gza-log-insights
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements
Profil aset
Agent pemrograman dan pengembangan
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Skenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Kecocokan Agent
Claude Code + CLI + Codex
Cocok untuk Codex, Claude Code, Cursor, CLI, atau Agent khusus.
Pasang
Siap
npx skills add mhawthorne/gza --skill gza-log-insights
Pemeliharaan
Terkini
1 hari sejak push
Risiko
Perlu ditinjau
Permission surface may require sandboxing
Kualitas GitHub
11
57/100 Kualitas · 67/100 Kepercayaan
Tag cakupan
Catatan ulasan
Permission surface may require sandboxing · No critical security, quality, usefulness, or compliance issues found.
Kartu adopsi Agent
Kepercayaan, audit, dan kesiapan pemasangan dalam sekali lihat
Skor ini menggabungkan metadata repositori publik, sinyal ulasan OpenAgentSkill, kebaruan pemeliharaan, dan kesiapan pemasangan. Ini adalah sinyal shortlist, bukan pengganti peninjauan manusia.
Kualitas
MenjanjikanUseful candidate, but compare it with alternatives before adopting.
Kepercayaan
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Perlu ditinjauTinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.
Trust Score OpenAgentSkill v5
Tinjauan manusia sebelum pemasangan
Choose a stronger alternative or inspect the source manually before any install attempt.
Star
11 star GitHub
Aktivitas repositori
11 star dan 1 fork
Pemeliharaan
1 hari sejak push
Lisensi
MIT
Pasang
npx skills add mhawthorne/gza --skill gza-log-insights
Keamanan pemasangan
Jalur pemasangan paket atau runtime standar
Cakupan izin
shell or command execution, filesystem or document access
Hasil Agent
Belum ada data hasil Agent
Dokumentasi
Konteks README/SKILL.md kuat
Ringkasan risiko
Tinjau sebelum produksi
- No critical security, quality, usefulness, or compliance issues found.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, filesystem or document access
Kesiapan pemasangan
Jalur pemasangan tersedia
- Jalur pemasangan tersedia
- Bukti repositori tersedia
- Lisensi dinyatakan
- Belum ada bukti hasil Agent-Proven
Metadata yang dapat dibaca Agent
Data keputusan yang dapat dibaca mesin untuk skill ini.
Gunakan blok ini atau JSON tersemat untuk memutuskan apakah Agent perlu memasang skill ini, memilih alternatif, atau meminta tinjauan manusia terlebih dahulu.
Tugas yang sesuai
- alur kerja Browser automation
- Tim Claude Code
- builders willing to evaluate younger projects
- Navigate pages
Agent yang sesuai
Keputusan pemasangan
- Perintah
- npx skills add mhawthorne/gza --skill gza-log-insights
- Kebijakan
- Tinjau
- Tinjauan manusia
- Ya
Kepercayaan dan risiko
- Kepercayaan
- 59/100
- Audit
- 73/100
- Tingkat risiko
- Perlu ditinjau
Lingkar hasil
- Endpoint
- /api/agent/outcome
- ID event
- resolve
- Hasil
- 5
Perintah pemasangan
npx skills add mhawthorne/gza --skill gza-log-insightsJangan gunakan ketika
- Tim yang membutuhkan SLA dengan dukungan vendor
- production agents without a repository review
- Low GitHub adoption signal
- No critical security, quality, usefulness, or compliance issues found.
- Petunjuk izin berisiko tinggi: eksekusi shell atau perintah
Keamanan Agent v2
41/100 · Hindari pemasangan otomatis
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
Tinggi
Eksekusi shell atau perintah
Metadata skill merujuk terminal, CLI, shell, subprocess, atau alur kerja eksekusi perintah.
Sedang
Akses jaringan
Skill kemungkinan mengambil halaman jarak jauh, API, repositori, atau layanan eksternal.
Sedang
Akses sistem file
Skill dapat membaca atau menulis file proyek, dokumen, artefak yang dihasilkan, atau status workspace lokal.
Sedang
Akses database
Skill dapat memeriksa skema, mengkueri database, atau bekerja dengan penyimpanan persisten.
- Petunjuk izin berisiko tinggi: eksekusi shell atau perintah
- Permission surface may require sandboxing
Target pemasangan
Pasang skill ini di alur Agent Anda
Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install mhawthorne-gza-log-insightsRencana resolusi Agent
Biarkan Agent memverifikasi kecocokan sebelum memasang.
API Resolve mengembalikan skill utama, alternatif, kebijakan keamanan, catatan audit, target pemasangan, dan prompt siap pakai.
Buka JSON
/api/agent/resolve?task=Use%20gza-log-insights%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Teks Resolve
/api/agent/resolve?task=Use%20gza-log-insights%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Serah-terima pemasangan
/api/skills/mhawthorne-gza-log-insights/install
Agent harus memeriksa
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Salin prompt
Task: Use gza-log-insights in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20gza-log-insights%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mhawthorne-gza-log-insights/install
Install command: npx skills add mhawthorne/gza --skill gza-log-insights
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Serah-terima Agent
Berikan jalur pemasangan kepada Agent, bukan direktori lain.
Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.
Serah-terima pemasangan
/api/skills/mhawthorne-gza-log-insights/install
Format teks LLM
/api/skills/mhawthorne-gza-log-insights/install?format=text
Cari alternatif
/api/skills/search?q=gza-log-insights&limit=3
Prompt Agent
Use gza-log-insights for this task. Review https://www.openagentskill.com/api/skills/mhawthorne-gza-log-insights/install, then install with: npx skills add mhawthorne/gza --skill gza-log-insightsMetadata Registry
Profil yang dapat dibaca Agent untuk pemilihan skill otomatis.
API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.
Manifest
/api/registry/manifest/mhawthorne-gza-log-insights
Teks LLM
/api/registry/manifest/mhawthorne-gza-log-insights?format=text
Alias pemasangan
/api/registry/install/mhawthorne-gza-log-insights
Rekomendasikan
/api/registry/recommend?task=Use%20gza-log-insights%20in%20an%20agent%20workflow&limit=3
Kecocokan Agent
Browser automation
Tag use case
Platform
Claude Code
Laporan audit
Perlu ditinjau · 73/100
Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.
Panel keputusan Agent
Needs validation for Browser automation
Do a manual repository review before adding this to an agent workflow.
Peran di stack
Perlu validasi
Kecocokan utama
Browser automation
Label kepercayaan
Perlu tinjauan manual
Jalur pemasangan
Perintah siap
Gunakan saat
- alur kerja Browser automation
- Tim Claude Code
- builders willing to evaluate younger projects
Bukti
- recent repository activity
- install command or GitHub repo available
- profil kualitas 57/100
- 2 event interaksi OpenAgentSkill
tinjau dulu
- Low GitHub adoption signal
- No critical security, quality, usefulness, or compliance issues found.
Jalur implementasi
- 1Pasang di Agent sandbox dan jalankan satu tugas Browser automation dari awal hingga akhir.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Profil kepercayaan
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Adopsi GitHub
Perbaiki11 star GitHub
Aktivitas star/fork
Perbaiki11 star dan 1 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus1 hari sejak push
Kejelasan lisensi
LulusMIT
Sinyal positif
- Tinjauan AI disetujui
- Jalur pemasangan tersedia
- Bukti repositori tersedia
- Repositori yang baru dipelihara
- Perintah pemasangan tidak memiliki pola berisiko tinggi yang jelas
- Loop hasil siap tetapi membutuhkan eksekusi Agent nyata pertama
Tinjau sebelum memasang
- No critical security, quality, usefulness, or compliance issues found.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, filesystem or document access
- GitHub adoption: 11 GitHub stars
- Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata
- Permission surface: shell or command execution, filesystem or document access
- Belum ada laporan hasil Agent nyata
- Tinjauan manusia diperlukan sebelum pemasangan tanpa pengawasan
Tindakan yang disarankan
Choose a stronger alternative or inspect the source manually before any install attempt.
Profil kualitas
Menjanjikan kandidat untuk alur kerja Agent
Useful candidate, but compare it with alternatives before adopting.
Kecocokan alur kerja
Gunakan skill ini pada skenario berikut
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Kecocokan alur kerja
Tambahkan ke alur kerja lengkap
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Daftar alternatif
Bandingkan sebelum memasang
Similar skills that may fit this task.
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
MoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
Ringkasan
--- name: gza-log-insights description: Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements allowed-tools: Read, Bash(uv run python -c:*), Bash(wc:*), Bash(ls:*), Bash(head:*), Bash(tail:*) version: 1.0.0 public: false ---
# Gza Log Insights
Analyze gza execution logs to find recurring anti-patterns, wasted compute, and actionable improvements. This skill scans task transcript logs (`*.log`) and paired ops logs (`*.ops.jsonl`), aggregates patterns across many runs, and produces recommendations for AGENTS.md updates, prompt improvements, or workflow changes.
## Process
### Step 1: Locate and inventory logs
Find the log directory and count available logs:
```bash uv run python -c " from gza.config import load_config cfg = load_config() log_dir = cfg.get_log_dir() print(str(log_dir)) " ```
Then list and count: ```bash ls <log_dir> | wc -l ```
If no logs exist, report that and stop.
### Step 2: Run the analysis script
Run the following comprehensive analysis across all log files. This script extracts patterns from the JSONL log format (where each line is a JSON entry with types: system, assistant, user, result).
```bash uv run python -c " import json, os, re, sys from collections import Counter, defaultdict from pathlib import Path
from gza.config import load_config cfg = load_config() log_dir = cfg.get_log_dir()
log_files = sorted(log_dir.glob('*.log')) ops_log_files = sorted(log_dir.glob('*.ops.jsonl')) if not log_files: print('No log files found.') sys.exit(0)
# --- Counters --- bare_commands = Counter() # commands missing 'uv run' failed_bash = Counter() # bash commands that failed git_errors = Counter() # git-specific errors tool_distribution = Counter() # overall tool usage skill_errors = Counter() # failed skill executions import_errors = Counter() # Python import errors file_too_large = 0 # Read tool file-too-large errors no_module_pytest = 0 # 'No module named pytest' sqlite_not_found = 0 # sqlite3 not available worktree_git_errors = 0 # git fails in cleaned-up worktrees test_runs_per_log = [] # (filename, count) for test-heavy logs result_subtypes = Counter() # success vs error_max_turns etc costs = [] # per-log costs high_cost_logs = [] # logs with cost info repeated_patterns = Counter() # any command run 5+ times in a single log
BARE_PREFIXES = ['gza ', 'pytest', 'mypy ', 'python ']
for logfile in log_files: tool_uses_in_log = {} # tool_use_id -> command test_runs = 0 bash_cmds_in_log = Counter() fname = logfile.name
with open(logfile) as f: for line in f: line = line.strip() if not line: continue try: entry = json.loads(line) except (json.JSONDecodeError, ValueError): continue
etype = entry.get('type', '')
# --- System init: skip ---
# --- Assistant messages: extract tool calls --- if etype == 'assistant': msg = entry.get('message', {}) for c in msg.get('content', []): if c.get('type') == 'tool_use': tool = c.get('name', '') inp = c.get('input', {}) tid = c.get('id', '') tool_distribution[tool] += 1
if tool == 'Bash': cmd = inp.get('command', '').strip() tool_uses_in_log[tid] = cmd bash_cmds_in_log[cmd[:80]] += 1
# Check bare commands for prefix in BARE_PREFIXES: if cmd.startswith(prefix) and not cmd.startswith('uv run'): bare_commands[cmd[:100]] += 1
# Count test/lint runs if 'pytest' in cmd or 'mypy' in cmd: test_runs += 1
# --- User messages: extract tool results --- if etype == 'user': msg = entry.get('message', {}) for c in msg.get('content', []): if c.get('type') != 'tool_result': continue tid = c.get('tool_use_id', '') is_err = c.get('is_error', False) content = str(c.get('content', ''))
# Skill errors — 'Execute skill: X' with is_error=True is NORMAL # in headless mode (skill loaded successfully). Only count as # error if the content indicates a real failure (e.g. 'Unknown skill'). if is_err and 'skill' in content.lower(): if 'Execute skill' in content: pass # Normal headless behavior, not an error elif 'Unknown skill' in content: skill_name = content.split('Unknown skill:')[-1].strip()[:40] if 'Unknown skill:' in content else content[:60] skill_errors[skill_name] += 1 else: skill_errors[content[:60]] += 1
# File too large if 'exceeds maximum allowed' in content: file_too_large += 1
# Specific error categories if 'not a git repository' in content: worktree_git_errors += 1 if 'sqlite3: command not found' in content: sqlite_not_found += 1 if 'No module named pytest' in content: no_module_pytest += 1 if 'ImportError' in content: idx = content.find('ImportError') import_errors[content[idx:idx+80]] += 1
# Failed bash commands if tid in tool_uses_in_log: cmd = tool_uses_in_log[tid] exit_match = re.search(r'Exit code (\d+)', content[:30]) if exit_match and exit_match.group(1) != '0': short = cmd[:60] failed_bash[short] += 1 if cmd.strip().startswith('git'): git_errors[short] += 1
# --- Result entry --- if etype == 'result': result_subtypes[entry.get('subtype', '?')] += 1 cost = entry.get('total_cost_usd', 0) if cost: costs.append(cost) high_cost_logs.append((fname, cost, entry.get('num_turns', 0)))
if test_runs > 0: test_runs_per_log.append((fname, test_runs))
# Repeated commands in single log for cmd, count in bash_cmds_in_log.items(): if count >= 5: repeated_patterns[cmd] += 1
# ========== OUTPUT ========== print('=' * 70) print('GZA LOG INSIGHTS REPORT') print(f'Analyzed {len(log_files)} transcript logs and {len(ops_log_files)} ops logs') print('=' * 70)
# Section 1: Outcome summary print('\n## Task Outcomes') for st, count in result_subtypes.most_common(): print(f' {st}: {count}') if costs: print(f' Total spend: \${sum(costs):.2f} across {len(costs)} tasks') print(f' Average cost: \${sum(costs)/len(costs):.2f}/task')
# Section 2: Bare commands if bare_commands: print(f'\n## Bare Commands (missing uv run) — {sum(bare_commands.values())} total') print('These commands were invoked without \"uv run\" prefix, which may fail in') print('environments without the package installed globally.') for cmd, count in bare_commands.most_common(15): print(f' {count}x: {cmd}')
# Section 3: Git errors if worktree_git_errors or git_errors: print(f'\n## Git Errors — {worktree_git_errors} "not a git repository" + {sum(git_errors.values())} failed git commands') print('Includes stale worktrees, missing repos, and other git failures.') for cmd, count in git_errors.most_common(10): print(f' {count}x: {cmd}')
# Section 4: Missing tools/modules missing = [] if no_module_pytest: missing.append(f'\"No module named pytest\": {no_module_pytest} occurrences') if sqlite_not_found: missing.append(f'\"sqlite3: command not found\": {sqlite_not_found} occurrences') if file_too_large: missing.append(f'Read tool file-too-large errors: {file_too_large} occurrences') if import_errors: for err, count in import_errors.most_common(5): missing.append(f'{err}: {count}x') if missing: print(f'\n## Missing Dependencies / Environment Issues') for m in missing: print(f' - {m}')
# Section 5: Skill resolution errors (not counting normal 'Execute skill' responses) if skill_errors: print(f'\n## Skill Resolution Errors — {sum(skill_errors.values())} total') print('Note: \"Execute skill: X\" with is_error=True is normal in headless mode.') print('Only \"Unknown skill\" and other genuine failures are counted here.') for skill, count in skill_errors.most_common(): print(f' {count}x: {skill}')
# Section 6: Test-heavy logs (potential loops) heavy = [(f, c) for f, c in test_runs_per_log if c >= 8] if heavy: print(f'\n## Test-Heavy Runs (8+ test/lint invocations — possible loops)') for fname, count in sorted(heavy, key=lambda x: -x[1])[:10]: print(f' {count} runs: {fname[:70]}')
# Section 7: Repeated commands within single logs if repeated_patterns: print(f'\n## Repeated Commands (same command 5+ times in one session)') for cmd, num_logs in repeated_patterns.most_common(10): print(f' in {num_logs} log(s): {cmd}')
# Section 8: Failed bash commands if failed_bash: print(f'\n## Most Common Bash Failures — {sum(failed_bash.values())} total') for cmd, count in failed_bash.most_common(15): print(f' {count}x: {cmd}')
# Section 9: Cost outliers if high_cost_logs: expensive = sorted(high_cost_logs, key=lambda x: -x[1])[:5] print(f'\n## Most Expensive Runs') for fname, cost, turns in expensive: print(f' \${cost:.2f} ({turns} turns): {fname[:60]}')
# Section 10: Tool distribution print(f'\n## Tool Usage Distribution') for tool, count in tool_distribution.most_common(): print(f' {tool}: {count}')
print() " ```
### Step 3: Read AGENTS.md for current guidance
Read the project's AGENTS.md to understand what instructions agents already have. This helps identify gaps — patterns in the logs that aren't addressed by existing documentation.
### Step 4: Synthesize recommendations
Based on the analysis, produce actionable recommendations in these categories:
#### A. AGENTS.md Updates For each anti-pattern found in logs, suggest a specific line to add to AGENTS.md that would prevent the issue. Examples:
- If bare `pytest`/`mypy`/`gza` commands are common: > Add to AGENTS.md: "Always use `uv run pytest`, `uv run mypy`, `uv run gza` — never bare commands. The project uses uv for dependency management."
- If git worktree errors are frequent: > Add to AGENTS.md: "When running in a worktree, verify git works before running git commands. If the worktree's .git file is stale, do not attempt git init or repair — report the issue instead."
- If `python -m pytest` is used instead of `uv run pytest`: > Add to AGENTS.md: "Use `uv run pytest` (not `python -m pytest` or bare `pytest`). The uv tool manages the virtual environment."
- If sqlite3 is used directly: > Add to AGENTS.md: "Do not use the `sqlite3` CLI — it may not be installed. Use `uv run python -c 'from gza.db import ...'` to query the database."
- If the Read tool hits file-too-large errors:
Detail teknis
- Versi
- 1.0.0
- Lisensi
- MIT
- Pembaruan terakhir
- 21 Agu 2026
- Diterbitkan
- 21 Agu 2026
Ringkasan keputusan
Perlu validasi
recent repository activity
Audit
Tinjauan pemasangan
Tinjauan pemasangan dan adopsi
- Keamanan
- 74/100
- Pemeliharaan
- 100/100
- Pasang
- 92/100
Bukti tervalidasi Agent
Bukti tervalidasi Agent
Laporan hasil setelah resolve, tinjau, pasang, dan satu eksekusi terbatas.
- Tingkat sukses
- —
- Kegagalan terbaru
- —
- Hasil
- 0
- Kualitas output
- —
- Gagal
- 0
- Tidak relevan
- 0
- Pemasangan
- 0
- Diblokir risiko
- 0
- Perlu penyiapan
- 0
- Produksi
- 0
Belum ada data hasil Agent. Eksekusi pertama dapat melaporkan keberhasilan, kebutuhan setup, blok risiko, kegagalan, atau tidak relevan melalui /api/agent/outcome.
Pasang
Tambahkan ke alur Agent
Gratis dan sumber terbuka. Tinjau laporan sebelum memasang pada Agent produksi.
Siklus pertumbuhan
Kit berbagi
Draf berbasis skenario untuk gza-log-insights, siap untuk posting manual di X.
gza-log-insights: Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or... 11 stars https://www.openagentskill.com/skills/mhawthorne-gza-log-insights?ref=x
Balasan opsional dengan perintah pemasangan
Listing + install path for gza-log-insights: https://www.openagentskill.com/skills/mhawthorne-gza-log-insights?ref=x Install: npx skills add mhawthorne/gza --skill gza-log-insights
Sumber listing
Diindeks Registry
Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.
- Kreator
- mhawthorne
- Sumber
- mhawthorne/gza
- Diindeks oleh
- Indeks komunitas OpenAgentSkill
Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.
Klaim skill iniKlaim pemilik
Klaim listing skill ini
Listing Diindeks Registry ini dikaitkan dengan mhawthorne, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.
Kit backlink kreator
Tambahkan badge bukti ke README Anda
Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.
[](https://www.openagentskill.com/skills/mhawthorne-gza-log-insights)
[](https://www.openagentskill.com/skills/mhawthorne-gza-log-insights)
[](https://www.openagentskill.com/skills/mhawthorne-gza-log-insights/audit)
[](https://www.openagentskill.com/skills/mhawthorne-gza-log-insights)Penulis
mhawthorne
@mhawthorne
Tag
Kecocokan platform
Sinyal kesehatan
- Star GitHub
- 11
- Skor kualitas
- 31/100
- Push GitHub terakhir
- 21 Agu 2026
- Petunjuk framework
- Tidak diketahui
- Tampilan OpenAgentSkill
- 2
- Salinan pemasangan
- 0
- Klik keluar
- 0
Sinyal komunitas
Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.
Kepercayaan & keamanan
Do not auto-install
- Adopsi GitHub11 star GitHubPerbaiki
- Aktivitas star/fork11 star dan 1 fork; aktivitas issue tidak tersedia dalam metadata saat iniPerbaiki
- Pemeliharaan terbaru1 hari sejak pushLulus
- Kejelasan lisensiMITLulus
- Kelengkapan README/SKILL.mdMetadata memuat konteks penggunaan dan alur kerja yang cukupLulus
- Risiko dependensi/runtimecommand execution surface, database surfaceInfo
Skill terkait
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K StarMoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarCua
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
21.4K Star