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mnemos-codex

AI Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@tomevault-io · no license declared
Token usage
Lean
Setup complexity
Plug-and-play
External API key
Not required
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview mnemos-codex.preview 
---
name: mnemos-codex
description: Use when Codex users or Codex agents need to install, configure, validate, troubleshoot, or oper…
category: ai
runtime: no special runtime
---

# mnemos-codex output preview

## PART A: Task fit
- Use case: Use when Codex users or Codex agents need to install, configure, validate, troubleshoot, or operate Mnemos through MCP, or when they mention Codex memory, AGENTS.md memory policy, Codex Automations, or Mnemos in Codex..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Default path / Claim discipline / Daily loop” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Use when Codex users or Codex agents need to install, configure, validate, troubleshoot, or operate Mnemos through MCP, or when they mention Codex memory, AGENTS.md memory policy, Codex Automations, or Mnemos in Codex.”.
- **02** When the source has headings, the agent prioritizes “Default path / Claim discipline / Daily loop” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Use when Codex users or Codex agents need to install, configure, validate, troubleshoot, or operate Mnemos through MCP, or when…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Default path”, “Claim discipline”, “Daily loop”, “Recall mode”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name mnemos-codex directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Default path / Claim discipline / Daily loop” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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