amq-spec

REVIEW · 55
Registry indexed

>-

Verified installs0
Stars82
Version1.0.0
Quality65/100 · Promising
Trust55/100 · Do not auto-install
Audit74/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

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

ResearchResearch agentsagent-skill

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

Promising
65

Useful candidate, but compare it with alternatives before adopting.

Trust

Do not auto-install
55

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

Audit

Needs review
74

A 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.

CodexClaude CodeCursorOpenAgentSkill CLI

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.

Open JSON

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLIOpenAI AgentsCLI

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-spec

Do 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

Agent safety v2

34/100 · Avoid automatic install

Blocked for auto-installblock

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.

Resolve via API

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.

skill install

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-spec

Agent 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 text plan

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.

Open install API

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-spec

Registry 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.

Open manifest

Agent fit

64/100

Research agents

Platforms

Claude Code, OpenAI Agents

Audit report

Needs review · 74/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Research agents

Prototype with this skill first; keep a fallback candidate ready.

64
Readiness
Prototype
Stage

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

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 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.

55
OpenAgentSkill Trust Score

GitHub adoption

CHECK

82 GitHub stars

Stars/forks activity

CHECK

82 stars, 9 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

MIT

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.

65
GitHub stars
82
Freshness
Today
Install ready
Yes
License
MIT
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.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

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

64
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

74
Needs review
Security
73/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
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

X

Scenario-led draft for amq-spec, ready for a manual X post.

Curator note
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
Open X draft
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

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
avivsinai
Indexed by
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Owner 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

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Author

A

avivsinai

@avivsinai

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

55
  • 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