cmi-continue

A read-only workflow for resuming unfinished project work from saved session handoff notes and a fresh repository check. CMI is the system used to track that work.

In plain words
What is it for?
Continuing paused work, reviewing open findings, and identifying unfinished tracked changes.
Why use it?
It shows what remains open without changing the project or relying only on memory from an earlier session.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/lenhonbp/codex-memory-intelligence/cmi-continue
Any agent
npx skills add lenhonbp/codex-memory-intelligence --skill cmi-continue
Clone the repo
git clone --depth 1 https://github.com/lenhonbp/codex-memory-intelligence

Made for: Claude Code, Codex.

Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,188 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00118 $0.04188
Opus 5 $0.00059 $0.02094
Sonnet 5 $0.00024 $0.00838
Haiku 4.5 $0.00012 $0.00419

Measured yesterday against content hash ce97f6d0a429, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cmi-continue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/cmi-continue/SKILL.md · 420 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 420 lines · 118 tokens per session scan A ce97f6d0a429

Subscribe to this mod's changes

cmi-continue is a skill published in the GitHub repository lenhonbp/codex-memory-intelligence (1 stars, last pushed 2d ago), with no licence file. It adds 118 tokens to every session and 4,188 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

Sverklo Code Intelligence

Gives you deep codebase understanding — semantic search, blast-radius analysis, PR review, and health audits.

sverklo/sverklo · 27 tokens

ragcode-agents

Orchestrate multi-agent workflows through RagCode. Turn a natural-language collaboration pattern (e.g. "Gemini plans, Claude breaks down, Codex implements, Claude reviews") into a validated DAG workflow spec, then run it with the RagCode agent workflow engine. Trigger when the user asks to coordinate multiple AI…

MarshallEriksen-Neura/ragcode · 96 tokens

ragcode-memory

Manage shared cross-AI memories for this project through RagCode MCP tools (memorywrite, memoryquery, memorylist, memorydelete), with ragcode memory CLI fallback when MCP is unavailable. Read prior decisions, user preferences, and feedback written by other AI agents, and record new insights so they persist across…

MarshallEriksen-Neura/ragcode · 101 tokens

memtrace-first

Route code discovery, debugging, flow tracing, how-code-works questions, and pre-edit rationale checks in indexed source-code repos to Memtrace graph plus Cortex decision tools. Use first before searching/reading code, and before editing, refactoring, deleting, or re-picking an approach that may have a recorded…

syncable-dev/memtrace-public · 115 tokens

memtrace-decision-memory

Use Cortex decision memory through the normal Memtrace MCP tools. Trigger for free-text questions about what was decided, chosen, rejected, banned, or established as a convention; for why a symbol exists or which contracts constrain it; for whether a known decision held, drifted, or was violated; and for the…

syncable-dev/memtrace-public · 137 tokens

memtrace-fleet-first

Coordinate fleets of coding agents sharing one repo+branch: publish typed intents, classify edit episodes, and resolve conflicts before they collide. Use FIRST when multiple agents work the same repo+branch, before reading/planning/editing, when joining a fleet or handing work off, and when the user says two agents…

syncable-dev/memtrace-public · 144 tokens