mnemos-codex

AI Community

Repo stars 0
Author repo skills-registry

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; 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.

Mnemos for Codex

Use this skill to put Mnemos on the supported Codex path without overstating host automation. In Codex, the practical Mnemos workflow is:

recall at the start of substantial work
curate durable facts during or after the task
consolidate before finishing

Default path

Prefer pip install "mnemos-memory[mcp]" and mnemos ui.
Treat the blessed Codex setup as two parts: MCP config plus a repo-level AGENTS.md memory block.
Read references/install.md for install and validation.
Read references/operations.md for daily use, troubleshooting, and honest capability framing.

Claim discipline

Safe to claim: Mnemos works in Codex through MCP, shared MNEMOS_CONFIG_PATH, repo-level AGENTS.md, and optional maintenance Automations.
Do not claim built-in prompt/tool lifecycle hooks or Claude Code parity.
Hard auto-capture in Codex is host-dependent and not shipped by Mnemos today.

Daily loop

Start in recall mode: call mnemos_retrieve with a task-focused query and repo-scoped arguments.
Do the work, then switch to curator mode: store only durable facts with mnemos_store.
Use mnemos_inspect before storing a correction or when a retrieved memory looks suspicious.
Finish substantial work with mnemos_consolidate.
If Mnemos MCP tools are unavailable, continue normally instead of blocking work.

Recall mode

Use at the start of coding, debugging, review, or handoff tasks.
Query for architecture, current repo conventions, recent fixes, environment quirks, and user preferences that matter for this task.
In Codex, prefer current_scope=project, scope_id=<workspace or repo name>, and allowed_scopes=project,global.

Curator mode

Use during or near the end of a substantial task.
Store decisions, constraints, environment facts, recurring bug patterns, and stable preferences.
Skip one-off chatter, ephemeral plans, stack traces without reusable lessons, and secrets.

Avoid

Do not present Codex Automations as session capture.
Do not tell users to type memories manually as the primary workflow.
Do not market Codex as Tier 1 until real daily-use validation is complete.

Source: anthony-maio/mnemos — distributed by TomeVault.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @tomevault-io · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Detected network behavior: Local-only
Install commands: None (reference only)

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

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Mnemos for Codex

Use this skill to put Mnemos on the supported Codex path without overstating host automation. In Codex, the practical Mnemos workflow is:

- recall at the start of substantial work
- curate durable facts during or after the task
- consolidate before finishing

## Default path

- Prefer `pip install "mnemos-memory[mcp]"` and `mnemos ui`.
- Treat the blessed Codex setup as two parts: MCP config plus a repo-level `AGENTS.md` memory block.
- Read `references/install.md` for install and validation.
- Read `references/operations.md` for daily use, troubleshooting, and honest capability framing.

## Claim discipline

- Safe to claim: Mnemos works in Codex through MCP, shared `MNEMOS_CONFIG_PATH`, repo-level `AGENTS.md`, and optional maintenance Automations.
- Do not claim built-in prompt/tool lifecycle hooks or Claude Code parity.
- Hard auto-capture in Codex is host-dependent and not shipped by Mnemos today.

## Daily loop

1. Start in recall mode: call `mnemos_retrieve` with a task-focused query and repo-scoped arguments.
2. Do the work, then switch to curator mode: store only durable facts with `mnemos_store`.
3. Use `mnemos_inspect` before storing a correction or when a retrieved memory looks suspicious.
4. Finish substantial work with `mnemos_consolidate`.
5. If Mnemos MCP tools are unavailable, continue normally instead of blocking work.

## Recall mode

- Use at the start of coding, debugging, review, or handoff tasks.
- Query for architecture, current repo conventions, recent fixes, environment quirks, and user preferences that matter for this task.
- In Codex, prefer `current_scope=project`, `scope_id=<workspace or repo name>`, and `allowed_scopes=project,global`.

## Curator mode

- Use during or near the end of a substantial task.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Default path → Claim discipline → Daily loop → Recall mode → Curator mode → Avoid
terms    -> Use this skill to put Mnemos on the supported Codex path without overstating host automation. · - Prefer pip install "mnemos-memory[mcp]" and mnemos ui. · - Safe to claim: Mnemos works in Codex through MCP, shared MNEMOSCONFIGPATH, repo-level AGENTS.md, and optional maintenance Automations. · 1. Start in recall mode: call mnemosretrieve with a task-focused query and repo-scoped arguments. · - Use at the start of coding, debugging, review, or handoff tasks. · - Use during or near the end of a substantial task. · - Do not present Codex Automations as session capture. · --- > Source: [anthony-maio/mnemos](https://github.com/anthony-maio/mnemos) — distributed by [TomeVault](https://tomevault.io).
files/cmd -> pip install "mnemos-memory[mcp]" · mnemos ui · AGENTS.md · references/install.md · references/operations.md · MNEMOSCONFIGPATH · mnemosretrieve · mnemosstore
body sha256 -> b24ecd650de6