Context Mode Output Sandbox
mksglu/context-mode
Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
Install Mnemos
$ npx skills add alinaqi/maggy --skill mnemos -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alinaqi/maggy mnemos --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mnemos .claude/skills/mnemos && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .claude/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alinaqi/maggy/tree/main/skills/mnemosType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alinaqi/maggy --skill mnemos -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alinaqi/maggy mnemos --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/mnemos .agents/skills/mnemos && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .agents/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alinaqi/maggy --skill mnemos -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alinaqi/maggy mnemos --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/mnemos .cursor/skills/mnemos && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .cursor/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alinaqi/maggy.git --path skills/mnemos--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alinaqi/maggy --skill mnemos -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alinaqi/maggy mnemos --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/mnemos .gemini/skills/mnemos && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .gemini/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alinaqi/maggy mnemosInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alinaqi/maggy --skill mnemos -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/mnemos .github/skills/mnemos && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .github/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alinaqi/maggy --skill mnemos -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alinaqi/maggy mnemos --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/mnemos .opencode/skills/mnemos && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mnemos" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/mnemos into .opencode/skills/mnemos/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mnemos", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mnemos- GitHub stars
- 707
- Token cost
- ~1.8k tokens
- SKILL.md length
- 695 words
- Files
- 1
- Skills in repo
- 71
- Repo updated
- First seen
- Licence
- MIT
At a glance
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
- Works in 7 steps: Statusline writes fatigue.json on every… → PreToolUse hook reads fatigue before… → PreCompact hook writes emergency… → …
- Tasks that involve Context engineering
- SKILL.md covers What It Does, Fatigue Model, How To Use and Claude Transcript Ingestion &…, plus 3 more sections
- Calls git
What it does
Mnemos is an agent skill from alinaqi/maggy. Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Context engineering. The repository describes itself as: What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center. The licence is MIT.
When your agent uses it
- Tasks that involve Context engineering
Example prompts
- “/mnemos”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Statusline writes fatigue.json on every API call
- PreToolUse hook reads fatigue before every edit, auto-checkpoints at 0.60+
- PreCompact hook writes emergency checkpoint, compaction marker, and tells summarizer what to preserve
- SessionStart "compact" fires immediately after compaction, re-injects full checkpoint (primary restore)
- SessionStart "startup|resume" loads last checkpoint on new/resumed sessions
- PreToolUse fallback (no matcher) detects compaction marker if SessionStart didn't fire
- Stop hook writes final checkpoint for next session
What it can do on your machine
Read from SKILL.md and the folder at commit 72a456e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mnemos loads about 1.8k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 695 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from alinaqi/maggy at commit 72a456e, republished under its MIT licence (© alinaqi). 695 words, ~1,776 tokens.
Download SKILL.mdSave it as
.claude/skills/mnemos/SKILL.md (or your agent's skills folder).- name
- mnemos
- description
- Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies
- when-to-use
- When you need durable working memory across compactions — checkpoint decisions, preserve task handoffs, or audit what was remembered
- user-invocable
- false
- effort
- high
Mnemos — Task-Scoped Memory Lifecycle
What It Does
Mnemos prevents lossy context compaction from destroying the structured knowledge you need most. It treats your working memory as a typed graph (MnemoGraph) where different types of knowledge have different eviction policies:
- GoalNodes and ConstraintNodes are NEVER evicted — they survive all compaction
- ResultNodes are compressed (summary kept) before eviction
- ContextNodes are evictable when their activation weight drops
- CheckpointNodes persist to disk for session resume
Fatigue Model
Mnemos monitors 4 dimensions of "agent fatigue" — all passively observed from hook data, no manual input needed:
| Dimension | Weight | Signal Source | What It Measures |
|---|---|---|---|
| Token utilization | 0.40 | Statusline JSON | How full the context window is |
| Scope scatter | 0.25 | PreToolUse file paths | How many directories the agent is bouncing between |
| Re-read ratio | 0.20 | PreToolUse Read calls | How often the agent re-reads files it already read (context loss) |
| Error density | 0.15 | PostToolUse outcomes | What fraction of tool calls are failing (agent struggling) |
Fatigue states and actions:
| State | Score | Action |
|---|---|---|
| FLOW | 0.0–0.4 | Normal operation |
| COMPRESS | 0.4–0.6 | Micro-consolidation runs (compress 3 ResultNodes, evict 1 cold ContextNode) |
| PRE-SLEEP | 0.6–0.75 | Checkpoint written, consolidation runs |
| REM | 0.75–0.9 | Emergency checkpoint, consider wrapping up |
| EMERGENCY | 0.9+ | Checkpoint written, hand off immediately |
How To Use
Automatic (hooks handle everything):
- Statusline writes
fatigue.jsonon every API call - PreToolUse hook reads fatigue before every edit, auto-checkpoints at 0.60+
- PreCompact hook writes emergency checkpoint, compaction marker, and tells summarizer what to preserve
- SessionStart "compact" fires immediately after compaction, re-injects full checkpoint (primary restore)
- SessionStart "startup|resume" loads last checkpoint on new/resumed sessions
- PreToolUse fallback (no matcher) detects compaction marker if SessionStart didn't fire
- Stop hook writes final checkpoint for next session
Post-Compaction Recovery (Three-Layer Defense):
When Claude Code compacts the context (~83% full), Mnemos uses three layers:
- Layer 1 (PreCompact): Outputs strong preservation instructions with inline checkpoint content for the summarizer. Writes
.mnemos/just-compactedmarker. - Layer 2 (SessionStart "compact"): PRIMARY re-injection. Fires immediately when Claude resumes after compaction — before any agent action. Consumes the marker and injects the full checkpoint into the fresh context. This is the recommended approach per the RFC (Wake State Reconstruction).
- Layer 3 (PreToolUse fallback): If SessionStart doesn't fire (older versions, edge cases), the first tool call triggers
mnemos-post-compact-inject.shwhich detects the marker and injects. Safety net only.
The result: after compaction, you'll see a "CONTEXT RESTORED AFTER COMPACTION" block with your goal, constraints, what you were working on, and progress. Resume from there.
Manual CLI:
mnemos init # Initialize .mnemos/
mnemos status # Show node counts + fatigue
mnemos fatigue # Detailed fatigue breakdown
mnemos checkpoint --force # Write checkpoint now
mnemos resume # Output checkpoint for context
mnemos consolidate # Run micro-consolidation
mnemos nodes --type goal # List active GoalNodes
mnemos add goal "Build auth" # Add a GoalNode
mnemos bridge-icpg # Import iCPG ReasonNodes
mnemos ingest-claude --all # Ingest Claude Code transcripts (see below)
mnemos haze --recent 10 # Show per-session haziness scoresShow full SKILL.md (290 more words)Show less
Claude Transcript Ingestion & Haziness
Mnemos can ingest Claude Code session transcripts (the per-session JSONL under
~/.claude/projects/) and score each session's haziness — a measure of how
much the agent struggled. The Stop hook does this automatically on session
exit; it is also available manually.
What's stored: only structural fields (roles, tool names, file paths, error flags, timestamps) plus a redacted, 200-char preview of each turn. Full content is never persisted, and secrets (API keys, tokens, PEM blocks, JWTs, credentials) are redacted before anything touches disk.
Haziness is a weighted score over five dimensions, each in [0,1]:
| Dimension | Weight | What it measures |
|---|---|---|
| correction_density | 0.30 | User corrections per eligible user turn |
| redo_ratio | 0.25 | Edits re-touched after an error |
| first_try_error_rate | 0.20 | Edits followed by errors within 3 turns |
| orphan_tool_use_rate | 0.15 | Tool calls with no matching result |
| backtrack_norm | 0.10 | git revert/reset --hard/restore calls |
The composite maps to a band: clear < 0.25 ≤ cloudy < 0.50 ≤ hazy < 0.75 ≤ lost.
mnemos ingest-claude --all # ingest every transcript + score
mnemos ingest-claude --session <id> # one session by id
mnemos ingest-claude --transcript <f> # a specific JSONL file
mnemos haze --recent 10 # table of recent sessions
mnemos haze --session <id> # per-dimension breakdownIngestion is idempotent (resumes via last_line_offset). Opt out per project
with touch .mnemos/claude-log.disabled.
Agent Instructions
When working on a task:
- Create a GoalNode at the start:
mnemos add goal "what you're trying to achieve" --task-id session-1 - Add ConstraintNodes for invariants:
mnemos add constraint "API backward compatibility" --scope src/api/ - Check fatigue before long operations:
mnemos fatigue - Checkpoint at sub-goal boundaries:
mnemos checkpoint - On session resume: the SessionStart hook automatically loads your checkpoint
iCPG Integration
Mnemos bridges with iCPG (Intent-Augmented Code Property Graph):
mnemos bridge-icpgimports active ReasonNodes as GoalNodes- Postconditions/invariants become ConstraintNodes
- Checkpoint includes iCPG state (active intent, unresolved drift)
Storage
Everything lives in .mnemos/ (gitignored):
mnemo.db— SQLite MnemoGraphfatigue.json— Live token metrics (updated per API call by statusline)signals.jsonl— Behavioral signal log (appended by PreToolUse + PostToolUse hooks)checkpoint-latest.json— Most recent checkpointcheckpoints/— Archived checkpoints
© alinaqi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/mnemos of alinaqi/maggy.
Open the folder on GitHubat commit 72a456e
Compare with similar skills
Mnemos next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mnemos this skillalinaqi/maggy | 707 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Context Mode Output Sandboxmksglu/context-mode | 26k | — | ~4.1k | Automated safety check: Pass | Custom licence | |
| Memori Long-Term MemoryMemoriLabs/Memori | 17k | — | ~2k | Automated safety check: Notes | Custom licence | |
| Picoclaw Skill Creatorsipeed/picoclaw | 30k | — | ~4.4k | Automated safety check: Pass | MIT | |
| ccc Semantic Code Searchcocoindex-io/cocoindex-code | 2.8k | — | ~938 | Automated safety check: Pass | Apache-2.0 | |
| Context Mode for Antigravity CLImksglu/context-mode | 26k | — | ~850 | Automated safety check: Pass | Custom licence |
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Questions about Mnemos
What does Mnemos do?
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies. Mnemos is an agent skill from alinaqi/maggy.
When should I use Mnemos?
Mnemos fits situations like: tasks that involve Context engineering.
How do I install Mnemos in Claude Code?
Run `npx skills add alinaqi/maggy --skill mnemos -a claude-code`. Or copy the skill folder (skills/mnemos in alinaqi/maggy) into .claude/skills/mnemos in your project. Claude Code loads it when a task matches its description.
How do I install Mnemos in Codex?
Run `npx skills add alinaqi/maggy --skill mnemos -a codex`. Or copy the skill folder (skills/mnemos in alinaqi/maggy) into .agents/skills/mnemos in your project. Codex loads it when a task matches its description.
Can I use Mnemos in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alinaqi/maggy --skill mnemos -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mnemos, .gemini/skills/mnemos, .github/skills/mnemos and .opencode/skills/mnemos in your project.
What does Mnemos need to run?
Going by SKILL.md and its folder, Mnemos needs the command-line tools its instructions call (git).
Does Mnemos access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Mnemos safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Mnemos use?
Mnemos is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mnemos use?
About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Mnemos?
Skills that share tags, products or a category with Mnemos: Context Mode Output Sandbox (mksglu/context-mode, 26k stars), Memori Long-Term Memory (MemoriLabs/Memori, 17k stars), Picoclaw Skill Creator (sipeed/picoclaw, 30k stars) and ccc Semantic Code Search (cocoindex-io/cocoindex-code, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mnemos?
alinaqi (a GitHub user) maintains it in alinaqi/maggy, which has 707 GitHub stars. The repository holds 71 skills in this directory. The repository was last updated on September 24, 2026.
Source: alinaqi/maggy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.