amq-spec
>-
Asset-Profil
Recherche und Wissensarbeit
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Szenario
Recherche-Agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent-Fit
Claude Code + OpenAI Agents + CLI
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add avivsinai/agent-message-queue --skill amq-spec
Wartung
Aktuell
1 Tage seit dem letzten Push
Risiko
Prüfung nötig
Dependency or permission surface needs review
GitHub-Qualität
82
65/100 Qualität · 63/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
Dependency or permission surface needs review · Permission surface may require sandboxing
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
82 GitHub-Stars
Repository-Aktivität
82 Stars und 9 Forks
Wartung
1 Tage seit dem letzten Push
Lizenz
MIT
Installieren
npx skills add avivsinai/agent-message-queue --skill amq-spec
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
secrets or environment access, shell or command execution
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Usable metadata, review docs
Risikoübersicht
Vor Produktion prüfen
- Skill relies on external AMQ tool; not self-contained and requires prior installation, but this is clearly stated in pre-flight checks.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 82 GitHub stars
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
- Research-Agent-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Suchquellen
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add avivsinai/agent-message-queue --skill amq-spec
- Richtlinie
- Blockieren
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 55/100
- Audit
- 74/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add avivsinai/agent-message-queue --skill amq-specNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- production agents without a repository review
- Skill relies on external AMQ tool; not self-contained and requires prior installation, but this is clearly stated in pre-flight checks.
- No OpenAgentSkill engagement data yet
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
Alternative
Last30days Skill
53.5K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
Academic Research Skills
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
GPT Researcher
28.0K Stars
npx skills add assafelovic/gpt-researcher
Alternative
DeepResearch
19.8K Stars
npx skills add Alibaba-NLP/DeepResearch
Agent-Sicherheit v2
34/100 · 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
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.
Hoch
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Dependency or permission surface needs review
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 avivsinai-amq-specAgent-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%20amq-spec%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20amq-spec%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/avivsinai-amq-spec/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 amq-spec in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20amq-spec%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/avivsinai-amq-spec/install
Install command: npx skills add avivsinai/agent-message-queue --skill amq-spec
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/avivsinai-amq-spec/install
LLM-Textformat
/api/skills/avivsinai-amq-spec/install?format=text
Alternativen finden
/api/skills/search?q=amq-spec&limit=3
Agent-Prompt
Use amq-spec for this task. Review https://www.openagentskill.com/api/skills/avivsinai-amq-spec/install, then install with: npx skills add avivsinai/agent-message-queue --skill amq-specRegistry-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/avivsinai-amq-spec
LLM-Text
/api/registry/manifest/avivsinai-amq-spec?format=text
Installationsalias
/api/registry/install/avivsinai-amq-spec
Empfehlen
/api/registry/recommend?task=Use%20amq-spec%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Recherche-Agents
Use-Case-Tags
Plattformen
Claude Code, OpenAI Agents
Audit-Bericht
Prüfung nötig · 74/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
Recherche-Agents
Vertrauenslabel
Zuerst prototypisieren
Installationspfad
Befehl bereit
Verwenden wenn
- Research-Agent-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 65/100
zuerst prüfen
- Skill relies on external AMQ tool; not self-contained and requires prior installation, but this is clearly stated in pre-flight checks.
- No OpenAgentSkill engagement data yet
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Recherche-Agents-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
Prüfen82 GitHub-Stars
Star-/Fork-Aktivität
Prüfen82 Stars und 9 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
- Skill relies on external AMQ tool; not self-contained and requires prior installation, but this is clearly stated in pre-flight checks.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 82 GitHub stars
- Stars/forks activity: 82 stars, 9 forks; issue activity unavailable in current metadata
- 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
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
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.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternativen-Shortlist
Vor Installation vergleichen
Similar skills that may fit this task.
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Übersicht
--- name: amq-spec version: 0.66.0 # x-release-please-version description: >- Parallel-research-then-converge design workflow between two agents. Use this skill when the user wants two agents to independently think through a design problem before aligning on a solution — "spec X with codex", "design X together", "both agents think through X", "brainstorm architecture together", "parallel research then joint proposal", "think through separately then align", "careful thought from both sides before coding", or any variation where the user wants collaborative design rather than just splitting implementation work. Also use this when you receive a message labeled workflow:spec and need to know the correct receiver-side protocol. Not for sending simple messages or reviews (use /amq-cli), implementing completed designs, or creating document templates. argument-hint: "<description of what to design> [with <partner>]" metadata: short-description: Multi-agent collaborative spec workflow compatibility: claude-code, codex-cli ---
# /amq-spec — Collaborative Specification Workflow
This skill defines a structured two-agent specification flow.
Use canonical phases in order: `Research -> Discuss -> Draft -> Review -> Present -> Execute`
Detailed step-by-step protocol lives in `references/spec-workflow.md`. This file is the concise operational entrypoint.
## Parse Input
From the user prompt, extract: - **topic**: short kebab-case spec name (e.g., `auth-token-rotation`) - **partner**: partner agent handle (default: `codex`) - **problem**: the full design problem statement
If topic/problem are unclear, ask for clarification.
## Pre-flight
1. Verify AMQ is available: `which amq` 2. Verify the AMQ root is discoverable (`.amqrc`, AMQ env vars, or the default `.agent-mail` layout); otherwise run: `amq coop init` 3. Use thread name: `spec/<topic>`
## First Action: Send problem to partner IMMEDIATELY
The entire point of the spec workflow is parallel research — both agents exploring the problem independently, then comparing notes. Every second you spend researching before sending is a second your partner sits idle waiting for the problem statement. That's why the send comes first, even though your instinct might be to "research first to give better context."
```bash amq send --to <partner> --kind question \ --labels workflow:spec,phase:request \ --thread spec/<topic> --subject "Spec: <topic>" --body "<problem>" ```
Send the user's problem description verbatim — your own analysis goes in the research phase, not the kickoff. If you pre-analyze, you bias the partner's independent research, which defeats the purpose of having two perspectives.
## Label Convention
Labels are how both agents and the receiver-side protocol table know which phase the conversation is in. Use existing AMQ kinds plus labels to express spec workflow semantics:
| Phase | Kind | Labels | |---|---|---| | Problem statement | `question` | `workflow:spec,phase:request` | | Research findings | `brainstorm` | `workflow:spec,phase:research` | | Discussion | `brainstorm` | `workflow:spec,phase:discuss` | | Plan draft | `review_request` | `workflow:spec,phase:draft` | | Plan feedback | `review_response` | `workflow:spec,phase:review` | | Final decision | `decision` | `workflow:spec,phase:decision` | | Progress/ETA | `status` | `workflow:spec` |
## Quick Command Skeleton
```bash # Initiate spec with problem statement amq send --to <partner> --kind question \ --labels workflow:spec,phase:request \ --thread spec/<topic> --subject "Spec: <topic>" --body "<problem>"
# Submit independent research amq send --to <partner> --kind brainstorm \ --labels workflow:spec,phase:research \ --thread spec/<topic> --subject "Research: <topic>" --body "<findings>"
# Discuss and align amq send --to <partner> --kind brainstorm \ --labels workflow:spec,phase:discuss \ --thread spec/<topic> --subject "Discussion: <topic>" --body "<analysis>"
# Draft plan amq send --to <partner> --kind review_request \ --labels workflow:spec,phase:draft \ --thread spec/<topic> --subject "Plan: <topic>" --body "<plan>"
# Review plan amq send --to <partner> --kind review_response \ --labels workflow:spec,phase:review \ --thread spec/<topic> --subject "Review: <topic>" --body "<feedback>"
# Optional final decision message amq send --to <partner> --kind decision \ --labels workflow:spec,phase:decision \ --thread spec/<topic> --subject "Final: <topic>" --body "<final plan>" ```
## When You RECEIVE a Spec Message
If you receive a message labeled `workflow:spec`, your action depends on the phase:
| Label | Your action | |---|---| | `phase:request` | Read the problem statement, do your **own independent research first**, then submit findings as `brainstorm` + `phase:research` | | `phase:research` | **Before reading**: check if you've already submitted your own research on this thread. If not, do your own research and submit it first. This preserves research independence — reading the partner's findings before forming your own view contaminates your perspective. Once your research is submitted, read the thread and start discussion as `brainstorm` + `phase:discuss`. | | `phase:discuss` | Reply with your analysis, continue discussion until aligned | | `phase:draft` | Review the plan and send feedback as `review_response` + `phase:review`. Your job here is review, not implementation — the plan needs to survive scrutiny before anyone builds it. | | `phase:review` | Revise plan if needed, or confirm alignment | | `phase:decision` | Stop. A `phase:decision` message is agent-to-agent alignment, **not** user approval, so do **not** implement from a spec decision alone. Only the human authorizes implementation, recorded as a structural gate to the initialized human handle (conventionally `user`; see the Operator Gates section in /amq-cli). Wait until the initiator confirms the human approved on the gate thread and assigns you work. |
**Why the partner doesn't implement**: The spec workflow is a design process. The initiator owns the relationship with the user and presents the final plan. If the partner implements without approval, the user loses control over what gets built. The agent-to-agent `phase:decision` message is alignment, not authorization: human approval is a structural gate to the initialized human handle, and partner agents must not implement from a spec decision alone. Implementation starts only after the initiator explicitly tells you the human approved and assigns work.
## Protocol Discipline
These rules exist because violations silently break the workflow's value proposition:
- **Send before researching** — parallel research is the whole point. Pre-researching wastes your partner's time and biases the outcome toward your initial framing. - **Submit your own research before reading partner's** — reading first contaminates your independent perspective. Two agents who read the same code and reach the same conclusion is less valuable than two agents who explore independently and then compare notes. - **Don't skip phases** — each phase builds on the previous. Collapsing directly to a finished spec skips the discussion where misunderstandings surface. - **Use `spec/<topic>` threads and the label convention** — this is how both agents (and the tooling) know which phase the conversation is in. Without consistent labels, the receiver-side protocol table above breaks. - **Don't enter plan mode during research** if it blocks tool usage — you need tools to explore the codebase. - **Present the final plan to the user before executing, and raise a structural gate**. The initiator owns the user relationship. After the decision phase, present the plan in chat AND raise a structural human gate using the initialized human handle (conventionally `user`) on a stable `gate/<topic>` thread, then wait for explicit approval on that thread. The agent-to-agent `phase:decision` message is alignment only; partner agents must not implement from it. See the Operator Gates section in /amq-cli for canonical mechanics, seeding, and guardrails.
## Reference
For full protocol details, templates, and phase gates, see: - [references/spec-workflow.md](references/spec-workflow.md)
Technische Details
- Version
- 1.0.0
- Lizenz
- MIT
- Letzte Aktualisierung
- 21. Aug. 2026
- Veröffentlicht
- 21. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 73/100
- 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 amq-spec, bereit für einen manuellen X-Post.
Before you hand an agent source-backed research, give it a repeatable starting point. amq-spec: >- 82 stars https://www.openagentskill.com/skills/avivsinai-amq-spec?ref=x
Optionale Antwort mit Installationsbefehl
Listing + install path for amq-spec: https://www.openagentskill.com/skills/avivsinai-amq-spec?ref=x Install: npx skills add avivsinai/agent-message-queue --skill amq-spec
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- avivsinai
- 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 avivsinai 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/avivsinai-amq-spec)
[](https://www.openagentskill.com/skills/avivsinai-amq-spec)
[](https://www.openagentskill.com/skills/avivsinai-amq-spec/audit)
[](https://www.openagentskill.com/skills/avivsinai-amq-spec)Autor
avivsinai
@avivsinai
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 82
- Qualitätswert
- 36/100
- Letzter GitHub-Push
- 21. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 0
- 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-Akzeptanz82 GitHub-StarsPrüfen
- Star-/Fork-Aktivität82 Stars und 9 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle Wartung1 Tage seit dem letzten PushBestanden
- LizenzklarheitMITBestanden
- README/SKILL.md-VollständigkeitÖffentliche Metadaten benötigen mehr README/SKILL.md-KontextInfo
- Abhängigkeits-/Laufzeitrisikocommand execution surface, credential or environment accessPrüfen
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