gza-log-insights
Analyze gza run logs to find recurring anti-patterns, wasted effort, and suggest AGENTS.md or workflow improvements
Asset-Profil
Coding- und Entwickler-Agents
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Szenario
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Agent-Fit
Claude Code + CLI + Codex
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add mhawthorne/gza --skill gza-log-insights
Wartung
Aktuell
1 Tage seit dem letzten Push
Risiko
Prüfung nötig
Permission surface may require sandboxing
GitHub-Qualität
11
57/100 Qualität · 67/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
Permission surface may require sandboxing · No critical security, quality, usefulness, or compliance issues found.
Agent-Adoptionskarte
Vertrauen, Audit und Installationsbereitschaft auf einen Blick
Diese Werte kombinieren öffentliche Repository-Metadaten, OpenAgentSkill-Reviewsignale, Wartungsaktualität und Installationsbereitschaft. Sie helfen bei der Vorauswahl, ersetzen aber keine menschliche Prüfung.
Qualität
VielversprechendUseful candidate, but compare it with alternatives before adopting.
Vertrauen
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Prüfung nötigMaschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
OpenAgentSkill Trust Score v5
Menschliche Prüfung vor Installation
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
11 GitHub-Stars
Repository-Aktivität
11 Stars und 1 Forks
Wartung
1 Tage seit dem letzten Push
Lizenz
MIT
Installieren
npx skills add mhawthorne/gza --skill gza-log-insights
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
shell or command execution, filesystem or document access
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Starker README/SKILL.md-Kontext
Risikoübersicht
Vor Produktion prüfen
- 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
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist angegeben
- Noch keine Agent-Proven-Ergebnisbelege
Agent-lesbare Metadaten
Maschinenlesbare Entscheidungsdaten für diesen Skill.
Nutze diesen Block oder das eingebettete JSON, um zu entscheiden, ob ein Agent diesen Skill installieren, eine Alternative wählen oder zuerst menschliche Prüfung anfordern soll.
Geeignete Aufgaben
- Browser automation-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Navigate pages
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add mhawthorne/gza --skill gza-log-insights
- Richtlinie
- Prüfen
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 59/100
- Audit
- 73/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add mhawthorne/gza --skill gza-log-insightsNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- production agents without a repository review
- Low GitHub adoption signal
- No critical security, quality, usefulness, or compliance issues found.
- Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
Agent-Sicherheit v2
41/100 · Automatische Installation vermeiden
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
Hoch
Shell- oder Befehlsausführung
Die Skill-Metadaten verweisen auf Terminal-, CLI-, Shell-, Subprozess- oder Befehlsausführungs-Workflows.
Mittel
Netzwerkzugriff
Die Skill ruft wahrscheinlich Remote-Seiten, APIs, Repositories oder externe Dienste ab.
Mittel
Dateisystemzugriff
Die Skill kann Projektdateien, Dokumente, generierte Artefakte oder den lokalen Arbeitsbereich lesen oder schreiben.
Mittel
Datenbankzugriff
Die Skill kann Schemata prüfen, Datenbanken abfragen oder mit persistenten Speichern arbeiten.
- Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
- Permission surface may require sandboxing
Installationsziele
Diesen Skill im Agent-Workflow installieren
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
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-insightsAgent-Auflösungsplan
Lass einen Agent die Eignung vor der Installation prüfen.
Die Resolve API liefert die beste Skill, Alternativen, Sicherheitsrichtlinien, Auditnotizen, Installationsziel und einen direkt nutzbaren Prompt.
JSON öffnen
/api/agent/resolve?task=Use%20gza-log-insights%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20gza-log-insights%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/mhawthorne-gza-log-insights/install
Agent sollte prüfen
- 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.
Prompt kopieren
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.Agent-Übergabe
Gib dem Agent den Installationspfad, nicht noch ein Verzeichnis.
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
Installationsübergabe
/api/skills/mhawthorne-gza-log-insights/install
LLM-Textformat
/api/skills/mhawthorne-gza-log-insights/install?format=text
Alternativen finden
/api/skills/search?q=gza-log-insights&limit=3
Agent-Prompt
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-insightsRegistry-Metadaten
Agent-lesbares Profil für die automatische Skill-Auswahl.
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Manifest
/api/registry/manifest/mhawthorne-gza-log-insights
LLM-Text
/api/registry/manifest/mhawthorne-gza-log-insights?format=text
Installationsalias
/api/registry/install/mhawthorne-gza-log-insights
Empfehlen
/api/registry/recommend?task=Use%20gza-log-insights%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Browser automation
Plattformen
Claude Code
Audit-Bericht
Prüfung nötig · 73/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Needs validation for Browser automation
Do a manual repository review before adding this to an agent workflow.
Rolle im Stack
Validierung nötig
Primäre Eignung
Browser automation
Vertrauenslabel
Manuelle Prüfung nötig
Installationspfad
Befehl bereit
Verwenden wenn
- Browser automation-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 57/100
- 2 OpenAgentSkill-Interaktionen
zuerst prüfen
- Low GitHub adoption signal
- No critical security, quality, usefulness, or compliance issues found.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Browser automation-Aufgabe vollständig aus.
- 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.
Vertrauensprofil
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub-Akzeptanz
Beheben11 GitHub-Stars
Star-/Fork-Aktivität
Beheben11 Stars und 1 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
Bestanden1 Tage seit dem letzten Push
Lizenzklarheit
BestandenMIT
Positive Signale
- KI-Prüfung genehmigt
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Kürzlich gewartetes Repository
- Der Installationsbefehl weist kein offensichtliches Hochrisikomuster auf
- Ergebniszyklus ist bereit, benötigt aber den ersten echten Agent-Lauf
Vor Installation prüfen
- 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
- Noch keine echten Agent-Ergebnisberichte
- Vor unbeaufsichtigter Installation ist menschliche Prüfung erforderlich
Empfohlene Aktion
Choose a stronger alternative or inspect the source manually before any install attempt.
Qualitätsprofil
Vielversprechend Kandidat für Agent-Workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow-Eignung
Diese Skill in diesen Szenarien nutzen
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.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
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.
Alternativen-Shortlist
Vor Installation vergleichen
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Übersicht
--- 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:
Technische Details
- Version
- 1.0.0
- Lizenz
- MIT
- Letzte Aktualisierung
- 21. Aug. 2026
- Veröffentlicht
- 21. Aug. 2026
Entscheidungsübersicht
Validierung nötig
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 74/100
- Wartung
- 100/100
- Installieren
- 92/100
Von Agent belegte Evidenz
Von Agent belegte Evidenz
Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.
- Erfolgsrate
- —
- Letzter Fehler
- —
- Ergebnisse
- 0
- Ausgabequalität
- —
- Fehlgeschlagen
- 0
- Nicht relevant
- 0
- Installationen
- 0
- Durch Risiko blockiert
- 0
- Einrichtung erforderlich
- 0
- Produktion
- 0
Noch keine Agent-Ergebnisdaten. Der erste Lauf kann Erfolg, Einrichtungsbedarf, Risikoblockaden, Fehler oder Irrelevanz über /api/agent/outcome melden.
Installieren
Zum Agent-Workflow hinzufügen
Kostenlos und Open Source. Bericht vor der Installation in Produktions-Agents prüfen.
Wachstums-Loop
Share-Kit
Szenariobasierter Entwurf für gza-log-insights, bereit für einen manuellen X-Post.
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
Optionale Antwort mit Installationsbefehl
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
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- mhawthorne
- Quelle
- mhawthorne/gza
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird mhawthorne zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](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)Autor
mhawthorne
@mhawthorne
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 11
- Qualitätswert
- 31/100
- Letzter GitHub-Push
- 21. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 2
- Installationskopien
- 0
- Externe Klicks
- 0
Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.
Vertrauen & Sicherheit
Do not auto-install
- GitHub-Akzeptanz11 GitHub-StarsBeheben
- Star-/Fork-Aktivität11 Stars und 1 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarBeheben
- Aktuelle Wartung1 Tage seit dem letzten PushBestanden
- LizenzklarheitMITBestanden
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikocommand execution surface, database surfaceInfo
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