ai-assist-git-pr-review
Perform a standards-based code review on a GitHub Pull Request, then post the findings as inline review comments and mark the PR as 'Requested changes'. Reads all of the agents files that exist in the repository under review (AGENTS.md, .agents-docs/, CLAUDE.md on the PR's base b
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 jparkerweb/ai-assist-skills --skill ai-assist-git-pr-review
Wartung
Aktuell
Heute gepusht
Risiko
Prüfung nötig
Lizenz ist unklar
GitHub-Qualität
88
61/100 Qualität · 62/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
Lizenz ist unklar · Dependency or permission surface needs review
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
88 GitHub-Stars
Repository-Aktivität
88 Stars und 12 Forks
Wartung
Heute gepusht
Lizenz
Unbekannt
Installieren
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-review
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
secrets or environment access, shell or command execution
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Starker README/SKILL.md-Kontext
Risikoübersicht
Vor Produktion prüfen
- Repository license is unknown; clarify licensing for the skill.
- Lizenz ist unklar
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist unklar
- 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
- GitHub automation-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Inspect repository metadata
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-review
- Richtlinie
- Blockieren
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 54/100
- Audit
- 70/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-reviewNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- production agents without a repository review
- Repository license is unknown; clarify licensing for the skill.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Lizenz ist unklar
Alternative
Code Review
168.6K Stars
npx skills add mattpocock/skills --skill code-review
Alternative
Grill With Docs
164.7K Stars
npx skills add mattpocock/skills --skill grill-with-docs
Alternative
To Spec
164.7K Stars
npx skills add mattpocock/skills --skill to-spec
Alternative
To Tickets
176.7K Stars
npx skills add mattpocock/skills --skill to-tickets
Agent-Sicherheit v2
22/100 · Automatische Installation vermeiden
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
Hoch
Shell- oder Befehlsausführung
Die Skill-Metadaten verweisen auf Terminal-, CLI-, Shell-, Subprozess- oder Befehlsausführungs-Workflows.
Mittel
Browser automation
Skill may drive a browser or interact with web pages.
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.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Lizenz ist unklar
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 jparkerweb-ai-assist-git-pr-reviewAgent-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%20ai-assist-git-pr-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20ai-assist-git-pr-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/jparkerweb-ai-assist-git-pr-review/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 ai-assist-git-pr-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-git-pr-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-git-pr-review/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-review
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/jparkerweb-ai-assist-git-pr-review/install
LLM-Textformat
/api/skills/jparkerweb-ai-assist-git-pr-review/install?format=text
Alternativen finden
/api/skills/search?q=ai-assist-git-pr-review&limit=3
Agent-Prompt
Use ai-assist-git-pr-review for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-git-pr-review/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-reviewRegistry-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/jparkerweb-ai-assist-git-pr-review
LLM-Text
/api/registry/manifest/jparkerweb-ai-assist-git-pr-review?format=text
Installationsalias
/api/registry/install/jparkerweb-ai-assist-git-pr-review
Empfehlen
/api/registry/recommend?task=Use%20ai-assist-git-pr-review%20in%20an%20agent%20workflow&limit=3
Agent-Fit
GitHub automation
Use-Case-Tags
Plattformen
Claude Code
Audit-Bericht
Prüfung nötig · 70/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for GitHub automation
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
GitHub automation
Vertrauenslabel
Zuerst prototypisieren
Installationspfad
Befehl bereit
Verwenden wenn
- GitHub automation-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 61/100
- 3 OpenAgentSkill-Interaktionen
zuerst prüfen
- Repository license is unknown; clarify licensing for the skill.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine GitHub 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
Prüfen88 GitHub-Stars
Star-/Fork-Aktivität
Prüfen88 Stars und 12 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
BestandenHeute gepusht
Lizenzklarheit
PrüfenUnbekannt
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
- Repository license is unknown; clarify licensing for the skill.
- Lizenz ist unklar
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 88 GitHub stars
- Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
- License clarity: Unknown
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- 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
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
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.
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.
Alternativen-Shortlist
Vor Installation vergleichen
Similar skills that may fit this task.
Code Review
Review a branch or diff against repository standards and the originating spec in two independent analysis passes.
Grill With Docs
A relentless interview that pressure-tests a plan against the codebase, sharpens domain language, and updates CONTEXT.md and ADRs when decisions become durable.
To Spec
Turn the current conversation and codebase context into a structured implementation spec, then publish it to the configured project issue tracker.
To Tickets
Break a plan, spec, or conversation into independently actionable tracer-bullet tickets with explicit blocking relationships.
Übersicht
--- name: ai-assist-git-pr-review description: "Perform a standards-based code review on a GitHub Pull Request, then post the findings as inline review comments and mark the PR as 'Requested changes'. Reads all of the agents files that exist in the repository under review (AGENTS.md, .agents-docs/, CLAUDE.md on the PR's base branch) — the repo's full documented standards, not just any agents files the PR happens to change — checks the diff against them plus general best practices, and gates every write behind explicit approval. Use this skill whenever the user wants to code-review a PR, review a pull request, check a PR against standards, request changes on a PR, or gives you a GitHub PR link and asks for a review. Also triggers on: 'review this PR', 'code review', 'review PR', 'check this pull request', 'request changes', 'review against our standards', or a bare github.com/.../pull/<n> URL with review intent. This is a review-only skill — it never approves, merges, closes, or pushes code." argument-hint: "[PR URL] — e.g. 'https://github.com/org/repo/pull/42' or 'review https://github.com/org/repo/pull/42'" ---
# PR REVIEW
**Objective:** Review a GitHub PR against documented standards (its agents files) plus general engineering best practices, then — after your explicit approval — post the findings as inline review comments and submit the review as **REQUEST_CHANGES**.
**Role:** Senior reviewer writing for senior engineers. Read the diff deeply, ground every finding in evidence (a cited standard or a concrete code risk), and keep comments short and actionable. A good review reads like it came from a careful teammate, not a linter.
Start each response with `🔍 [PR Review — Step X: Name]` so the user can follow the flow.
## Safety Model
| Level | Actions | Behavior | |-------|---------|----------| | **Auto** | Read PR metadata, diff, changed files, agents files, existing review threads; analyze and de-duplicate | Execute immediately | | **Gated** | Post the review (inline comments + REQUEST_CHANGES) | Preview every comment → explicit approval → post → verify | | **Blocked** | Approve, merge, close, push, edit code, dismiss reviews | Never. This skill only *requests changes*. |
The single write in this skill is posting the review. Nothing reaches GitHub until the user has seen every comment and approved. This matters because a review is visible to the whole team and notifies the author — surprising them with unreviewed machine output erodes trust in the tool.
## Prerequisites
**gh CLI (BLOCKING — before any `gh` command):** run `gh --version` first. If it fails, the CLI isn't installed — tell the user to install and authenticate GitHub CLI (`gh auth login`), then stop. If `gh auth status` fails, prompt them to authenticate. Don't attempt other `gh` calls until both pass.
## Process
### Step 1: Get the PR link
The PR URL may be in `$ARGUMENTS`. If it isn't, ask: *"Which PR would you like me to review? Paste the GitHub PR link."* Don't guess or assume the current branch — this skill reviews an arbitrary PR by URL, which may live in a different repo than the current directory.
Parse the URL `https://github.com/<owner>/<repo>/pull/<number>` into `$owner`, `$repo`, `$number`. If it doesn't match that shape, ask the user to re-paste a full PR URL.
### Step 2: Load PR context
Fetch metadata (single call):
```bash gh api repos/$owner/$repo/pulls/$number \ --jq '{title, state, draft, headSha: .head.sha, baseRef: .base.ref, changedFiles: .changed_files, additions, deletions, author: .user.login}' ```
- **state != "open"** → stop: "PR #N is <state>. Reviews can only be posted on open PRs." (A closed/merged PR can't receive a REQUEST_CHANGES review.) - Capture `headSha` — you'll pin the review to it so comments land on the exact revision you reviewed. - If it's a draft, note it but continue (drafts can still be reviewed).
### Step 3: Gather the standards (the agents files)
The whole point of this review is conformance to *this repo's* documented standards, so read them from the PR's repo — not the local workspace, which may be a different project. **These are the agents files that exist in the repository, which define the standards — not merely the agents files the PR happens to modify.** A PR usually doesn't touch the agents docs at all; you still read the repo's full set to know the rules the changed code must follow. See `references/posting-review.md` §Gathering Agents Files for the exact `gh api` calls. In short:
1. List the **entire** repo tree on the PR's base branch and find every agents file present in the repo: `AGENTS.md`, `CLAUDE.md`, anything under `.agents-docs/`, and any `AGENTS.md`/`CLAUDE.md` in subdirectories. Do this against the full tree, independent of what the PR changed. 2. Read them. `AGENTS.md` files are usually a lightweight index that links to detailed docs under `.agents-docs/` — follow the links for any area the diff touches (e.g. if the PR changes C# code, read the C# coding-standards doc). 3. Distill a working checklist of concrete, checkable rules (commit format, naming, layering/wrapper boundaries, test structure, forbidden patterns, etc.). Keep the rules and *where each came from* so every finding can cite its source.
If the repo has **no** agents files, tell the user and offer to proceed on general best practices alone — the review is weaker without documented standards, so let them decide.
### Step 4: Fetch the diff and review
Get the changed files and the diff:
```bash gh pr diff $number --repo $owner/$repo ```
For any changed file where you need full surrounding context (not just the hunk), read it from the PR head: see `references/posting-review.md` §Reading a File at the PR Head. Don't review from hunks alone when a rule depends on context the hunk doesn't show (e.g. "private methods below public methods" needs the whole class).
**Evaluate each change against, in priority order:** 1. **Documented standards** from Step 3 — the primary bar. A violation of a written team rule is always worth flagging. 2. **General best practices** — obvious bugs, security issues (injection, secrets, auth gaps), missing null/error handling, race conditions, performance cliffs, and clear maintainability problems, even when no agents file mentions them.
**What NOT to flag (this matters — over-flagging erodes trust in the review):** - **Style the docs only *illustrate*, not *mandate*.** A code sample in an agents doc shows one way to write something; it is not a rule. Bracket-quoting object names, `BEGIN/END` wrappers, brace placement, and similar formatting choices are not findings unless a doc states them as an explicit requirement ("must", "always", a rule in prose — not just an example snippet). When in doubt, treat it as illustrative and stay silent. - **Personal-preference refactors** with no functional or documented basis. If you'd only be substituting your taste for the author's, don't comment. - **Speculative concerns** you can't ground in the diff, a cited standard, or a concrete risk. Every comment must trace to a rule or a real problem — if you can't name the basis, drop it.
A short review of real issues is worth far more than a long one padded with style opinions.
**Categorize each finding by severity** (this is what the user asked to see so they can triage NIT vs must-fix):
| Severity | Meaning | Examples | |----------|---------|----------| | **CRITICAL** | Must fix before merge — correctness, security, or data-loss risk; or a hard team rule that will break CI/deploy | SQL injection, leaked secret, null deref on a hot path, wrong commit format that the CI gate rejects, calling ServiceRepositories directly when the repo forbids it | | **WARNING** | Should fix — bug risk, missing validation/error handling, a documented convention violated, measurable perf issue | Swallowed exception, missing test for new logic, naming that violates the coding-standards doc, N+1 query | | **NIT** | Optional — style, readability, minor refactor with no functional impact | Import ordering, comment wording, a slightly cleaner idiom |
Anchor each finding to a specific `path` + `line` **that appears in the diff** (`side: RIGHT` for added/context lines, `LEFT` for deleted). This skill posts **inline comments only — no summary write-up of the PR.** A finding that doesn't map neatly to a changed line should be anchored to the nearest related changed line (e.g. attach a "missing test" note to the new file's `CREATE`/signature line); if it genuinely can't be tied to any changed line, drop it rather than writing a prose summary. See `references/posting-review.md` §Anchoring Rules.
Be disciplined about noise: don't invent findings to look thorough. If the PR is genuinely clean, it's fine to end up with only one or two comments — quality over volume.
### Step 5: De-duplicate against existing review threads
Before presenting anything, read the review activity that is **already on the PR** and classify each of your candidate findings as either **new** or **already-raised**. Other reviewers — human teammates and bots like GitHub Copilot and Devin Review — have often already raised the same points, and the author may have fixed them, replied with a rationale, or consciously declined. You must **not silently repost** an already-raised point (that relitigates a settled thread and signals you didn't read it) — but you must **not silently drop it either**. Instead, set already-raised findings aside and surface them to the user in Step 6 as items to review, so *they* decide whether the prior dismissal/resolution was actually correct. This step is mandatory, not optional. See `references/posting-review.md` §Gathering Existing Review Threads for the exact `gh api`/GraphQL calls. In short:
1. Fetch **all** prior review activity: inline review comments (`/pulls/$number/comments`), review summaries (`/pulls/$number/reviews`), and issue-level comments (`/issues/$number/comments`) — from every author, including bots (`Copilot`, `devin-ai-integration[bot]`, etc.) and the PR author's own replies. Also pull each thread's **resolved/outdated** status via GraphQL, which is a strong "already handled" signal. 2. For every candidate finding from Step 4, check whether an existing thread already covers the same issue on the same file/area. Classify it as **already-raised** if any of these hold (record *which* signal, *who* raised it, and *how* it was handled — replied/declined/resolved/outdated/fixed — you'll show this in Step 6): - A prior comment makes substantially the same point (even if worded differently or at a slightly different line). - The author (or anyone) **replied** to that thread dismissing it with a rationale or explaining it's intentional. - The thread is marked **resolved** or **outdated**, or the code it pointed at has since changed (a fix likely landed). 3. Everything else is a **new** finding. For each already-raised finding, also form a quick judgment: does the prior resolution look sound, or does it seem prematurely dismissed / not actually addressed? You'll present that assessment alongside the item so the user can decide whether to re-raise it.
Both buckets go to the user in Step 6 — **new** findings as proposed comments, **already-raised** findings as review items. Never auto-post an already-raised finding; only include it in the posted review if the user explicitly tells you to re-raise it.
### Step 6: Present findings for approval (GATED)
Show the user the complete set of inline comments before anything is posted. Use this structure:
``` 🔍 Review of PR #<n> — <title> Standards source: <which agents files informed this>
New findings — will be posted if approved (<count>): 1. [CRITICAL] <path>:<line> — <one-line finding> (cites: <standard or "best practice">) > <the exact comment body that will be posted> 2. [WARNING] <path>:<line> — ... 3. [NIT] <path>:<line> — ...
Already raised by others — NOT posted, for your review (<coun
Technische Details
- Version
- 1.0.0
- Lizenz
- Unknown
- Letzte Aktualisierung
- 22. Aug. 2026
- Veröffentlicht
- 21. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 63/100
- 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 ai-assist-git-pr-review, bereit für einen manuellen X-Post.
ai-assist-git-pr-review: Perform a standards-based code review on a GitHub Pull Request, then post the findings as inl... 88 stars https://www.openagentskill.com/skills/jparkerweb-ai-assist-git-pr-review?ref=x
Optionale Antwort mit Installationsbefehl
Listing + install path for ai-assist-git-pr-review: https://www.openagentskill.com/skills/jparkerweb-ai-assist-git-pr-review?ref=x Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-git-pr-review
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- jparkerweb
- 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 jparkerweb 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/jparkerweb-ai-assist-git-pr-review)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-git-pr-review)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-git-pr-review/audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-git-pr-review)Autor
jparkerweb
@jparkerweb
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 88
- Qualitätswert
- 36/100
- Letzter GitHub-Push
- 22. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 3
- Installationskopien
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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-Akzeptanz88 GitHub-StarsPrüfen
- Star-/Fork-Aktivität88 Stars und 12 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle WartungHeute gepushtBestanden
- LizenzklarheitUnbekanntPrüfen
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikocommand execution surface, credential or environment accessBeheben
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