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Agent skill

Clean Startup Log

by guqiong96 in guqiong96/Lsglang

Clean up noisy startup warnings and spurious prints in SGLang server logs.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Clean Startup Log

skills CLI
$ npx skills add guqiong96/Lsglang --skill clean-startup-log -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install guqiong96/Lsglang clean-startup-log --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/guqiong96/Lsglang.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/clean-startup-log .claude/skills/clean-startup-log && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
clean-startup-log
GitHub stars
144
Used in
1 other repo
Token cost
~4.5k tokens
SKILL.md length
1,471 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
Apache-2.0

At a glance

Clean up noisy startup warnings and spurious prints in SGLang server logs.

  • Works in 12 steps: Launch a server and capture the log → Compare against the clean reference log → Classify each noisy line → …
  • Users ask to clean up unwanted warnings
  • SKILL.md covers Workflow, Key Architecture: Why Logs…, Known Noise Sources and Fixes… and Investigation Techniques
  • Calls uv

What it does

Clean Startup Log is an agent skill from guqiong96/Lsglang. Clean up noisy startup warnings and spurious prints in SGLang server logs. Use when users ask to clean up unwanted warnings, deprecation messages, or third-party noise in the server startup output.

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering. It works with SGLang. The repository describes itself as: Lsglang is a special extension of sglang that fully utilizes CPU and GPU computing resources with an efficient GPU parallel + NUMA parallel architecture, suitable for MOE model… The licence is Apache-2.0.

When your agent uses it

  • Users ask to clean up unwanted warnings
  • Deprecation messages
  • Third-party noise in the server startup output

Example prompts

  • “/clean-startup-log”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Launch a server and capture the log
  2. Compare against the clean reference log
  3. Classify each noisy line
  4. Present findings before fixing
  5. Apply fixes and verify
  6. torchao "Skipping import of cpp extensions due to incompatible torch version"
  7. "torch_dtype is deprecated! Use dtype instead!" (PARTIALLY FIXED)
  8. "BaseImageProcessorFast is deprecated"
  9. "No platform detected. Using base SRTPlatform with defaults."
  10. NCCL version 2.27.7+cuda13.0
  11. [Gloo] Rank X is connected to Y peer ranks
  12. torchao SyntaxWarning: invalid escape sequence

What it can do on your machine

Read from SKILL.md and the folder at commit 4f19944. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Clean Startup Log loads about 4.5k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 1,471 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from guqiong96/Lsglang at commit 4f19944, republished under its Apache-2.0 licence (© guqiong96). 1,471 words, ~4,543 tokens.

Download SKILL.mdSave it as .claude/skills/clean-startup-log/SKILL.md (or your agent's skills folder).
name
clean-startup-log
description
Clean up noisy startup warnings and spurious prints in SGLang server logs. Use when users ask to clean up unwanted warnings, deprecation messages, or third-party noise in the server startup output.
disable-model-invocation
true

Clean Up SGLang Server Startup Logs

Goal: ensure the server startup log is clean and minimal, with no spurious warnings, deprecation messages, or unformatted prints from third-party libraries.

Workflow

1. Launch a server and capture the log
bash
uv run sglang serve --model-path Qwen/Qwen3-8B 2>&1 | tee /tmp/startup_log.txt

Wait until the server prints The server is fired up and ready to roll!, then Ctrl-C.

For TP>1 testing:

bash
uv run sglang serve --model-path Qwen/Qwen3-8B --tp 2 2>&1 | tee /tmp/startup_log.txt

For MoE / hybrid-SWA models (e.g. gpt-oss), test separately — they exercise different code paths:

bash
uv run sglang serve --model-path openai/gpt-oss-20b 2>&1 | tee /tmp/startup_log.txt
2. Compare against the clean reference log

Read /tmp/startup_log.txt and compare it against the reference log at the bottom of this file. Identify lines that:

  • Do NOT have the [timestamp] or [timestamp TPx] logger prefix
  • Contain WARNING, deprecated, is deprecated, or similar noise
  • Are printed by third-party libraries (transformers, torchao, NCCL, Gloo, tqdm, etc.)
  • Are duplicate/redundant with information already logged by SGLang
  • Appear multiple times due to ModelConfig being constructed in multiple processes
3. Classify each noisy line

For each noisy line, determine:

CategoryAction
SGLang code using wrong APIFix the SGLang code (e.g., replace deprecated API with new one)
SGLang code logging at wrong levelChange log level (e.g., warning -> debug for non-actionable messages)
Duplicated across processesDowngrade to debug — info logged in one process becomes noise in 3-4
Third-party lib prints at import timeSuppress the logger or redirect stdout during that import
C-level print from .so libraryRedirect fd 1 during the specific C call, or accept it if too invasive
Real warning the user should seeKeep it
4. Present findings before fixing

List all noisy lines with their source and proposed fix. Ask the user to review before making changes.

5. Apply fixes and verify

After approval, apply fixes one at a time, re-launch the server, and verify each fix works.

Key Architecture: Why Logs Repeat

ModelConfig is constructed 3-4 times during startup across different processes:

  1. Main process: ServerArgs.__post_init__() → get_model_config() → ModelConfig()
  2. Scheduler subprocess: Scheduler.init_model_config() → ModelConfig.from_server_args()
  3. Scheduler subprocess: TpModelWorker._init_model_config() → ModelConfig.from_server_args()
  4. Main process: TokenizerManager.init_model_config() → ModelConfig.from_server_args()

Similarly, get_tokenizer() is called 5 times across processes:

  1. resolve_auto_parsers (main) — template_detection.py
  2. Scheduler.init_tokenizer() (scheduler subprocess) — scheduler.py
  3. DetokenizerManager (detokenizer subprocess) — detokenizer_manager.py
  4. TpModelWorker.__init__() (scheduler subprocess) — tp_worker.py
  5. TokenizerManager (main) — tokenizer_manager.py

Any logger.info() or logger.warning() in ModelConfig.__init__() or get_tokenizer() will appear 3-5 times. Keep these at logger.debug().

Known Noise Sources and Fixes (from past sessions)

1. torchao "Skipping import of cpp extensions due to incompatible torch version"
  • Source: torchao/__init__.py — printed via logger.warning() when torch version < 2.11.0
  • Trigger: sglang/__init__.py -> _apply_hf_patches() -> _patch_removed_symbols() -> from transformers.models.llama import modeling_llama -> deep import chain -> transformers/quantizers/auto.py -> from .quantizer_torchao import TorchAoHfQuantizer -> imports torchao
  • Fix: In hf_transformers_patches.py::_patch_removed_symbols(), temporarily set the torchao logger level to ERROR around the modeling_llama import:
    python
    _torchao_logger = logging.getLogger("torchao")
_prev_level = _torchao_logger.level
_torchao_logger.setLevel(logging.ERROR)
try:
    from transformers.models.llama import modeling_llama
finally:
    _torchao_logger.setLevel(_prev_level)
2. "torch_dtype is deprecated! Use dtype instead!" (PARTIALLY FIXED)
  • Source: transformers/configuration_utils.py — the torch_dtype property warns via logger.warning_once()
  • Trigger: Model files accessing config.torch_dtype instead of config.dtype
  • Fix applied so far: Only models/gpt_oss.py (lines 222, 471) — tested with openai/gpt-oss-20b.
  • Remaining files that still use config.torch_dtype (fix each only after testing with the corresponding model):
    • models/bailing_moe.py (line 302)
    • models/llada2.py (line 313)
    • models/qwen3_next.py (lines 192, 209)
    • models/qwen3_5.py (line 245)
    • models/nano_nemotron_vl.py (lines 79, 102, 284)
    • models/llava.py (lines 732, 734-737)
    • model_loader/loader.py (line 649)
  • Note: common.py was already fixed in a prior session. If new model files are added with config.torch_dtype, the warning will reappear — grep for \.torch_dtype to find them.
  • Important: Only change config.torch_dtype → config.dtype for models you have actually tested. The dtype property should return the same value, but verify per-model to avoid regressions.
3. "BaseImageProcessorFast is deprecated"
  • Source: transformers/utils/import_utils.py — the lazy module __getattr__ warns when BaseImageProcessorFast is accessed
  • Trigger: base_processor.py and ernie45_vl.py have from transformers import BaseImageProcessorFast at top level. These are imported eagerly via tokenizer_manager.py -> multimodal_processor.py -> base_processor.py, even for non-multimodal models.
  • Fix: Replace from transformers import BaseImageProcessorFast with from transformers import BaseImageProcessor and update all isinstance(..., BaseImageProcessorFast) checks to isinstance(..., BaseImageProcessor)
4. "No platform detected. Using base SRTPlatform with defaults."
  • Source: sglang/srt/platforms/__init__.py — logger.warning()
  • Fix: Change to logger.debug() — this is expected on machines without a platform plugin and not actionable.
5. NCCL version 2.27.7+cuda13.0
  • Source: C-level print from libnccl.so during ncclCommInitRank() call
  • Status: Accepted as-is. SGLang already logs the version via sglang is using nccl==X.Y.Z. The C-level print cannot be suppressed without redirecting stdout fd, which is too invasive. NCCL_DEBUG=WARN does not suppress it in NCCL 2.27+.
6. [Gloo] Rank X is connected to Y peer ranks
  • Source: C++ Gloo library print during process group init
  • Status: Accepted as-is. From C++ code inside PyTorch's Gloo backend.
7. torchao SyntaxWarning: invalid escape sequence
  • Source: torchao/quantization/quant_api.py — a raw string with unescaped \.
  • Status: Upstream torchao bug. Cannot fix from SGLang side.
8. tqdm progress bars (e.g., Multi-thread loading shards, Capturing batches)
  • Status: These are expected and useful. They show progress during weight loading and CUDA graph capture. Keep them.
9. CUTE_DSL "Unexpected error during package walk" — double-logged (FIXED)
  • Source: nvidia-cutlass-dsl package at .venv/.../cutlass/cutlass_dsl/cutlass.py, line 391. Logger named CUTE_DSL with its own StreamHandler.
  • Trigger: During CUDA graph capture, cutlass DSL walks packages and hits an unexpected error for cutlass.cute.experimental.
  • Root cause of double-logging: The CUTE_DSL logger has propagate=True (default), so the warning is emitted by both the CUTE_DSL handler (with its format) and the root logger (SGLang's format).
  • Fix applied: In entrypoints/engine.py, changed CUTE_DSL_LOG_LEVEL from "30" (WARNING) to "40" (ERROR). This suppresses the WARNING at both the CUTE_DSL logger and root propagation levels. The env var controls both logger.setLevel() and console_handler.setLevel() in cutlass's setup_log().
Show full SKILL.md (594 more words)Show less
10. ModelConfig init logs repeated 3x (FIXED)
  • Lines: "Downcasting torch.float32 to ...", "Hybrid swa model: ...", "DeepGemm is enabled but ..."
  • Source: configs/model_config.py — _get_and_verify_dtype() (line 1457), _derive_hybrid_model() (line 497), _verify_quantization() (line 1236)
  • Root cause: ModelConfig.__init__() is called 3-4 times in different processes (see "Key Architecture" above). Each construction fires the same log lines.
  • Fix applied: Downgraded all three from logger.info()/logger.warning() to logger.debug(). The dtype is already visible in server_args and Load weight end. Hybrid SWA info appears in Tree cache initialized. DeepGemm is not actionable.
11. Tokenizer retry/fallback messages repeated 3-4x (FIXED)
  • Lines: "Tokenizer loaded as generic TokenizersBackend ... retrying", "Loading tokenizer ... directly as PreTrainedTokenizerFast", "Tokenizer for ... loaded as generic TokenizersBackend. Set --trust-remote-code"
  • Source: utils/hf_transformers/tokenizer.py — _resolve_tokenizers_backend() (line 215), _load_tokenizer_by_declared_class() (line 110), final warning (line 244)
  • Root cause: 5 separate get_tokenizer() calls across processes (see "Key Architecture" above). Each produces 3 log lines. Concurrent subprocess launches cause interleaved/doubled output.
  • Fix applied: Downgraded all three from logger.warning()/logger.info() to logger.debug().
12. Template detection logs — 5 lines consolidated to 1 (FIXED)
  • Lines: "Detected reasoning config '...' from template rule '...'", "Detected reasoning parser '...' from template rule '...'", "Detected tool-call parser '...' from template rule '...'", "Auto-detected reasoning parser: ...", "Auto-detected tool-call parser: ..."
  • Source: managers/template_detection.py (lines 337, 370) logged each detection rule match. managers/template_manager.py (lines 177-182) logged summary lines that duplicated the detection logs.
  • Fix applied: Removed per-rule logs from template_detection.py. Consolidated the 5 lines in template_manager.py into a single summary: "Auto-detected template features: reasoning_config=..., reasoning_parser=..., tool_call_parser=..."
13. KV cache dtype logged separately from allocation (FIXED)
  • Lines: "Using KV cache dtype: torch.bfloat16" then "KV Cache is allocated. #tokens: ..., K size: ..., V size: ..."
  • Source: model_executor/model_runner.py (line 2217) and mem_cache/memory_pool.py (line 740)
  • Fix applied: Removed the standalone dtype log from model_runner.py. Added dtype field to the allocation log in memory_pool.py: "KV Cache is allocated. dtype: torch.bfloat16, #tokens: ..., K size: ..., V size: ..."
14. CUTLASS backend warning — B200 → SM100, warning → info (FIXED)
  • Line: "CUTLASS backend is disabled when piecewise cuda graph is enabled due to TMA descriptor initialization issues on B200."
  • Source: layers/attention/flashinfer_backend.py (line 249)
  • Fix applied: Changed "B200" to "SM100 GPUs" (the condition checks is_sm100_supported() which matches SM10x, not just B200). Downgraded from logger.warning() to logger.info() since it's an expected automatic fallback.
15. max_total_num_tokens and Tree cache initialized log ordering
  • Issue: max_total_num_tokens=... appears before Tree cache initialized:... even though tree cache is conceptually part of memory setup.
  • Root cause: max_total_num_tokens is logged inside init_model_worker() (scheduler.py:972), which runs before build_kv_cache() (scheduler.py:425) where tree cache is created.
  • Status: Not fixed — reordering was reverted. Acceptable as-is.
16. Ignore import error when loading sglang.srt.models.midashenglm
  • Source: models/registry.py (line 109) — logger.warning() during import_model_classes() which iterates all model modules via pkgutil.iter_modules
  • Trigger: The midashenglm model depends on torchaudio, which fails to load
  • Status: Should be downgraded to logger.debug() — not actionable when loading an unrelated model. Same pattern exists in managers/multimodal_processor.py, dllm/algorithm/__init__.py, multimodal_gen/runtime/models/registry.py.
17. Multiple NUMA nodes found for GPU X
  • Source: utils/numa_utils.py (line 112) — logger.warning()
  • Status: Could be downgraded to logger.info(). The situation is handled gracefully ("Using the first one") and not actionable.
18. Warmup /model_info access log
  • Source: Uvicorn access log, triggered by SGLang's own warmup at entrypoints/http_server.py (line 1877)
  • Status: SGLang talking to itself. Could suppress uvicorn access logger during warmup, or exclude /model_info from warmup access logging.

Investigation Techniques

Trace what triggers an import
python
import sys
_real_import = __builtins__.__import__
def _tracing_import(name, *args, **kwargs):
    if 'TARGET_MODULE' in name:
        import traceback
        print(f'=== Importing {name} ===')
        traceback.print_stack()
    return _real_import(name, *args, **kwargs)
__builtins__.__import__ = _tracing_import
Trace what triggers a logger warning
python
import logging, traceback
class TraceHandler(logging.Handler):
    def emit(self, record):
        if 'SEARCH_STRING' in record.getMessage():
            traceback.print_stack()
h = TraceHandler()
h.setLevel(logging.WARNING)
logging.getLogger('TARGET_LOGGER_NAME').addHandler(h)
Find C-level prints in .so files
bash
strings /path/to/library.so | grep "SEARCH_STRING"
Find all config.torch_dtype accesses (for deprecation warning)
bash
grep -rn '\.torch_dtype' python/sglang/srt/models/ python/sglang/srt/model_loader/ python/sglang/srt/utils/hf_transformers/

Reference: Clean Startup Log (TP=1, Qwen3-8B)

[2026-05-24 00:52:39] Attention backend not specified. Use trtllm_mha backend by default.
[2026-05-24 00:52:39] TensorRT-LLM MHA only supports page_size of 16, 32 or 64, changing page_size from None to 64.
[2026-05-24 00:52:40] server_args=ServerArgs(model_path='Qwen/Qwen3-8B', ...)
[2026-05-24 00:52:40] Multiple NUMA nodes found for GPU 0: [...]. Using the first one.
[2026-05-24 00:52:42] Using default HuggingFace chat template with detected content format: string
[2026-05-24 00:52:42] Auto-detected template features: reasoning_config=..., reasoning_parser=qwen3, tool_call_parser=qwen
[2026-05-24 00:52:50] Init torch distributed begin.
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
[2026-05-24 00:52:50] Init torch distributed ends. elapsed=0.21 s, mem usage=0.10 GB
[2026-05-24 00:52:51] Load weight begin. avail mem=275.75 GB
[2026-05-24 00:52:51] Found local HF snapshot for Qwen/Qwen3-8B at ...; skipping download.
Multi-thread loading shards: 100% Completed | 5/5 [00:01<00:00,  2.62it/s]
[2026-05-24 00:52:54] Load weight end. elapsed=2.62 s, type=Qwen3ForCausalLM, avail mem=260.48 GB, mem usage=15.28 GB.
[2026-05-24 00:52:54] KV Cache is allocated. dtype: torch.bfloat16, #tokens: 1707904, K size: 117.28 GB, V size: 117.28 GB
[2026-05-24 00:52:54] Memory pool end. avail mem=25.28 GB
[2026-05-24 00:52:54] CUTLASS backend is disabled when piecewise cuda graph is enabled due to TMA descriptor initialization issues on SM100 GPUs. Using auto backend instead for stability.
[2026-05-24 00:52:54] Capture cuda graph begin. This can take up to several minutes. avail mem=24.16 GB
[2026-05-24 00:52:54] Capture cuda graph bs [1, 2, 4, ...]
Capturing batches (bs=1 avail_mem=23.56 GB): 100% | 52/52 [00:05<00:00, 10.36it/s]
[2026-05-24 00:53:00] Capture cuda graph end. Time elapsed: 5.38 s. mem usage=0.60 GB. avail mem=23.56 GB.
[2026-05-24 00:53:00] Capture piecewise CUDA graph begin. avail mem=23.56 GB
[2026-05-24 00:53:00] Capture cuda graph num tokens [4, 8, 12, ...]
Compiling num tokens (num_tokens=4): 100% | 74/74 [00:09<00:00, 7.44it/s]
Capturing num tokens (num_tokens=4 avail_mem=21.24 GB): 100% | 74/74 [00:07<00:00, 10.44it/s]
[2026-05-24 00:53:18] Capture piecewise CUDA graph end. Time elapsed: 18.18 s. mem usage=2.32 GB. avail mem=21.24 GB.
[2026-05-24 00:53:20] Tree cache initialized: source=default impl=RadixCache hybrid_swa=False hybrid_ssm=False hierarchical=False streaming_wrapped=False
[2026-05-24 00:53:20] max_total_num_tokens=1707904, chunked_prefill_size=16384, max_prefill_tokens=16384, max_running_requests=4096, context_len=40960, available_gpu_mem=21.24 GB
[2026-05-24 00:53:20] INFO:     Started server process [1964249]
[2026-05-24 00:53:20] INFO:     Waiting for application startup.
[2026-05-24 00:53:20] Using default chat sampling params from model generation config: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
[2026-05-24 00:53:20] INFO:     Application startup complete.
[2026-05-24 00:53:20] INFO:     Uvicorn running on http://127.0.0.1:30000 (Press CTRL+C to quit)
[2026-05-24 00:53:21] Prefill batch, #new-seq: 1, #new-token: 64, ...
[2026-05-24 00:53:21] INFO:     127.0.0.1:... - "POST /generate HTTP/1.1" 200 OK
[2026-05-24 00:53:21] The server is fired up and ready to roll!

Note: [Gloo] messages and tqdm progress bars are acceptable. The key is no warnings or deprecation messages from transformers, torchao, or other third-party libraries. The CUTLASS backend is disabled message is now info level, not a warning.

© guqiong96, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/clean-startup-log of guqiong96/Lsglang.

Open the folder on GitHubat commit 4f19944

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in guqiong96/Lsglang, which our catalogue first saw on October 7, 2026.

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Questions about Clean Startup Log

What does Clean Startup Log do?

Clean up noisy startup warnings and spurious prints in SGLang server logs. Clean Startup Log is an agent skill from guqiong96/Lsglang. Clean up noisy startup warnings and spurious prints in SGLang server logs.

When should I use Clean Startup Log?

Clean Startup Log fits situations like: users ask to clean up unwanted warnings; deprecation messages; third-party noise in the server startup output.

How do I install Clean Startup Log in Claude Code?

Run `npx skills add guqiong96/Lsglang --skill clean-startup-log -a claude-code`. Or copy the skill folder (.claude/skills/clean-startup-log in guqiong96/Lsglang) into .claude/skills/clean-startup-log in your project. Claude Code loads it when a task matches its description.

How do I install Clean Startup Log in Codex?

Run `npx skills add guqiong96/Lsglang --skill clean-startup-log -a codex`. Or copy the skill folder (.claude/skills/clean-startup-log in guqiong96/Lsglang) into .agents/skills/clean-startup-log in your project. Codex loads it when a task matches its description.

Can I use Clean Startup Log in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add guqiong96/Lsglang --skill clean-startup-log -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/clean-startup-log, .gemini/skills/clean-startup-log, .github/skills/clean-startup-log and .opencode/skills/clean-startup-log in your project.

What does Clean Startup Log need to run?

Going by SKILL.md and its folder, Clean Startup Log needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Clean Startup Log access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Clean Startup Log safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Clean Startup Log use?

Clean Startup Log is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Clean Startup Log use?

About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Clean Startup Log?

Skills that share tags, products or a category with Clean Startup Log: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Kl Consistency Test (sgl-project/sglang, 37k stars), Sglang Cherrypick (sgl-project/sglang, 37k stars) and Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Clean Startup Log?

guqiong96 (a GitHub user) maintains it in guqiong96/Lsglang, which has 144 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 5, 2026.

Source: guqiong96/Lsglang on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.