deepagent-memory-fold
DeepAgent-style memory folding for VCO sessions: compress long context into structured working/tool memory without using episodic-memory.
Best use case
deepagent-memory-fold is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
DeepAgent-style memory folding for VCO sessions: compress long context into structured working/tool memory without using episodic-memory.
Teams using deepagent-memory-fold should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deepagent-memory-fold/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deepagent-memory-fold Compares
| Feature / Agent | deepagent-memory-fold | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
DeepAgent-style memory folding for VCO sessions: compress long context into structured working/tool memory without using episodic-memory.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# DeepAgent Memory Fold (VCO)
## When to use
Use this skill when:
- The task is long-horizon and context is getting large
- You need to “take a breath” and restart reasoning from a compact state
- You see repeated retries / route instability / losing track of decisions
- You need to hand off to another agent or start a new session
## Governance constraints (must follow)
- VCO memory governance **disables** `episodic-memory`.
- Use **state_store** (session) by default.
- Only write to Serena memory when the user explicitly approves a **project decision**.
## Runtime (Upstream vendoring)
DeepAgent upstream is vendored (optional/advanced):
- `C:\Users\羽裳\.codex\_external\ruc-nlpir\DeepAgent\`
Runtime config + preflight (no secrets stored/printed):
- `C:\Users\羽裳\.codex\skills\vibe\config\ruc-nlpir-runtime.json`
- `pwsh C:\Users\羽裳\.codex\skills\vibe\scripts\ruc-nlpir\preflight.ps1`
## Output contract (structured fold)
Produce a “folded memory” object with these sections:
1. **Working memory**
- Current goal
- Current sub-goal
- Current blockers
- Next 3 actions
2. **Tool memory**
- Tools/skills used
- What worked / what failed
- Availability notes (keys required, deps missing)
3. **Evidence memory**
- Top 5 evidence anchors (file:line or URLs)
4. **Decision log**
- Only decisions actually made (no speculation)
5. **Resume prompt**
- A compact prompt that can be pasted into a new session
## Where to store it
- Default: write to `outputs/runtime/memory-fold.json` (or similar session output)
- If user requests: also write a human-readable `memory-fold.md`
## Minimal template (copy/paste)
```json
{
"working_memory": {
"goal": "",
"sub_goal": "",
"blockers": [],
"next_actions": []
},
"tool_memory": {
"used": [],
"worked": [],
"failed": [],
"availability": []
},
"evidence_memory": {
"anchors": []
},
"decision_log": [],
"resume_prompt": ""
}
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