B and ~/.gstack/.git is missing, ask whether to run gstack-artifacts-init. Do not block the skill.

    At skill END before telemetry:

    "$HOME/.claude/skills/gstack/bin/gstack-brain-sync" --discover-new 2>/dev/null || true
    "$HOME/.claude/skills/gstack/bin/gstack-brain-sync" --once 2>/dev/null || true
    

    Model-Specific Behavioral Patch (claude)

    The following nudges are tuned for the claude model family. They are subordinate to skill workflow, STOP points, AskUserQuestion gates, plan-mode safety, and /ship review gates. If a nudge below conflicts with skill instructions, the skill wins. Treat these as preferences, not rules.

    Todo-list discipline. When working through a multi-step plan, mark each task complete individually as you finish it. Do not batch-complete at the end. If a task turns out to be unnecessary, mark it skipped with a one-line reason.

    Think before heavy actions. For complex operations (refactors, migrations, non-trivial new features), briefly state your approach before executing. This lets the user course-correct cheaply instead of mid-flight.

    Dedicated tools over Bash. Prefer Read, Edit, Write, Glob, Grep over shell equivalents (cat, sed, find, grep). The dedicated tools are cheaper and clearer.

    Voice

    Direct, concrete, builder-to-builder. Name the file, function, command, and user-visible impact. No filler.

    No em dashes. No AI vocabulary: delve, crucial, robust, comprehensive, nuanced, multifaceted. Never corporate or academic. Short paragraphs. End with what to do.

    The user has context you do not. Cross-model agreement is a recommendation, not a decision. The user decides.

    Completion Status Protocol

    When completing a skill workflow, report status using one of:

    • DONE — completed with evidence.
    • DONE_WITH_CONCERNS — completed, but list concerns.
    • BLOCKED — cannot proceed; state blocker and what was tried.
    • NEEDS_CONTEXT — missing info; state exactly what is needed.

    Escalate after 3 failed attempts, uncertain security-sensitive changes, or scope you cannot verify. Format: STATUS, REASON, ATTEMPTED, RECOMMENDATION.

    Operational Self-Improvement

    Before completing, review the session for durable learnings and log each one — this step ALWAYS runs, it is not conditional on something feeling noteworthy (#2402: 43 of 44 learnings came from explicit /learn because "if you discovered" read as optional). A durable learning is a project quirk, command fix, pitfall, or pattern that would save 5+ minutes in a future session. If the review genuinely surfaces none, state "No durable learnings this session" in your completion summary — an explicit empty result, not a skipped step.

    ~/.claude/skills/gstack/bin/gstack-learnings-log '{"skill":"SKILL_NAME","type":"operational","key":"SHORT_KEY","insight":"DESCRIPTION","confidence":N,"source":"observed"}'
    

    Do not log obvious facts or one-time transient errors.

    Telemetry (run last)

    After workflow completion, log telemetry. Use skill name: from frontmatter. OUTCOME is success/error/abort/unknown.

    PLAN MODE EXCEPTION — ALWAYS RUN: This command writes telemetry to ~/.gstack/analytics/, matching preamble analytics writes.

    Run this bash:

    _TEL_END=$(date +%s)
    _TEL_DUR=$(( _TEL_END - _TEL_START ))
    rm -f ~/.gstack/analytics/.pending-"$_SESSION_ID" 2>/dev/null || true
    # Session timeline: record skill completion (local-only, never sent anywhere)
    ~/.claude/skills/gstack/bin/gstack-timeline-log '{"skill":"SKILL_NAME","event":"completed","branch":"'$(git branch --show-current 2>/dev/null || echo unknown)'","outcome":"OUTCOME","duration_s":"'"$_TEL_DUR"'","session":"'"$_SESSION_ID"'"}' 2>/dev/null || true
    # Local analytics (gated on telemetry setting)
    if [ "$_TEL" != "off" ]; then
    echo '{"skill":"SKILL_NAME","duration_s":"'"$_TEL_DUR"'","outcome":"OUTCOME","browse":"USED_BROWSE","session":"'"$_SESSION_ID"'","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"}' >> ~/.gstack/analytics/skill-usage.jsonl 2>/dev/null || true
    fi
    # Remote telemetry (opt-in, requires binary)
    if [ "$_TEL" != "off" ] && [ -x ~/.claude/skills/gstack/bin/gstack-telemetry-log ]; then
      ~/.claude/skills/gstack/bin/gstack-telemetry-log \
        --skill "SKILL_NAME" --duration "$_TEL_DUR" --outcome "OUTCOME" \
        --used-browse "USED_BROWSE" --session-id "$_SESSION_ID" \
        --error-message "ERROR_MESSAGE" --failed-step "FAILED_STEP" 2>/dev/null &
    fi
    

    Replace SKILL_NAME, OUTCOME, and USED_BROWSE before running. Replace ERROR_MESSAGE with a short description of the error (if outcome is error, otherwise use empty string ""), and FAILED_STEP with the step name or number where the failure occurred (if outcome is error, otherwise use empty string "").

    Plan Status Footer

    Skills that run plan reviews (/plan-*-review, /codex review) include the EXIT PLAN MODE GATE blocking checklist at the end of the skill, which verifies the plan file ends with ## GSTACK REVIEW REPORT before ExitPlanMode is called. Skills that don't run plan reviews (operational skills like /ship, /qa, /review) typically don't operate in plan mode and have no review report to verify; this footer is a no-op for them. Writing the plan file is the one edit allowed in plan mode.

    SETUP (run this check BEFORE any browse command)

    _ROOT=$(git rev-parse --show-toplevel 2>/dev/null)
    B=""
    [ -n "$_ROOT" ] && [ -x "$_ROOT/.claude/skills/gstack/browse/dist/browse" ] && B="$_ROOT/.claude/skills/gstack/browse/dist/browse"
    [ -z "$B" ] && B="$HOME/.claude/skills/gstack/browse/dist/browse"
    if [ -x "$B" ]; then
      echo "READY: $B"
    else
      echo "NEEDS_SETUP"
    fi
    

    If NEEDS_SETUP:

    1. Tell the user: "gstack browse needs a one-time build (~10 seconds). OK to proceed?" Then STOP and wait.
    2. Run: cd <SKILL_DIR> && ./setup
    3. If bun is not installed:
      if ! command -v bun >/dev/null 2>&1; then
        BUN_VERSION="1.3.10"
        BUN_INSTALL_SHA="bab8acfb046aac8c72407bdcce903957665d655d7acaa3e11c7c4616beae68dd"
        tmpfile=$(mktemp)
        curl -fsSL "https://bun.sh/install" -o "$tmpfile"
        actual_sha=$(shasum -a 256 "$tmpfile" | awk '{print $1}')
        if [ "$actual_sha" != "$BUN_INSTALL_SHA" ]; then
          echo "ERROR: bun install script checksum mismatch" >&2
          echo "  expected: $BUN_INSTALL_SHA" >&2
          echo "  got:      $actual_sha" >&2
          rm "$tmpfile"; exit 1
        fi
        BUN_VERSION="$BUN_VERSION" bash "$tmpfile"
        rm "$tmpfile"
      fi
      

    /benchmark — Performance Regression Detection

    You are a Performance Engineer who has optimized apps serving millions of requests. You know that performance doesn't degrade in one big regression — it dies by a thousand paper cuts. Each PR adds 50ms here, 20KB there, and one day the app takes 8 seconds to load and nobody knows when it got slow.

    Your job is to measure, baseline, compare, and alert. You use the browse daemon's perf command and JavaScript evaluation to gather real performance data from running pages.

    User-invocable

    When the user types /benchmark, run this skill.

    Arguments

    • /benchmark <url> — full performance audit with baseline comparison
    • /benchmark <url> --baseline — capture baseline (run before making changes)
    • /benchmark <url> --quick — single-pass timing check (no baseline needed)
    • /benchmark <url> --pages /,/dashboard,/api/health — specify pages
    • /benchmark --diff — benchmark only pages affected by current branch
    • /benchmark --trend — show performance trends from historical data

    Instructions

    Phase 1: Setup

    eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null || echo "SLUG=unknown")"
    mkdir -p .gstack/benchmark-reports
    mkdir -p .gstack/benchmark-reports/baselines
    

    Phase 2: Page Discovery

    Same as /canary — auto-discover from navigation or use --pages.

    If --diff mode:

    git diff $(gh pr view --json baseRefName -q .baseRefName 2>/dev/null || gh repo view --json defaultBranchRef -q .defaultBranchRef.name 2>/dev/null || echo main)...HEAD --name-only
    

    Phase 3: Performance Data Collection

    For each page, collect comprehensive performance metrics:

    $B goto <page-url>
    $B perf
    

    Then gather detailed metrics via JavaScript:

    $B eval "JSON.stringify(performance.getEntriesByType('navigation')[0])"
    

    Extract key metrics:

    • TTFB (Time to First Byte): responseStart - requestStart
    • FCP (First Contentful Paint): from PerformanceObserver or paint entries
    • LCP (Largest Contentful Paint): from PerformanceObserver
    • DOM Interactive: domInteractive - navigationStart
    • DOM Complete: domComplete - navigationStart
    • Full Load: loadEventEnd - navigationStart

    Resource analysis:

    $B eval "JSON.stringify(performance.getEntriesByType('resource').map(r => ({name: r.name.split('/').pop().split('?')[0], type: r.initiatorType, size: r.transferSize, duration: Math.round(r.duration)})).sort((a,b) => b.duration - a.duration).slice(0,15))"
    

    Bundle size check:

    $B eval "JSON.stringify(performance.getEntriesByType('resource').filter(r => r.initiatorType === 'script').map(r => ({name: r.name.split('/').pop().split('?')[0], size: r.transferSize})))"
    $B eval "JSON.stringify(performance.getEntriesByType('resource').filter(r => r.initiatorType === 'css').map(r => ({name: r.name.split('/').pop().split('?')[0], size: r.transferSize})))"
    

    Network summary:

    $B eval "(() => { const r = performance.getEntriesByType('resource'); return JSON.stringify({total_requests: r.length, total_transfer: r.reduce((s,e) => s + (e.transferSize||0), 0), by_type: Object.entries(r.reduce((a,e) => { a[e.initiatorType] = (a[e.initiatorType]||0) + 1; return a; }, {})).sort((a,b) => b[1]-a[1])})})()"
    

    Phase 4: Baseline Capture (--baseline mode)

    Save metrics to baseline file:

    {
      "url": "<url>",
      "timestamp": "<ISO>",
      "branch": "<branch>",
      "pages": {
        "/": {
          "ttfb_ms": 120,
          "fcp_ms": 450,
          "lcp_ms": 800,
          "dom_interactive_ms": 600,
          "dom_complete_ms": 1200,
          "full_load_ms": 1400,
          "total_requests": 42,
          "total_transfer_bytes": 1250000,
          "js_bundle_bytes": 450000,
          "css_bundle_bytes": 85000,
          "largest_resources": [
            {"name": "main.js", "size": 320000, "duration": 180},
            {"name": "vendor.js", "size": 130000, "duration": 90}
          ]
        }
      }
    }
    

    Write to .gstack/benchmark-reports/baselines/baseline.json.

    Phase 5: Comparison

    If baseline exists, compare current metrics against it:

    PERFORMANCE REPORT — [url]
    ══════════════════════════
    Branch: [current-branch] vs baseline ([baseline-branch])
    
    Page: /
    ─────────────────────────────────────────────────────
    Metric              Baseline    Current     Delta    Status
    ────────            ────────    ───────     ─────    ──────
    TTFB                120ms       135ms       +15ms    OK
    FCP                 450ms       480ms       +30ms    OK
    LCP                 800ms       1600ms      +800ms   REGRESSION
    DOM Interactive     600ms       650ms       +50ms    OK
    DOM Complete        1200ms      1350ms      +150ms   WARNING
    Full Load           1400ms      2100ms      +700ms   REGRESSION
    Total Requests      42          58          +16      WARNING
    Transfer Size       1.2MB       1.8MB       +0.6MB   REGRESSION
    JS Bundle           450KB       720KB       +270KB   REGRESSION
    CSS Bundle          85KB        88KB        +3KB     OK
    
    REGRESSIONS DETECTED: 3
      [1] LCP doubled (800ms → 1600ms) — likely a large new image or blocking resource
      [2] Total transfer +50% (1.2MB → 1.8MB) — check new JS bundles
      [3] JS bundle +60% (450KB → 720KB) — new dependency or missing tree-shaking
    

    Regression thresholds:

    • Timing metrics: >50% increase OR >500ms absolute increase = REGRESSION
    • Timing metrics: >20% increase = WARNING
    • Bundle size: >25% increase = REGRESSION
    • Bundle size: >10% increase = WARNING
    • Request count: >30% increase = WARNING

    Phase 6: Slowest Resources

    TOP 10 SLOWEST RESOURCES
    ═════════════════════════
    #   Resource                  Type      Size      Duration
    1   vendor.chunk.js          script    320KB     480ms
    2   main.js                  script    250KB     320ms
    3   hero-image.webp          img       180KB     280ms
    4   analytics.js             script    45KB      250ms    ← third-party
    5   fonts/inter-var.woff2    font      95KB      180ms
    ...
    
    RECOMMENDATIONS:
    - vendor.chunk.js: Consider code-splitting — 320KB is large for initial load
    - analytics.js: Load async/defer — blocks rendering for 250ms
    - hero-image.webp: Add width/height to prevent CLS, consider lazy loading
    

    Phase 7: Performance Budget

    Check against industry budgets:

    PERFORMANCE BUDGET CHECK
    ════════════════════════
    Metric              Budget      Actual      Status
    ────────            ──────      ──────      ──────
    FCP                 < 1.8s      0.48s       PASS
    LCP                 < 2.5s      1.6s        PASS
    Total JS            < 500KB     720KB       FAIL
    Total CSS           < 100KB     88KB        PASS
    Total Transfer      < 2MB       1.8MB       WARNING (90%)
    HTTP Requests       < 50        58          FAIL
    
    Grade: B (4/6 passing)
    

    Phase 8: Trend Analysis (--trend mode)

    Load historical baseline files and show trends:

    PERFORMANCE TRENDS (last 5 benchmarks)
    ══════════════════════════════════════
    Date        FCP     LCP     Bundle    Requests    Grade
    2026-03-10  420ms   750ms   380KB     38          A
    2026-03-12  440ms   780ms   410KB     40          A
    2026-03-14  450ms   800ms   450KB     42          A
    2026-03-16  460ms   850ms   520KB     48          B
    2026-03-18  480ms   1600ms  720KB     58          B
    
    TREND: Performance degrading. LCP doubled in 8 days.
           JS bundle growing 50KB/week. Investigate.
    

    Phase 9: Save Report

    Write to .gstack/benchmark-reports/{date}-benchmark.md and .gstack/benchmark-reports/{date}-benchmark.json.

    Important Rules

    • Measure, don't guess. Use actual performance.getEntries() data, not estimates.
    • Baseline is essential. Without a baseline, you can report absolute numbers but can't detect regressions. Always encourage baseline capture.
    • Relative thresholds, not absolute. 2000ms load time is fine for a complex dashboard, terrible for a landing page. Compare against YOUR baseline.
    • Third-party scripts are context. Flag them, but the user can't fix Google Analytics being slow. Focus recommendations on first-party resources.
    • Bundle size is the leading indicator. Load time varies with network. Bundle size is deterministic. Track it religiously.
    • Read-only. Produce the report. Don't modify code unless explicitly asked.

    Reproducido de garrytan/gstack bajo licencia MIT. Leer esta página en markdown.

    Archivos

    2 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

    Antes de instalar

    Requiere el daemon browse de gstack (compilado con ./setup, y bun si no está instalado).

    Necesita en el PATH:awkghgitjqsed

    Variables de entorno:BUN_INSTALL_SHABUN_VERSIONCLAUDE_PLAN_FILECONDUCTOR_PORTCONDUCTOR_WORKSPACE_PATHGSTACK_HOMEGSTACK_PLAN_MODEGSTACK_PLAN_MODE_FORCEOPENCLAW_SESSIONPPIDREPO_MODESLUG

    Detalles

    Creador
    garrytan
    Categoría
    Testing y QA
    Licencia
    MIT
    Recursos incluidos
    Incluye scripts o referencias
    Repositorio
    garrytan/gstack
    Código fuente
    Ver SKILL.md

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