Maintains project context across sessions via .cm/CONTINUITY.md: reads at start, updates at end. Tracks mistakes, learnings, goals, actions, decisions to avoid repeating errors.
From cmnpx claudepluginhub tody-agent/codymaster --plugin cmThis skill uses the workspace's default tool permissions.
DESIGN_STANDARD_TEMPLATE.mdFEATURES-v4.mdREADME-ru.mdREADME-vi.mdREADME-zh.mdREADME.mdcli/README.mdcli/pyproject.tomlcli/requirements.txtcli/templates/base/skill-core.mdcli/templates/platforms/claude.yamlcli/templates/platforms/cursor.yamlcli/templates/platforms/figma.yamlcli/templates/platforms/vscode-mcp.yamlcli/templates/platforms/windsurf.yamlcli/uxmaster/__init__.pycli/uxmaster/__main__.pycli/uxmaster/cli.pycli/uxmaster/commands/__init__.pycli/uxmaster/commands/extract.pyEnables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Context persistence across sessions. Mistakes captured. Learnings applied. Inspired by Loki Mode's CONTINUITY.md protocol (Autonomi).
ALWAYS — This is a background protocol, not an explicit invocation.
.cm/CONTINUITY.md to orient yourself.cm/CONTINUITY.md with progress# Initialize working memory for current project
cm continuity init
# Check current state
cm continuity status
# View captured learnings
cm continuity learnings
1. Read .cm/CONTINUITY.md to understand current state
2. Read "Mistakes & Learnings" to avoid past errors
3. Check "Next Actions" to determine what to do
4. Reference Active Goal throughout your work
PRE-ACT ATTENTION CHECK (before every significant action):
- Re-read Active Goal
- Ask: "Does my planned action serve this goal?"
- Ask: "Am I solving the original problem, not a tangent?"
- If DRIFT detected → log it → return to goal
1. Update "Just Completed" with accomplishments
2. Update "Next Actions" with remaining work
3. Record any new "Mistakes & Learnings"
4. Record any "Key Decisions" made
5. Update "Files Modified" list
6. Set currentPhase and timestamp
ON_ERROR:
1. Capture error details (stack trace, context)
2. Analyze root cause (not just symptoms)
3. Write learning to CONTINUITY.md "Mistakes & Learnings"
4. Update approach based on learning
5. Retry with corrected approach
6. Max 3 retries per error pattern before ESCALATE
# CodyMaster Working Memory
Last Updated: [ISO timestamp]
Current Phase: [planning|executing|testing|deploying|reviewing]
Current Iteration: [number]
Project: [project name]
## Active Goal
[What we're currently trying to accomplish — 1-2 sentences max]
## Current Task
- ID: [task-id from dashboard]
- Title: [task title]
- Status: [in-progress|blocked|reviewing]
- Skill: [cm-skill being used]
- Started: [timestamp]
## Just Completed
- [Most recent accomplishment with file:line references]
- [Previous accomplishment]
- [etc — last 5 items]
## Next Actions (Priority Order)
1. [Immediate next step]
2. [Following step]
3. [etc]
## Active Blockers
- [Any current blockers or waiting items]
## Key Decisions This Session
- [Decision]: [Rationale] — [timestamp]
## Mistakes & Learnings
### Pattern: Error → Learning → Prevention
- **What Failed:** [Specific error that occurred]
- **Why It Failed:** [Root cause analysis]
- **How to Prevent:** [Concrete action to avoid this in future]
- **Timestamp:** [When learned]
- **Agent:** [Which agent]
- **Task:** [Which task ID]
## Working Context
[Critical information for current work — API keys paths,
architecture decisions, patterns being followed]
## Files Currently Being Modified
- [file path]: [what we're changing]
The memory systems complement each other:
1. CONTINUITY.md = Working memory (current session state)
2. learnings.json = Extracted error patterns (persists across sessions)
3. decisions.json = Architecture decisions (persists across projects)
4. tasks.md/cm-tasks.json = Task queue + RARV logs (Mode D integration)
CONTINUITY.md is the PRIMARY source of truth for "what am I doing right now?"
| Skill | How it integrates |
|---|---|
cm-execution | RARV Mode D reads CONTINUITY.md in REASON phase |
cm-planning | Sets Active Goal and Next Actions |
cm-debugging | Records errors in Mistakes & Learnings |
cm-quality-gate | VERIFY phase updates CONTINUITY.md |
cm-code-review | Records review feedback as learnings |
✅ DO:
- Read CONTINUITY.md at session start (ALWAYS)
- Update CONTINUITY.md at session end (ALWAYS)
- Record EVERY error in Mistakes & Learnings
- Keep "Just Completed" to last 5 items
- Be specific: "Fixed auth bug in login.ts:42" not "Fixed stuff"
❌ DON'T:
- Skip reading CONTINUITY.md ("I remember what I was doing")
- Write vague learnings: "It didn't work" → WHY didn't it work?
- Ignore past learnings when they're relevant
- Let CONTINUITY.md grow beyond ~500 words (rotate old entries)
- Delete Mistakes & Learnings (archive to learnings.json instead)
Your memory is your superpower. Without it, you repeat every mistake forever.