amq-spec
>-
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add avivsinai/agent-message-queue --skill amq-spec
Maintenance
fresh
Pushed today
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
82
65/100 Quality · 63/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
82 GitHub stars
Repo activity
82 stars, 9 forks
Maintenance
Pushed today
License
MIT
Install
npx skills add avivsinai/agent-message-queue --skill amq-spec
Install safety
standard package or runtime install path
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Review before production
- 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
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add avivsinai/agent-message-queue --skill amq-spec
- Policy
- block
- Human review
- yes
Trust and risk
- Trust
- 55/100
- Audit
- 74/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add avivsinai/agent-message-queue --skill amq-specDo not use when
- teams that need a vendor-supported SLA
- 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
- High-risk permission hints: 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 safety v2
34/100 · Avoid automatic install
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.
high
Shell or command execution
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- High-risk permission hints: Shell or command execution, Secrets or environment access
- Dependency or permission surface needs review
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
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 resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/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
Install handoff
/api/skills/avivsinai-amq-spec/install
Agent should check
- 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.
Copy 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.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/avivsinai-amq-spec/install
LLM text format
/api/skills/avivsinai-amq-spec/install?format=text
Find alternatives
/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 metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/avivsinai-amq-spec
LLM text
/api/registry/manifest/avivsinai-amq-spec?format=text
Install alias
/api/registry/install/avivsinai-amq-spec
Recommend
/api/registry/recommend?task=Use%20amq-spec%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
Needs review · 74/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 65/100 quality profile
review first
- 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
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 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.
Trust profile
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
CHECK82 GitHub stars
Stars/forks activity
CHECK82 stars, 9 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSMIT
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- 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
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
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 fit
Add it to a complete workflow
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.
Alternative shortlist
Compare before you install
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
Overview
--- 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)
Technical details
- Version
- 1.0.0
- License
- MIT
- Last updated
- Aug 21, 2026
- Published
- Aug 21, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 73/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for amq-spec, ready for a manual 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
Optional reply with install command
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
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- avivsinai
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to avivsinai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](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)Author
avivsinai
@avivsinai
Tags
Platform fit
Health signals
- GitHub stars
- 82
- Quality score
- 36/100
- Last GitHub push
- Aug 21, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Do not auto-install
- GitHub adoption82 GitHub starsCHECK
- Stars/forks activity82 stars, 9 forks; issue activity unavailable in current metadataCHECK
- Recent maintenancePushed todayPASS
- License clarityMITPASS
- README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
- Dependency/runtime riskcommand execution surface, credential or environment accessCHECK
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