amq-spec
>-
Profil aset
Riset dan pekerjaan pengetahuan
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Skenario
Agent riset
I need my agent to research a topic, compare sources, and produce a concise report.
Kecocokan Agent
Claude Code + OpenAI Agents + CLI
Cocok untuk Codex, Claude Code, Cursor, CLI, atau Agent khusus.
Pasang
Siap
npx skills add avivsinai/agent-message-queue --skill amq-spec
Pemeliharaan
Terkini
1 hari sejak push
Risiko
Perlu ditinjau
Dependency or permission surface needs review
Kualitas GitHub
82
65/100 Kualitas · 63/100 Kepercayaan
Tag cakupan
Catatan ulasan
Dependency or permission surface needs review · Permission surface may require sandboxing
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
82 star GitHub
Aktivitas repositori
82 star dan 9 fork
Pemeliharaan
1 hari sejak push
Lisensi
MIT
Pasang
npx skills add avivsinai/agent-message-queue --skill amq-spec
Keamanan pemasangan
Jalur pemasangan paket atau runtime standar
Cakupan izin
secrets or environment access, shell or command execution
Hasil Agent
Belum ada data hasil Agent
Dokumentasi
Usable metadata, review docs
Ringkasan risiko
Tinjau sebelum produksi
- 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
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 Agent riset
- Tim Claude Code
- builders willing to evaluate younger projects
- Sumber pencarian
Agent yang sesuai
Keputusan pemasangan
- Perintah
- npx skills add avivsinai/agent-message-queue --skill amq-spec
- Kebijakan
- Blokir
- Tinjauan manusia
- Ya
Kepercayaan dan risiko
- Kepercayaan
- 55/100
- Audit
- 74/100
- Tingkat risiko
- Perlu ditinjau
Lingkar hasil
- Endpoint
- /api/agent/outcome
- ID event
- resolve
- Hasil
- 5
Perintah pemasangan
npx skills add avivsinai/agent-message-queue --skill amq-specJangan gunakan ketika
- Tim yang membutuhkan SLA dengan dukungan vendor
- 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
- Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
Skill alternatif
Last30days Skill
53.5K Star
npx skills add mvanhorn/last30days-skill -g
Skill alternatif
Academic Research Skills
38.4K Star
npx skills add Imbad0202/academic-research-skills
Skill alternatif
GPT Researcher
28.0K Star
npx skills add assafelovic/gpt-researcher
Skill alternatif
DeepResearch
19.8K Star
npx skills add Alibaba-NLP/DeepResearch
Keamanan Agent v2
34/100 · Hindari pemasangan otomatis
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.
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.
Tinggi
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- Petunjuk izin berisiko tinggi: Shell or command execution, Secrets or environment access
- Dependency or permission surface needs review
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 avivsinai-amq-specRencana 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%20amq-spec%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Teks Resolve
/api/agent/resolve?task=Use%20amq-spec%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Serah-terima pemasangan
/api/skills/avivsinai-amq-spec/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 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.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/avivsinai-amq-spec/install
Format teks LLM
/api/skills/avivsinai-amq-spec/install?format=text
Cari alternatif
/api/skills/search?q=amq-spec&limit=3
Prompt Agent
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-specMetadata 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/avivsinai-amq-spec
Teks LLM
/api/registry/manifest/avivsinai-amq-spec?format=text
Alias pemasangan
/api/registry/install/avivsinai-amq-spec
Rekomendasikan
/api/registry/recommend?task=Use%20amq-spec%20in%20an%20agent%20workflow&limit=3
Kecocokan Agent
Agent riset
Tag use case
Platform
Claude Code, OpenAI Agents
Laporan audit
Perlu ditinjau · 74/100
Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.
Panel keputusan Agent
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Peran di stack
Kandidat cadangan
Kecocokan utama
Agent riset
Label kepercayaan
Buat prototipe dulu
Jalur pemasangan
Perintah siap
Gunakan saat
- Alur kerja Agent riset
- Tim Claude Code
- builders willing to evaluate younger projects
Bukti
- recent repository activity
- install command or GitHub repo available
- profil kualitas 65/100
tinjau dulu
- 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
Jalur implementasi
- 1Pasang di Agent sandbox dan jalankan satu tugas Agent riset 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
Periksa82 star GitHub
Aktivitas star/fork
Periksa82 star dan 9 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
- 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
- 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
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.
Kecocokan alur kerja
Tambahkan ke alur kerja lengkap
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.
Daftar alternatif
Bandingkan sebelum memasang
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
Ringkasan
--- 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)
Detail teknis
- Versi
- 1.0.0
- Lisensi
- MIT
- Pembaruan terakhir
- 21 Agu 2026
- Diterbitkan
- 21 Agu 2026
Ringkasan keputusan
Kandidat cadangan
recent repository activity
Audit
Tinjauan pemasangan
Tinjauan pemasangan dan adopsi
- Keamanan
- 73/100
- 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 amq-spec, siap untuk posting manual di X.
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
Balasan opsional dengan perintah pemasangan
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
Sumber listing
Diindeks Registry
Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.
- Kreator
- avivsinai
- 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 avivsinai, 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/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)Penulis
avivsinai
@avivsinai
Tag
Kecocokan platform
Sinyal kesehatan
- Star GitHub
- 82
- Skor kualitas
- 36/100
- Push GitHub terakhir
- 21 Agu 2026
- Petunjuk framework
- Tidak diketahui
- Tampilan OpenAgentSkill
- 0
- 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 GitHub82 star GitHubPeriksa
- Aktivitas star/fork82 star dan 9 fork; aktivitas issue tidak tersedia dalam metadata saat iniPeriksa
- Pemeliharaan terbaru1 hari sejak pushLulus
- Kejelasan lisensiMITLulus
- Kelengkapan README/SKILL.mdMetadata publik memerlukan konteks README/SKILL.md yang lebih kuatInfo
- Risiko dependensi/runtimecommand execution surface, credential or environment accessPeriksa
Skill terkait
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.
53.5K StarAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarGPT Researcher
Run autonomous deep research over web and local sources
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Tongyi Deep Research, the Leading Open-source Deep Research Agent
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