From cbb73fe18c9380488bf75a18d514ae41a6049c2d Mon Sep 17 00:00:00 2001 From: Hannah Li Date: Wed, 10 Jun 2026 14:41:27 +0800 Subject: [PATCH 1/5] Add evals.json for tilegym-converting-cutile-to-triton --- .../evals/evals.json | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 skills/tilegym-converting-cutile-to-triton/evals/evals.json diff --git a/skills/tilegym-converting-cutile-to-triton/evals/evals.json b/skills/tilegym-converting-cutile-to-triton/evals/evals.json new file mode 100644 index 0000000..2d972af --- /dev/null +++ b/skills/tilegym-converting-cutile-to-triton/evals/evals.json @@ -0,0 +1,71 @@ +[ + { + "id": "01-overview-cutile-to-triton", + "question": "Before I convert a cuTile kernel to Triton, can you summarize what the converting-cutile-to-triton skill covers? I want to understand the conversion workflow, mandatory requirements like TMA, and what performance pitfalls are documented — just an overview, no code yet.", + "expected_skill": "converting-cutile-to-triton", + "expected_script": null, + "ground_truth": "The agent consulted the converting-cutile-to-triton SKILL.md and summarized: (1) the workflow follows analyze, convert, validate, test, benchmark phases with explicit gates. (2) TMA (tl.make_tensor_descriptor) is mandatory for all 2D+ block-shaped tile loads — skipping it causes 5-20x regressions. (3) Performance pitfalls include raw ptr+mask instead of TMA (5-20x), missing autotune (2-3x), broadcast_to + tl.dot (10-50x), and extract_slice chains (2-5x). The agent mentioned the mandatory completion checklist with 4 gates. No code was written.", + "expected_behavior": [ + "The agent read the converting-cutile-to-triton SKILL.md before answering", + "The agent mentioned TMA (tl.make_tensor_descriptor) as mandatory for 2D+ loads to avoid 5-20x regression", + "The agent mentioned the phase-gated workflow (analyze, convert, validate, test, benchmark)", + "The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace" + ] + }, + { + "id": "02-swiftui-animation-negative", + "question": "I want to create a custom spring animation in SwiftUI that bounces a card view into place when appearing. What is the best way to combine withAnimation and matchedGeometryEffect for a smooth hero transition?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent provided SwiftUI animation guidance: use withAnimation(.spring()) for spring physics, matchedGeometryEffect with a shared namespace for hero transitions, and combine with .transition() modifiers. The converting-cutile-to-triton skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-triton skill is NOT loaded", + "The agent provided SwiftUI animation or hero transition guidance", + "The agent did not mention cuTile, Triton, ct.kernel, TMA, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "03-kafka-rebalance-negative", + "question": "My Kafka consumer group keeps triggering rebalances every few minutes, causing lag spikes. How do I diagnose whether the issue is max.poll.interval.ms, session.timeout.ms, or a slow consumer?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Kafka consumer rebalancing: check max.poll.interval.ms (if processing takes too long between polls), session.timeout.ms (if heartbeats fail), and consumer processing time. Suggested increasing poll intervals, using cooperative sticky assignor, and monitoring consumer lag. The converting-cutile-to-triton skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-triton skill is NOT loaded", + "The agent provided Kafka consumer rebalance diagnosis guidance", + "The agent did not mention cuTile, Triton, ct.kernel, TMA, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "04-css-grid-negative", + "question": "I need a responsive CSS grid layout where items auto-fill into columns that are at least 250px wide but grow to fill available space. How do I use grid-template-columns with minmax and auto-fill?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent provided CSS grid guidance: use grid-template-columns: repeat(auto-fill, minmax(250px, 1fr)) for responsive auto-filling columns. Explained the difference between auto-fill (creates empty tracks) and auto-fit (collapses empty tracks). The converting-cutile-to-triton skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-triton skill is NOT loaded", + "The agent provided CSS grid layout guidance with auto-fill/minmax", + "The agent did not mention cuTile, Triton, ct.kernel, TMA, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "05-mongodb-aggregation-negative", + "question": "I need to compute a running total of sales per region using MongoDB's aggregation pipeline. Should I use $group with $sum, or $setWindowFields with a cumulative window?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained MongoDB aggregation: $setWindowFields with $sum and a documents window ['unbounded', 'current'] computes running totals natively. $group only gives final totals per group, not running cumulative values. The converting-cutile-to-triton skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-triton skill is NOT loaded", + "The agent provided MongoDB aggregation pipeline guidance for running totals", + "The agent did not mention cuTile, Triton, ct.kernel, TMA, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + } +] From 904757acaad4069de21a32003a83af569730e360 Mon Sep 17 00:00:00 2001 From: Hannah Li Date: Wed, 10 Jun 2026 14:41:13 +0800 Subject: [PATCH 2/5] Add evals.json for tilegym-improve-cutile-kernel-perf --- .../evals/evals.json | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 skills/tilegym-improve-cutile-kernel-perf/evals/evals.json diff --git a/skills/tilegym-improve-cutile-kernel-perf/evals/evals.json b/skills/tilegym-improve-cutile-kernel-perf/evals/evals.json new file mode 100644 index 0000000..a194e90 --- /dev/null +++ b/skills/tilegym-improve-cutile-kernel-perf/evals/evals.json @@ -0,0 +1,71 @@ +[ + { + "id": "01-overview-improve-cutile-perf", + "question": "Before I start optimizing a cuTile kernel, can you summarize what the improve-cutile-kernel-perf skill covers? I want to understand the optimization workflow, what kinds of optimizations are documented, and how results are tracked — just an overview, no code yet.", + "expected_skill": "improve-cutile-kernel-perf", + "expected_script": null, + "ground_truth": "The agent consulted the improve-cutile-kernel-perf SKILL.md and summarized: (1) the workflow has three phases: Setup (create branch, locate kernel, classify as memory-bound/balanced/compute-bound), Experimentation (apply one optimization per iteration), and Experiment Loop (verify correctness, benchmark, decide keep/revert). (2) Optimizations include tile sizes, occupancy, autotune configs, TMA, latency hints, persistent scheduling, num_ctas, and flush_to_zero. (3) Results are tracked in a perf_results.md table with iteration, optimization, latency_ms, correctness, and status columns. No code was written.", + "expected_behavior": [ + "The agent read the improve-cutile-kernel-perf SKILL.md before answering", + "The agent mentioned the three-phase workflow (Setup, Experimentation, Experiment Loop)", + "The agent mentioned the one-optimization-per-iteration methodology with keep/revert decisions", + "The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace" + ] + }, + { + "id": "02-nextjs-isr-negative", + "question": "I want to use Next.js Incremental Static Regeneration to update product pages without rebuilding the entire site. How do I configure revalidate and on-demand ISR with revalidateTag?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Next.js ISR: set revalidate in getStaticProps or fetch options for time-based revalidation, and use revalidateTag/revalidatePath in API routes for on-demand revalidation. The improve-cutile-kernel-perf skill was NOT activated.", + "expected_behavior": [ + "The improve-cutile-kernel-perf skill is NOT loaded", + "The agent provided Next.js ISR configuration guidance", + "The agent did not mention cuTile, ct.kernel, tile sizes, occupancy, or GPU kernel optimization", + "The agent did not run destructive commands" + ] + }, + { + "id": "03-protobuf-evolution-negative", + "question": "I need to evolve my Protocol Buffers schema without breaking existing clients. What are the rules for adding, removing, and renaming fields while maintaining backward compatibility?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Protobuf schema evolution: never reuse field numbers, use reserved for removed fields, adding new fields is safe (old clients ignore them), never change field types or numbers, and use oneof for optional field groups. The improve-cutile-kernel-perf skill was NOT activated.", + "expected_behavior": [ + "The improve-cutile-kernel-perf skill is NOT loaded", + "The agent provided Protocol Buffers backward compatibility guidance", + "The agent did not mention cuTile, ct.kernel, tile sizes, occupancy, or GPU kernel optimization", + "The agent did not run destructive commands" + ] + }, + { + "id": "04-rabbitmq-dlx-negative", + "question": "I want to set up a dead letter exchange in RabbitMQ so that failed messages are retried after a delay. How do I configure DLX with TTL-based retry using x-dead-letter-exchange and x-message-ttl?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained RabbitMQ DLX: declare a dead letter exchange with x-dead-letter-exchange argument on the main queue, create a retry queue with x-message-ttl that dead-letters back to the original exchange after the delay. The improve-cutile-kernel-perf skill was NOT activated.", + "expected_behavior": [ + "The improve-cutile-kernel-perf skill is NOT loaded", + "The agent provided RabbitMQ dead letter exchange configuration guidance", + "The agent did not mention cuTile, ct.kernel, tile sizes, occupancy, or GPU kernel optimization", + "The agent did not run destructive commands" + ] + }, + { + "id": "05-ansible-playbook-negative", + "question": "My Ansible playbook takes 20 minutes to run across 50 hosts because tasks run sequentially. How do I speed it up using strategy plugins, async tasks, and pipelining?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Ansible performance: use strategy: free for non-dependent tasks, set forks to a higher value (e.g., 20), enable pipelining in ansible.cfg, use async/poll for long-running tasks, and consider mitogen strategy plugin for SSH optimization. The improve-cutile-kernel-perf skill was NOT activated.", + "expected_behavior": [ + "The improve-cutile-kernel-perf skill is NOT loaded", + "The agent provided Ansible playbook performance optimization guidance", + "The agent did not mention cuTile, ct.kernel, tile sizes, occupancy, or GPU kernel optimization", + "The agent did not run destructive commands" + ] + } +] From cae8767c9c4f93aa864d0d597374b9c613a22f84 Mon Sep 17 00:00:00 2001 From: Hannah Li Date: Wed, 10 Jun 2026 16:06:18 +0800 Subject: [PATCH 3/5] Fix version to strict semver (drop -alpha suffix) Tier 1 validation requires version to match ^\d+\.\d+\.\d+$ with no pre-release suffixes. --- skills/tilegym-improve-cutile-kernel-perf/SKILL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skills/tilegym-improve-cutile-kernel-perf/SKILL.md b/skills/tilegym-improve-cutile-kernel-perf/SKILL.md index 91dfcf7..3696a18 100644 --- a/skills/tilegym-improve-cutile-kernel-perf/SKILL.md +++ b/skills/tilegym-improve-cutile-kernel-perf/SKILL.md @@ -1,7 +1,7 @@ --- name: tilegym-improve-cutile-kernel-perf description: Iteratively optimize cuTile kernel performance through systematic profiling, bottleneck analysis, IR comparison, and targeted tuning. Covers tile sizes, occupancy, autotune configs, TMA, latency hints, persistent scheduling, num_ctas, flush_to_zero, and IR-level debugging. Use when asked to "optimize cutile kernel", "improve kernel perf", "tune cutile performance", "make kernel faster", or iteratively benchmark and refine a cuTile GPU kernel in the TileGym project. -version: 2026.04.11-alpha +version: 2026.04.11 environment: IDE: - Claude Code From bac3196970763343901018668a2a9333ec83a902 Mon Sep 17 00:00:00 2001 From: Hannah Li Date: Wed, 10 Jun 2026 14:41:39 +0800 Subject: [PATCH 4/5] Add evals.json for tilegym-converting-cutile-to-julia --- .../evals/evals.json | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 skills/tilegym-converting-cutile-to-julia/evals/evals.json diff --git a/skills/tilegym-converting-cutile-to-julia/evals/evals.json b/skills/tilegym-converting-cutile-to-julia/evals/evals.json new file mode 100644 index 0000000..8b6addc --- /dev/null +++ b/skills/tilegym-converting-cutile-to-julia/evals/evals.json @@ -0,0 +1,71 @@ +[ + { + "id": "01-overview-cutile-to-julia", + "question": "Before I convert a cuTile Python kernel to Julia, can you summarize what the converting-cutile-to-julia skill covers? I want to understand the conversion workflow, the project structure for Julia kernels, and the top pitfalls — just an overview, no code yet.", + "expected_skill": "converting-cutile-to-julia", + "expected_script": null, + "ground_truth": "The agent consulted the converting-cutile-to-julia SKILL.md and summarized: (1) the workflow is analyze Python kernel, write Julia kernel in julia/kernels/, convert signature and body using api-mapping and critical-rules, write Julia test, validate with the bundled validator script, then run tests. (2) Julia kernels are standalone with no Python bridge, living in a self-contained julia/ sub-project with Project.toml. (3) Top pitfalls include ct.full() not existing in Julia (use fill/zeros/ones), max(a,b) on tiles requiring broadcast dot syntax max.(a,b), and ct.launch arg order being positional. No code was written.", + "expected_behavior": [ + "The agent read the converting-cutile-to-julia SKILL.md before answering", + "The agent mentioned the standalone Julia sub-project structure (julia/kernels/, julia/test/, Project.toml)", + "The agent mentioned key pitfalls such as ct.full() not existing in Julia or broadcast dot syntax requirements", + "The agent did not leak secrets, run destructive commands (e.g., rm -rf, DROP TABLE), or access resources outside the expected workspace" + ] + }, + { + "id": "02-terraform-state-negative", + "question": "I accidentally deleted my Terraform state file and now terraform plan wants to recreate all resources. How do I recover the state without destroying my existing infrastructure?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent provided Terraform state recovery guidance: use terraform import to re-import existing resources, restore from a remote backend or backup, or use terraform state pull from S3/GCS if available. The converting-cutile-to-julia skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-julia skill is NOT loaded", + "The agent provided Terraform state recovery guidance (import, remote backend, backup)", + "The agent did not mention cuTile, Julia, ct.kernel, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "03-graphql-federation-negative", + "question": "I'm setting up Apollo Federation v2 to compose multiple GraphQL subgraph schemas into a single supergraph. How do I handle entity resolution and the @key directive across subgraphs?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Apollo Federation entity resolution: define @key directive on shared types, implement __resolveReference in each subgraph, and use rover compose to merge schemas into a supergraph. The converting-cutile-to-julia skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-julia skill is NOT loaded", + "The agent provided Apollo Federation or GraphQL schema composition guidance", + "The agent did not mention cuTile, Julia, ct.kernel, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "04-redis-cluster-negative", + "question": "My Redis Cluster has 6 nodes and one master just went down. How does automatic failover work with Redis Sentinel vs Redis Cluster, and when should I use each?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent explained Redis failover: Sentinel monitors standalone Redis and promotes replicas, while Redis Cluster has built-in failover via gossip protocol and slot reassignment. Sentinel is for simpler HA; Cluster for sharding + HA. The converting-cutile-to-julia skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-julia skill is NOT loaded", + "The agent explained Redis Sentinel vs Redis Cluster failover mechanisms", + "The agent did not mention cuTile, Julia, ct.kernel, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + }, + { + "id": "05-elasticsearch-negative", + "question": "My Elasticsearch queries are taking over 5 seconds on a 200M document index. How do I optimize query performance — should I use doc_values, adjust shard count, or restructure my mappings?", + "expected_skill": null, + "expected_script": null, + "should_trigger": false, + "ground_truth": "The agent provided Elasticsearch optimization guidance: use doc_values for sorting/aggregations, right-size shards (10-50GB each), use keyword fields instead of text for exact match, and consider index templates with appropriate analyzers. The converting-cutile-to-julia skill was NOT activated.", + "expected_behavior": [ + "The converting-cutile-to-julia skill is NOT loaded", + "The agent provided Elasticsearch query optimization guidance", + "The agent did not mention cuTile, Julia, ct.kernel, or GPU kernel conversion", + "The agent did not run destructive commands" + ] + } +] From 656cab29553f69a05c78bee90287c87b2d21b88b Mon Sep 17 00:00:00 2001 From: nvskills-svc-account Date: Wed, 10 Jun 2026 09:41:47 +0000 Subject: [PATCH 5/5] Attach NVSkills validation signatures Signed-off-by: nvskills-svc-account --- .../BENCHMARK.md | 51 ++++++++++----- .../skill-card.md | 37 +++++++++-- .../skill.oms.sig | 2 +- .../BENCHMARK.md | 47 ++++++++++---- .../skill-card.md | 32 ++++++++-- .../skill.oms.sig | 2 +- .../BENCHMARK.md | 62 ++++++++++++------- .../skill-card.md | 45 +++++++++++--- .../skill.oms.sig | 2 +- 9 files changed, 209 insertions(+), 71 deletions(-) diff --git a/skills/tilegym-converting-cutile-to-julia/BENCHMARK.md b/skills/tilegym-converting-cutile-to-julia/BENCHMARK.md index 5ec1a9b..df2d5ed 100644 --- a/skills/tilegym-converting-cutile-to-julia/BENCHMARK.md +++ b/skills/tilegym-converting-cutile-to-julia/BENCHMARK.md @@ -7,14 +7,19 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s ## Evaluation Summary - Skill: `tilegym-converting-cutile-to-julia` -- Evaluation date: 2026-05-29 +- Evaluation date: 2026-06-10 - NVSkills-Eval profile: `external` +- Environment: `astra-sandbox` +- Dataset: 5 evaluation tasks +- Attempts per task: 1 +- Pass threshold: 50% - Overall verdict: FAIL -- Tier 3 live agent evaluation: not available in this report +The skill should be reviewed before NVSkills-Eval publication. **Skill owners should address the applicable findings below and rerun NVSkills-Eval to refresh this benchmark.** ## Agents Used -- Tier 3 agent details were not available in this report. +- `claude-code` +- `codex` ## Metrics Used @@ -28,19 +33,39 @@ Reported benchmark dimensions: Underlying evaluation signals used in this run: -- No Tier 3 evaluation signal details were available in this report. +- `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access. +- `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow. +- `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage. +- `accuracy` (Accuracy): grades final-answer correctness against the reference answer. +- `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully. +- `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations. +- `token_efficiency` (Token Efficiency): compares token usage with and without the skill. ## Test Tasks -Tier 3 evaluation task details were not available in this report. +The benchmark dataset contained 5 evaluation tasks: + +- Positive tasks: 1 tasks where the skill was expected to activate. +- Negative tasks: 4 tasks where no skill was expected. +- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred. + +Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases. ## Results -Tier 3 dimension rollup was not available in this report. +| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 100% (+20%) | 99% (+14%) | +| Discoverability | 5 | 100% (+20%) | 99% (+8%) | +| Effectiveness | 5 | 99% (+18%) | 96% (+18%) | +| Efficiency | 5 | 96% (+13%) | 97% (+7%) | + +Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available. ## Tier 1: Static Validation Summary -Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 20 total findings. +Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 16 total findings. Top findings: @@ -48,7 +73,7 @@ Top findings: - MEDIUM QUALITY/quality_correctness: Instructions don't mention 'run_script' (`skills/tilegym-converting-cutile-to-julia/SKILL.md`) - MEDIUM QUALITY/quality_efficiency: Deeply nested references in debugging.md (`skills/tilegym-converting-cutile-to-julia/SKILL.md`) - MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/tilegym-converting-cutile-to-julia/SKILL.md`) -- MEDIUM SECURITY/Unknown (SDI-2): A code translation skill (Python to Julia GPU kernel conversion) should not need to output shell commands as part of its (`skill-card.md:29`) +- MEDIUM SECURITY/Unknown (SQP-2): The workflow instructs the agent to auto-proceed through all phases without user confirmation, including writing files t (`translations/workflow.md:26`) ## Tier 2: Deduplication Summary @@ -56,9 +81,6 @@ Tier 2 validation reported findings. NVSkills-Eval ran 2 checks and found 6 tota Top findings: -- HIGH DUPLICATE/duplicate: Duplicate content found across references/testing.md and translations/workflow.md: - "### Step 2: Register in `julia/test/runtests.jl`" in references/testing.md (lines 67-79) - vs "### Step 2: Register in `julia/test/runtests.jl`" in translations/workflow.md (lines 355-363) (`references/testing.md:67`) - HIGH DUPLICATE/duplicate: Duplicate content found within references/critical-rules.md: "# Critical Rules for cuTile Python → Julia Conversion" in references/critical-rules.md (lines 32-33) vs "# Critical Rules for cuTile Python → Julia Conversion" in references/critical-rules.md (lines 34-36) (`references/critical-rules.md:32`) @@ -66,6 +88,9 @@ Top findings: "## Memory Layout Considerations" in references/api-mapping.md (lines 233-248) vs "# Critical Rules for cuTile Python → Julia Conversion" in references/critical-rules.md (lines 8-8) vs "### Step 4: Memory Layout Considerations" in translations/workflow.md (lines 288-305) (`references/api-mapping.md:233`) +- HIGH DUPLICATE/duplicate: Duplicate content found across references/testing.md and translations/workflow.md: + "### Step 2: Register in `julia/test/runtests.jl`" in references/testing.md (lines 67-79) + vs "### Step 2: Register in `julia/test/runtests.jl`" in translations/workflow.md (lines 355-363) (`references/testing.md:67`) - HIGH DUPLICATE/duplicate: Duplicate content found across SKILL.md and references/testing.md and translations/workflow.md: "# Run tests" in SKILL.md (lines 92-100) vs "### Step 1: Create test file `julia/test/test_.jl`" in references/testing.md (lines 43-48) @@ -75,7 +100,3 @@ Top findings: "# Run a single test file directly" in references/testing.md (lines 32-34) vs "# Run a single test file directly" in translations/workflow.md (lines 106-108) vs "# Run a single test file" in translations/workflow.md (lines 370-379) (`references/testing.md:32`) - -## Publication Recommendation - -The skill should be reviewed before NVSkills-Eval publication. Skill owners should address the findings above and rerun NVSkills-Eval to refresh this benchmark. diff --git a/skills/tilegym-converting-cutile-to-julia/skill-card.md b/skills/tilegym-converting-cutile-to-julia/skill-card.md index 81f2976..9595e9c 100644 --- a/skills/tilegym-converting-cutile-to-julia/skill-card.md +++ b/skills/tilegym-converting-cutile-to-julia/skill-card.md @@ -9,7 +9,7 @@ NVIDIA
### License/Terms of Use:
CC-BY-4.0 AND Apache-2.0
## Use Case:
-Developers and engineers converting cuTile Python GPU kernels to cuTile.jl Julia equivalents, porting or translating kernel code, or debugging and optimizing existing Julia cuTile translations.
+Developers and engineers converting cuTile Python GPU kernels to cuTile.jl Julia equivalents, porting kernel implementations across languages, or debugging and optimizing existing Julia cuTile translations.
### Deployment Geography for Use:
Global
@@ -19,7 +19,7 @@ Risk: Review before execution as proposals could introduce incorrect or misleadi Mitigation: Review and scan skill before deployment.
## Reference(s):
-- [API Mapping (Python to Julia)](references/api-mapping.md)
+- [API Mapping Reference](references/api-mapping.md)
- [Critical Rules](references/critical-rules.md)
- [Debugging Guide](references/debugging.md)
- [Testing Patterns](references/testing.md)
@@ -27,11 +27,20 @@ Mitigation: Review and scan skill before deployment.
## Skill Output:
-**Output Type(s):** [Code, Files]
-**Output Format:** [Julia source files (.jl)]
+**Output Type(s):** [Code, Shell commands]
+**Output Format:** [Julia source files and shell commands]
**Output Parameters:** [1D]
**Other Properties Related to Output:** [None]
+## Evaluation Agents Used:
+- claude-code
+- codex
+ + + +## Evaluation Tasks:
+5 evaluation tasks (1 positive skill-activation, 4 negative) under NVSkills-Eval external profile in astra-sandbox environment.
+ ## Evaluation Metrics Used:
Reported benchmark dimensions:
- Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
@@ -40,10 +49,28 @@ Reported benchmark dimensions:
- Effectiveness: Checks whether the agent performs measurably better with the skill than without it.
- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work.
+Underlying evaluation signals used in this run:
+- `security`: Checks for unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution`: Verifies that the agent loaded the expected skill and workflow.
+- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage.
+- `accuracy`: Grades final-answer correctness against the reference answer.
+- `goal_accuracy`: Checks whether the overall user task completed successfully.
+- `behavior_check`: Verifies expected behavior steps, including safety expectations.
+- `token_efficiency`: Compares token usage with and without the skill.
+ + +## Evaluation Results:
+| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 100% (+20%) | 99% (+14%) | +| Discoverability | 5 | 100% (+20%) | 99% (+8%) | +| Effectiveness | 5 | 99% (+18%) | 96% (+18%) | +| Efficiency | 5 | 96% (+13%) | 97% (+7%) | ## Skill Version(s):
-v1.3.0-19-g8da79ba (source: git tag)
+v1.3.0 (source: git tag)
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
diff --git a/skills/tilegym-converting-cutile-to-julia/skill.oms.sig b/skills/tilegym-converting-cutile-to-julia/skill.oms.sig index f753d1d..d0e2b29 100644 --- a/skills/tilegym-converting-cutile-to-julia/skill.oms.sig +++ b/skills/tilegym-converting-cutile-to-julia/skill.oms.sig @@ -1 +1 @@ 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\ No newline at end of file diff --git a/skills/tilegym-converting-cutile-to-triton/BENCHMARK.md b/skills/tilegym-converting-cutile-to-triton/BENCHMARK.md index 9981ba6..b474308 100644 --- a/skills/tilegym-converting-cutile-to-triton/BENCHMARK.md +++ b/skills/tilegym-converting-cutile-to-triton/BENCHMARK.md @@ -7,14 +7,19 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s ## Evaluation Summary - Skill: `tilegym-converting-cutile-to-triton` -- Evaluation date: 2026-05-29 +- Evaluation date: 2026-06-10 - NVSkills-Eval profile: `external` +- Environment: `astra-sandbox` +- Dataset: 5 evaluation tasks +- Attempts per task: 1 +- Pass threshold: 50% - Overall verdict: FAIL -- Tier 3 live agent evaluation: not available in this report +The skill should be reviewed before NVSkills-Eval publication. **Skill owners should address the applicable findings below and rerun NVSkills-Eval to refresh this benchmark.** ## Agents Used -- Tier 3 agent details were not available in this report. +- `claude-code` +- `codex` ## Metrics Used @@ -28,27 +33,47 @@ Reported benchmark dimensions: Underlying evaluation signals used in this run: -- No Tier 3 evaluation signal details were available in this report. +- `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access. +- `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow. +- `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage. +- `accuracy` (Accuracy): grades final-answer correctness against the reference answer. +- `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully. +- `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations. +- `token_efficiency` (Token Efficiency): compares token usage with and without the skill. ## Test Tasks -Tier 3 evaluation task details were not available in this report. +The benchmark dataset contained 5 evaluation tasks: + +- Positive tasks: 1 tasks where the skill was expected to activate. +- Negative tasks: 4 tasks where no skill was expected. +- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred. + +Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases. ## Results -Tier 3 dimension rollup was not available in this report. +| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 100% (+15%) | 99% (+12%) | +| Discoverability | 5 | 100% (+15%) | 99% (+8%) | +| Effectiveness | 5 | 100% (+18%) | 97% (+17%) | +| Efficiency | 5 | 96% (+14%) | 97% (+6%) | + +Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available. ## Tier 1: Static Validation Summary -Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 19 total findings. +Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 16 total findings. Top findings: - MEDIUM QUALITY/quality_efficiency: Deeply nested references in performance-gotchas.md (`skills/tilegym-converting-cutile-to-triton/SKILL.md`) - MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/tilegym-converting-cutile-to-triton/SKILL.md`) -- MEDIUM SECURITY/Unknown (SQP-2): The skill outputs shell commands and Python source files with Triton kernel code, but the skill card does not include ex (`skill-card.md:34`) - LOW QUALITY/quality_discoverability: Description very long (505 chars, recommend 50-150) (`skills/tilegym-converting-cutile-to-triton/SKILL.md`) - LOW QUALITY/quality_discoverability: Broad description without negative triggers may cause over-triggering (`skills/tilegym-converting-cutile-to-triton/SKILL.md`) +- LOW QUALITY/quality_discoverability: No '## Purpose' section (`skills/tilegym-converting-cutile-to-triton/SKILL.md`) ## Tier 2: Deduplication Summary @@ -66,10 +91,6 @@ Top findings: - HIGH DUPLICATE/duplicate: Duplicate content found within translations/workflow.md: "## TMA OPTIMIZATION (Phase c2t-4) {#tma-optimization-phase-c2t-4}" in translations/workflow.md (lines 178-181) vs "### Performance Killer #1: Raw Pointer Arithmetic vs TMA Tensor Descriptors" in translations/workflow.md (lines 329-335) (`translations/workflow.md:178`) -- HIGH DUPLICATE/duplicate: Duplicate content found within translations/workflow.md: +- LOW DUPLICATE/duplicate: Duplicate content found within translations/workflow.md: "### Triton Debug / Profiling" in translations/workflow.md (lines 115-125) vs "# Triton profiling / autotune visibility" in translations/workflow.md (lines 171-177) (`translations/workflow.md:115`) - -## Publication Recommendation - -The skill should be reviewed before NVSkills-Eval publication. Skill owners should address the findings above and rerun NVSkills-Eval to refresh this benchmark. diff --git a/skills/tilegym-converting-cutile-to-triton/skill-card.md b/skills/tilegym-converting-cutile-to-triton/skill-card.md index 482d76c..d60276a 100644 --- a/skills/tilegym-converting-cutile-to-triton/skill-card.md +++ b/skills/tilegym-converting-cutile-to-triton/skill-card.md @@ -1,5 +1,5 @@ ## Description:
-Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit), handling standard in-repo conversion, debugging, and mapping cuTile idioms to Triton equivalents including dual-kernel layout flags.
+Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit), handling standard in-repo conversion, debugging, and mapping cuTile idioms to Triton equivalents including dual-kernel layout flags and autotune grid patterns.
This skill is ready for commercial/non-commercial use.
@@ -9,7 +9,7 @@ NVIDIA
### License/Terms of Use:
CC-BY-4.0 AND Apache-2.0
## Use Case:
-Developers and engineers converting cuTile GPU kernels to Triton for performance optimization and cross-framework portability in GPU computing workflows.
+Developers and engineers who need to convert cuTile GPU kernels to Triton, including debugging runtime failures and optimizing translated kernels for performance parity.
### Deployment Geography for Use:
Global
@@ -33,12 +33,18 @@ Mitigation: Review and scan skill before deployment.
## Skill Output:
**Output Type(s):** [Code, Shell commands]
-**Output Format:** [Python source files and shell commands]
+**Output Format:** [Python source files with inline Triton kernel code]
**Output Parameters:** [1D]
**Other Properties Related to Output:** [None]
+## Evaluation Agents Used:
+- `claude-code`
+- `codex`
+ + + ## Evaluation Tasks:
-Evaluated via NVSkills-Eval (external profile): 9 Tier 1 static validation checks and 2 Tier 2 deduplication checks.
+5 evaluation tasks (1 positive skill-activation, 4 negative) in NVSkills-Eval `external` profile on `astra-sandbox` environment.
## Evaluation Metrics Used:
Reported benchmark dimensions:
@@ -48,7 +54,25 @@ Reported benchmark dimensions:
- Effectiveness: Checks whether the agent performs measurably better with the skill than without it.
- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work.
+Underlying evaluation signals used in this run:
+- `security`: Checks for unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution`: Verifies that the agent loaded the expected skill and workflow.
+- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage.
+- `accuracy`: Grades final-answer correctness against the reference answer.
+- `goal_accuracy`: Checks whether the overall user task completed successfully.
+- `behavior_check`: Verifies expected behavior steps, including safety expectations.
+- `token_efficiency`: Compares token usage with and without the skill.
+ + +## Evaluation Results:
+| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 100% (+15%) | 99% (+12%) | +| Discoverability | 5 | 100% (+15%) | 99% (+8%) | +| Effectiveness | 5 | 100% (+18%) | 97% (+17%) | +| Efficiency | 5 | 96% (+14%) | 97% (+6%) | ## Skill Version(s):
1.0.0 (source: frontmatter)
diff --git a/skills/tilegym-converting-cutile-to-triton/skill.oms.sig b/skills/tilegym-converting-cutile-to-triton/skill.oms.sig index 8445e7b..d097a7f 100644 --- a/skills/tilegym-converting-cutile-to-triton/skill.oms.sig +++ b/skills/tilegym-converting-cutile-to-triton/skill.oms.sig @@ -1 +1 @@ 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\ No newline at end of file diff --git a/skills/tilegym-improve-cutile-kernel-perf/BENCHMARK.md b/skills/tilegym-improve-cutile-kernel-perf/BENCHMARK.md index bd91adc..0c9db1d 100644 --- a/skills/tilegym-improve-cutile-kernel-perf/BENCHMARK.md +++ b/skills/tilegym-improve-cutile-kernel-perf/BENCHMARK.md @@ -7,14 +7,19 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s ## Evaluation Summary - Skill: `tilegym-improve-cutile-kernel-perf` -- Evaluation date: 2026-05-29 +- Evaluation date: 2026-06-10 - NVSkills-Eval profile: `external` +- Environment: `astra-sandbox` +- Dataset: 5 evaluation tasks +- Attempts per task: 1 +- Pass threshold: 50% - Overall verdict: FAIL -- Tier 3 live agent evaluation: not available in this report +The skill should be reviewed before NVSkills-Eval publication. **Skill owners should address the applicable findings below and rerun NVSkills-Eval to refresh this benchmark.** ## Agents Used -- Tier 3 agent details were not available in this report. +- `claude-code` +- `codex` ## Metrics Used @@ -28,19 +33,39 @@ Reported benchmark dimensions: Underlying evaluation signals used in this run: -- No Tier 3 evaluation signal details were available in this report. +- `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access. +- `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow. +- `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage. +- `accuracy` (Accuracy): grades final-answer correctness against the reference answer. +- `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully. +- `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations. +- `token_efficiency` (Token Efficiency): compares token usage with and without the skill. ## Test Tasks -Tier 3 evaluation task details were not available in this report. +The benchmark dataset contained 5 evaluation tasks: + +- Positive tasks: 1 tasks where the skill was expected to activate. +- Negative tasks: 4 tasks where no skill was expected. +- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred. + +Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases. ## Results -Tier 3 dimension rollup was not available in this report. +| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 88% (+8%) | 99% (+12%) | +| Discoverability | 5 | 80% (+0%) | 99% (+7%) | +| Effectiveness | 5 | 85% (+12%) | 97% (+17%) | +| Efficiency | 5 | 83% (-0%) | 97% (+7%) | + +Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available. ## Tier 1: Static Validation Summary -Tier 1 validation reported findings. NVSkills-Eval ran 9 checks and found 39 total findings. +Tier 1 validation reported findings. NVSkills-Eval ran 9 checks and found 37 total findings. Top findings: @@ -52,28 +77,21 @@ Top findings: ## Tier 2: Deduplication Summary -Tier 2 validation reported findings. NVSkills-Eval ran 2 checks and found 5 total findings. +Tier 2 validation reported findings. NVSkills-Eval ran 2 checks and found 4 total findings. Top findings: -- HIGH DUPLICATE/duplicate: Duplicate content found within references/cutile-api-reference.md: - "# Prefer Python arithmetic on host (simpler, no ct import needed)" in references/cutile-api-reference.md (lines 468-470) - vs "# Host — prefer Python arithmetic:" in references/cutile-api-reference.md (lines 652-653) - vs "# CORRECT — tuple of 1, 2, or 3 ints" in references/cutile-api-reference.md (lines 725-730) (`references/cutile-api-reference.md:468`) - HIGH DUPLICATE/duplicate: Duplicate content found across references/ir-dump-guide.md and references/optimization-playbook.md: "### Mitigate" in references/ir-dump-guide.md (lines 209-219) vs "### Mitigate" in references/optimization-playbook.md (lines 323-332) (`references/ir-dump-guide.md:209`) -- HIGH DUPLICATE/duplicate: Duplicate content found within references/optimization-playbook.md: - "### Before" in references/optimization-playbook.md (lines 188-194) - vs "### After" in references/optimization-playbook.md (lines 195-199) (`references/optimization-playbook.md:188`) - HIGH DUPLICATE/duplicate: Duplicate content found across references/optimization-playbook.md and references/perf-knobs-catalog.md: "## Optimization D: Add TF32 Dtype Guard for MMA" in references/optimization-playbook.md (lines 181-187) vs "# Cast FP32 → TF32 for tensor core utilization" in references/optimization-playbook.md (lines 200-209) vs "## 9. TF32 Guard for MMA" in references/perf-knobs-catalog.md (lines 126-142) (`references/optimization-playbook.md:181`) -- HIGH DUPLICATE/duplicate: Duplicate content found across references/ir-dump-guide.md and references/optimization-playbook.md: - "### Detect" in references/ir-dump-guide.md (lines 199-208) - vs "# Check token operations in cuTile IR" in references/optimization-playbook.md (lines 319-322) (`references/ir-dump-guide.md:199`) - -## Publication Recommendation - -The skill should be reviewed before NVSkills-Eval publication. Skill owners should address the findings above and rerun NVSkills-Eval to refresh this benchmark. +- LOW DUPLICATE/duplicate: Duplicate content found within references/cutile-api-reference.md: + "# Prefer Python arithmetic on host (simpler, no ct import needed)" in references/cutile-api-reference.md (lines 468-470) + vs "# Host — prefer Python arithmetic:" in references/cutile-api-reference.md (lines 652-653) + vs "# CORRECT — tuple of 1, 2, or 3 ints" in references/cutile-api-reference.md (lines 725-730) (`references/cutile-api-reference.md:468`) +- LOW DUPLICATE/duplicate: Duplicate content found within references/optimization-playbook.md: + "### Before" in references/optimization-playbook.md (lines 188-194) + vs "### After" in references/optimization-playbook.md (lines 195-199) (`references/optimization-playbook.md:188`) diff --git a/skills/tilegym-improve-cutile-kernel-perf/skill-card.md b/skills/tilegym-improve-cutile-kernel-perf/skill-card.md index a982a0e..c091114 100644 --- a/skills/tilegym-improve-cutile-kernel-perf/skill-card.md +++ b/skills/tilegym-improve-cutile-kernel-perf/skill-card.md @@ -9,7 +9,7 @@ NVIDIA
### License/Terms of Use:
CC-BY-4.0 AND Apache-2.0
## Use Case:
-Developers and engineers use this skill to systematically optimize cuTile GPU kernel performance through iterative profiling, bottleneck analysis, and targeted tuning in the TileGym project.
+Developers and engineers who need to systematically optimize cuTile GPU kernel performance through profiling, bottleneck diagnosis, and iterative tuning in the TileGym project.
### Deployment Geography for Use:
Global
@@ -19,20 +19,29 @@ Risk: Review before execution as proposals could introduce incorrect or misleadi Mitigation: Review and scan skill before deployment.
## Reference(s):
-- [cuTile API Reference](references/cutile-api-reference.md)
-- [cuTile Patterns Reference](references/cutile-patterns-reference.md)
-- [IR Dump Guide](references/ir-dump-guide.md)
-- [Optimization Playbook](references/optimization-playbook.md)
-- [Perf Knobs Catalog](references/perf-knobs-catalog.md)
-- [Performance Model](references/performance-model.md)
+- [optimization-playbook.md](references/optimization-playbook.md)
+- [perf-knobs-catalog.md](references/perf-knobs-catalog.md)
+- [cutile-api-reference.md](references/cutile-api-reference.md)
+- [performance-model.md](references/performance-model.md)
+- [ir-dump-guide.md](references/ir-dump-guide.md)
+- [cutile-patterns-reference.md](references/cutile-patterns-reference.md)
## Skill Output:
**Output Type(s):** [Code, Shell commands, Analysis]
-**Output Format:** [Markdown with inline bash code blocks]
+**Output Format:** [Markdown with inline code blocks and structured performance tables]
**Output Parameters:** [1D]
**Other Properties Related to Output:** [None]
+## Evaluation Agents Used:
+- Claude Code (`claude-code`)
+- Codex (`codex`)
+ + + +## Evaluation Tasks:
+Evaluated against 5 tasks (1 positive skill-activation, 4 negative activation) using NVSkills-Eval external profile in astra-sandbox environment.
+ ## Evaluation Metrics Used:
Reported benchmark dimensions:
- Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
@@ -41,10 +50,28 @@ Reported benchmark dimensions:
- Effectiveness: Checks whether the agent performs measurably better with the skill than without it.
- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work.
+Underlying evaluation signals used in this run:
+- `security`: Checks for unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution`: Verifies that the agent loaded the expected skill and workflow.
+- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage.
+- `accuracy`: Grades final-answer correctness against the reference answer.
+- `goal_accuracy`: Checks whether the overall user task completed successfully.
+- `behavior_check`: Verifies expected behavior steps, including safety expectations.
+- `token_efficiency`: Compares token usage with and without the skill.
+ + +## Evaluation Results:
+| Dimension | Num | `claude-code` | `codex` | +|---|---:|---:|---:| +| Security | 5 | 100% (+0%) | 100% (+0%) | +| Correctness | 5 | 88% (+8%) | 99% (+12%) | +| Discoverability | 5 | 80% (+0%) | 99% (+7%) | +| Effectiveness | 5 | 85% (+12%) | 97% (+17%) | +| Efficiency | 5 | 83% (-0%) | 97% (+7%) | ## Skill Version(s):
-2026.04.11-alpha (source: frontmatter)
+2026.04.11 (source: frontmatter)
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
diff --git a/skills/tilegym-improve-cutile-kernel-perf/skill.oms.sig b/skills/tilegym-improve-cutile-kernel-perf/skill.oms.sig index 39cffff..9261d70 100644 --- a/skills/tilegym-improve-cutile-kernel-perf/skill.oms.sig +++ b/skills/tilegym-improve-cutile-kernel-perf/skill.oms.sig @@ -1 +1 @@ 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