zymtrace-anchored-query

name	zymtrace-anchored-query
last_validated	"2026-05-24T00:00:00.000Z"
description	Reusable wrapper for the knowledge-base-first SQL pattern, adapted to the zymtrace ClickHouse profiling backend. Operator names the metric / question / time range. Skill anchors the `zymtrace_profiling.events` schema first (DESCRIBE + label-value + cardinality probes), derives the safe narrow SQL, runs it via `kubectl port-forward` + `curl -X POST`, and saves the raw payload to a provenance-bearing bundle per the perf-lake-contract. Workload-agnostic. The ClickHouse cousin of `prometheus-anchored-query`. Triggers on "zymtrace anchored query", "clickhouse anchored query", "zymtrace query", "save zymtrace payload", "anchored zymtrace", "anchored clickhouse", or any combination of "zymtrace / clickhouse / profile" with "anchored / safe / provenance / query / saved-payload".
allowed-tools	["Bash(kubectl:port-forward,*)","Bash(kubectl:get,*)","Bash(curl:*)","Bash(jq:*)","Bash(sha256sum:*)","Bash(date:*)","Read","Write"]

zymtrace-anchored-query

Purpose

Make ad-hoc ClickHouse queries against the zymtrace profiling backend safe by default by wrapping the "anchor labels in the schema FIRST, then run the query" discipline as a reusable skill. Saves the raw payload to a provenance-bearing bundle so future queries can replay the same shape, and so the perf-lake-contract is honored without operator effort.

This skill exists because the zymtrace_profiling.events table on a busy cluster holds tens of millions of rows per day across every profiler-enabled namespace. An SQL query that forgets to bound timestamp or pod_name LIKE '...' returns a full-table scan that overloads ClickHouse and starves real-time ingest. The anchor-then-query pattern works for arbitrary profiling questions. This skill makes it agent-routable.

prometheus-anchored-query is the Prometheus version of the same pattern. Use this when the question targets profiling data (CPU on-cpu samples, GPU cuda kernel samples, stack-traces, function-resolution lookups). Use the Prometheus skill when the question targets time-series cluster metrics.

When to use

• Operator has a profiling question that maps to a ClickHouse query but doesn't know the exact pod_name pattern, time-window, or event_kind to use yet.
• Operator wants to save a zymtrace payload to disk with provenance (for evidence bundles, profile-findings write-ups, share-with-team artifacts, or feeding the optional kernel_class_gpu_pct + cpu_spinpoll_pct fields into the inference-perf-baseline-bridge schema).
• Operator wants to confirm a pod_name LIKE pattern matches the expected pod set + that the time window has enough samples to be statistically useful before running a full top-N query.

Do not use this skill for:

• Already-vetted profile queries - just run the SQL directly via curl -X POST 'http://localhost:9123/' --data-binary @<query.sql>.
• Live UI exploration - the zymtrace web UI has its own flamegraph/timeline views. This skill is for headless reproducible queries.
• Cross-tenant queries - every example here scopes to a single pod_name LIKE pattern. Cross-tenant exploration belongs to the zymtrace UI or the zymtrace MCP's pattern-recognition tools.

Example prompts

• "What are the top 25 hottest CUDA kernels in basic-inference pods over the last 5 minutes?"
• "Save the on_cpu hot-Python-callers query for glm-inference between 16:00 and 16:30 UTC."
• "Did shm_broadcast.py CPU time in the glm-inference pods drop after we set VLLM_USE_SHM_BROADCAST_BLOCKING=1? Compare the last 30 minutes against the window before the change."
• "Get me the kernel-class breakdown for the kimi-inference pods during the perf-bench window I just ran so I can fill kernel_class_gpu_pct in the perf-baseline bundle."
• /zymtrace-anchored-query --question "top-25 cuda kernels for basic-inference pods, last 5 min" --time-range "now-5m..now"
• /zymtrace-anchored-query --pod 'basic-inference%' --event-kind cuda --window '<start-UTC>..<end-UTC>' --top 25

Prerequisites

1. Cluster context active - kubectl config current-context returns the cluster running zymtrace.
2. zymtrace backend deployed - kubectl -n zymtrace get pods -l app=zymtrace-clickhouse shows at least one Running pod.
3. Operator question or pod+kind+window - one of:
   • Free-form question (skill derives pod_name pattern + event_kind + window via schema introspection).
   • Explicit --pod <LIKE-pattern> --event-kind <cuda|on_cpu> --window <UTC-range>.
4. Time window - --time-range "now-Nm..now" or absolute UTC range. Default now-5m..now. Queries with no timestamp bound are refused.
5. PROFILE_AND_OPTIMIZE_REPO_ROOT for the bundle path (falls back to ${PWD}/zymtrace-bundles/<run-id>/).

Interaction style

Iterative. The whole point of the skill is the "anchor first, then query" discipline. Pause after the schema-anchor call to surface the proposed SQL before running it.

Workflow

Phase 0: resolve the question

If operator provided a free-form question, restate the question + the proposed pod_name LIKE pattern, event_kind, and time window in one sentence. Get confirmation.

If operator provided explicit pod + kind + window, proceed.

Phase 1: schema + label anchor

Start the port-forward if not already running:

# Idempotent: skip if a port-forward to svc/zymtrace-clickhouse is already up on 9123.
if ! curl -fsS -o /dev/null -m 2 'http://localhost:9123/ping'; then
  nohup kubectl -n zymtrace port-forward svc/zymtrace-clickhouse 9123:8123 \
    > /tmp/zt-ch-pf.log 2>&1 &
  disown
  sleep 3
fi

Probe the schema for the table the query will hit (default zymtrace_profiling.events):

curl -fsS -X POST 'http://${CH_USER}:${CH_PASSWORD}@localhost:9123/' \
  --data-binary 'DESCRIBE TABLE zymtrace_profiling.events FORMAT JSON' \
  > /tmp/zt-schema-anchor.json

Then sanity-check cardinality of the proposed selector before running the full top-N query:

curl -fsS -X POST 'http://${CH_USER}:${CH_PASSWORD}@localhost:9123/' \
  --data-binary "
SELECT
  count() AS rows,
  uniqExact(pod_name) AS pods,
  min(timestamp) AS first_ts,
  max(timestamp) AS last_ts
FROM zymtrace_profiling.events
WHERE pod_name LIKE '<pattern>'
  AND event_kind = '<kind>'
  AND timestamp >= '<start>'
  AND timestamp <= '<end>'
FORMAT JSON
"

The cardinality probe response tells us:

• Whether the selector matches any rows (catches typos in pod_name LIKE patterns immediately).
• The exact pod_name set the query will scan (catches accidental cross-pod scope).
• The actual timestamp range that has data (catches misaligned windows - e.g. a window anchored on the bench's scheduled start when the run actually began and ended a few minutes later, a common first-pass mistake).
• The sample count (so the operator can decide if the window is too short to be statistically meaningful - <1000 samples is typically not enough).

If rows = 0: do NOT immediately conclude "no signal". Zymtrace data is not instantaneous - rank two causes in this order:

1. Ingest lag (transient). If the pod/window are freshly-run (the bench just ended), zymtrace may not have flushed to ClickHouse yet - it flushes asynchronously (~seconds-to-minutes). Wait for the flush and re-run the cardinality probe for the freshest data before concluding. A simple poll (mirrors scripts/zymtrace-ingest-wait.sh):

   for i in $(seq 1 "${ZYM_INGEST_MAX_ATTEMPTS:-6}"); do
     rows=$(curl -fsS -X POST 'http://${CH_USER}:${CH_PASSWORD}@localhost:9123/' \
       --data-binary "<the cardinality probe SELECT ... FORMAT JSON>" | jq -r '.data[0].rows')
     [ "${rows:-0}" -gt 0 ] && break
     sleep "${ZYM_INGEST_WAIT_SEC:-20}"   # requery for the freshest data
   done
   

2. Wrong selector / window. If it STAYS 0 past the flush window, the pod_name LIKE pattern or [start,end] doesn't match - re-query by host=<node>+window (the hash-suffixed pod-name filter often reads empty when the host filter returns frames) and confirm the window.

Per the fail-fast rationale in server/docs/zymtrace-query-hygiene.md, running the top-N on an empty result would look like "no signal" when it is really either ingest lag (requery) or a wrong selector (fix it) - never silently proceed.

If rows > 10_000_000: refuse. The selector is too broad. Narrow the time window or pod_name pattern.

Phase 2: emit the SQL

Construct the narrow SQL using only schema-anchor-confirmed labels and the operator's --time-range. The SQL must include all three bounds:

WHERE pod_name LIKE '<pattern>'
  AND event_kind = '<kind>'
  AND timestamp >= '<start>'
  AND timestamp <= '<end>'

Print the proposed SQL back:

Proposed SQL: <full query>
Anchor:       pod_name LIKE '<pattern>', event_kind = '<kind>'
Time range:   <start> .. <end>
Expected rows scanned: ~<from cardinality probe>
LIMIT clause: <N rows> (default 25)

Ask: "Run this query?" Do not auto-advance.

Phase 3: run + save

After confirmation, run the query via curl + save the raw response:

RUN_ID="$(date -u +%Y%m%dT%H%M%SZ)"
BUNDLE_DIR="${PROFILE_AND_OPTIMIZE_REPO_ROOT:-${PWD}}/experiments/artifacts/zymtrace-bundles/${RUN_ID}"
mkdir -p "${BUNDLE_DIR}"

cat > "${BUNDLE_DIR}/query.sql" <<'EOF'
<the exact SQL from Phase 2>
EOF

curl -fsS -X POST 'http://${CH_USER}:${CH_PASSWORD}@localhost:9123/' \
  --data-binary "@${BUNDLE_DIR}/query.sql" \
  -H 'X-ClickHouse-Format: JSON' \
  -o "${BUNDLE_DIR}/response.json"

sha256sum "${BUNDLE_DIR}/query.sql" "${BUNDLE_DIR}/response.json" \
  > "${BUNDLE_DIR}/sha256sums.txt"

Save the bundle layout (parallels prometheus-bundles/<run-id>/ for consistency):

${PROFILE_AND_OPTIMIZE_REPO_ROOT}/experiments/artifacts/zymtrace-bundles/<run-id>/
  SOURCE.md                     # operator + question + cluster + UTC-ts + git SHA
  query.sql                     # the exact SQL run
  query.json                    # the curl request shape (URL, headers, body-ref)
  response.json                 # the raw ClickHouse response (saved verbatim)
  summary.md                    # human-readable: row count, top-5 by pct, anchor recap
  schema-anchor.json            # the DESCRIBE + cardinality-probe responses (provenance)
  sha256sums.txt                # query.sql + response.json sha256

Phase 4: report

Print:

• Row count + scanned-rows count (sanity check: did we get the cardinality the anchor predicted?).
• Top 5 rows of the response (highest pct or total_samples).
• Bundle path for the saved payload.
• Cross-link to inference-perf-baseline-bridge if the operator's question maps to a kernel_class_gpu_pct or cpu_spinpoll_pct field (it usually does - that's the canonical consumer of this skill's output).

Kernel-class bucketing (appendix)

The inference-perf-baseline-bridge schema's kernel_class_gpu_pct field is derived by bucketing kernel names from this skill's GPU-side response. The canonical regex set covers the kernel classes that dominate FP8 MoE serving on B200-class hardware:

Bucket key	Regex on kernel (func_name)	What it catches
fp8_bmm	`^bmm_(E4m3	Bfloat16)_E4m3E4m3ORsm100_fp8_gemmORdeep_gemm::sm100_fp8`
tp_all_reduce	multimem_all_reduce_kernel OR trtllm_allreduce OR oneshot_lamport	Tensor-parallel all-reduce kernels (both PyTorch CUDASymmetricMemoryOps + flashinfer paths)
cuda_event_sync	cudaEventSynchronize OR cuda-sync	Host-side CUDA event sync (sign of cudagraph coverage gaps)
mla_attention	fmhaSm100fKernel OR cute_flash_fwd_sm100 OR mla_attention	DeepSeek-V3-style MLA attention (Q576/V512 paged-KV)
moe_routing_finalize	`moe::dev::(routing	finalize
per_token_group_quant	per_token_group_quant_8bit	Block-FP8 activation quantization (group_size=128)

Apply the regex against kernel in the GPU response, sum the pct per bucket, leave classes outside this set under arbitrary <other-class> keys (e.g. nvjet_trt_llm, d2d_memcpy, elementwise_misc).

Full-context reporting (no bare numbers)

Per docs/METHODOLOGY.md "Full-context reporting": every number this skill emits (throughput, latency, TPOT/ITL, BW, %SoL, speedup, efficiency, goodput, acceptance rate, scaling efficiency, thermal/failure rate - whatever it reports) MUST carry its full measurement-context descriptor, and every comparison MUST be matched on it. A bare number is a defect - it cannot set a default, ship a config, or appear in a report.

• Identity: model (+HF path), hardware (exact ceiling token GB300/B200), quant, kv-cache dtype.
• Parallelism: TP, DP (replicas), PP, EP, parallel_strategy.
• Serving cfg: max-num-seqs, max-num-batched-tokens, gpu-memory-utilization, max-model-len, cudagraph_mode/enforce_eager, async_scheduling, prefix-caching.
• Workload: dataset, ISL/OSL (or mean in/out tokens), concurrency, num-prompts.
• Regime: warm vs cold. Latency vs throughput tier.
• Stack: image/vllm commit, bench backend, serving engine.
• Grounding: %SoL (+ ceiling key from configs/sol-ceilings.yaml - never inline a peak), sol_rigor (L1-L4), trials n (mean±std), same-node, baseline named. (If the metric is not roofline-bound - e.g. accuracy/acceptance - omit %SoL but keep the rest of the descriptor.)
• Per-number exact shape (no smoothing): when reporting more than one number, keep EACH with its own exact shape (ISL/OSL, concurrency, dataset, regime) - never normalize a set to one uniform descriptor that hides per-point variation (e.g. c=1 @ ISL1024/OSL256 + c=64 @ ISL4096/OSL512, NOT one shared "random").

Next lever / BREAKTHROUGH (Grind Mandate)

If this skill emits a measured result, its output MUST end by naming the next perf lever, its expected unlock (direction + rough magnitude), and the gate that proves/refutes it, per docs/METHODOLOGY.md "Always be grinding". A measured win is the new floor, not the finish -- so do everything we can to find the next BREAKTHROUGH: the highest-EV unlock toward Speed-of-Light (a new champion / kernel / router / quant / parallelism / spec-decode win, or an unblocked stack), not just the next micro-lever. Rank the candidate breakthrough levers by value x cost (the GRIND FRONTIER, perftunereport value_view), pursue the top, bank the rest with evidence. Record WHY a refuted lever loses, update the standing frontier in the active bundle's HANDOFF.md. Never conclude "exhausted/optimal/done" without an explicit next-lever frontier (an empty frontier AND a documented SoL wall only). Delete this section ONLY if the skill produces no measurements.

Safety

• Empty != no data (ClickHouse ingest lag). A rows = 0 on a freshly-run pod/window is often ingest lag, not absence - zymtrace flushes to ClickHouse asynchronously. Wait for the flush and re-probe (ZYM_INGEST_WAIT_SEC / ZYM_INGEST_MAX_ATTEMPTS) before concluding. Only a persistent 0 past the flush is a wrong selector / real gap. See server/docs/zymtrace-query-hygiene.md.
• Schema-anchor first is mandatory. Phase 1 cannot be skipped. The whole point of the skill is to enforce that discipline.
• All three bounds required. Every SQL the skill emits has pod_name LIKE, event_kind =, and timestamp >=/<=. Queries missing any of the three are refused.
• No full-table scans. If the cardinality probe reports >10M rows scanned, the skill refuses to run the top-N. Operator narrows the selector and retries.
• LIMIT 25 default. The default top-N is 25 rows. Operator can override to as many as 100. Anything larger is refused - the bundle is for human-readable summary, not bulk export.
• Raw payload preservation. Per server/docs/perf-lake-contract.md, every saved query records the SQL, the schema anchor, the time range, the response time, and the cluster context.
• Read-only. The skill only runs SELECT / DESCRIBE / SHOW statements. Never INSERT / ALTER / DROP / TRUNCATE. The ClickHouse credential the port-forward uses (${CH_USER}:${CH_PASSWORD}) is the default in-cluster read/write account, but this skill self-restricts to read-only via its query-construction logic - operator-supplied SQL that contains any non-SELECT verb is refused before the curl.
• Port-forward hygiene. The nohup kubectl port-forward is left running in /tmp/zt-ch-pf.log. Cleanup is operator-driven. The skill does not kill the port-forward on exit (subsequent queries re-use it).
• No credentials in conversation. The curl lines read CH_USER / CH_PASSWORD from the environment. Nothing is hard-coded here. Never paste a real production password into the conversation or any saved bundle.

Source-of-truth references

• prometheus-anchored-query - the Prometheus cousin of this skill.
• inference-perf-baseline-bridge - primary consumer. Derives kernel_class_gpu_pct + cpu_spinpoll_pct from this skill's output.
• analyze-zymtrace-workload - the zymtrace MCP analytical workflow (different layer. Pattern-recognition over the same data).
• Bundled server CLAUDE.md - fail-fast + provenance rules.