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.
npx agentmods add commands/shyftlabs/continuum/session-memorygit clone --depth 1 https://github.com/shyftlabs/continuumWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00425 |
| Opus 5 | $0.00000 | $0.00212 |
| Sonnet 5 | $0.00000 | $0.00085 |
| Haiku 4.5 | $0.00000 | $0.00042 |
Grade A, and why
session-memory 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 2d ago.
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.
This is a copy
100% identical to session-memory — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Cross-Session Memory
Purpose
Maintain context and learnings across Claude Code sessions for continuous improvement.
Memory Features
1. Automatic State Persistence
At session end, automatically saves:
- Active agents and specializations
- Task history and patterns
- Performance metrics
- Neural network weights
- Knowledge base updates
2. Session Restoration
// Using MCP tools for memory operations
mcp__claude-flow__memory_usage({
"action": "retrieve",
"key": "session-state",
"namespace": "sessions"
})
// Restore swarm state
mcp__claude-flow__context_restore({
"snapshotId": "sess-123"
})
Fallback with npx:
npx claude-flow hook session-restore --session-id "sess-123"
3. Memory Types
Project Memory:
- File relationships
- Common edit patterns
- Testing approaches
- Build configurations
Agent Memory:
- Specialization levels
- Task success rates
- Optimization strategies
- Error patterns
Performance Memory:
- Bottleneck history
- Optimization results
- Token usage patterns
- Efficiency trends
4. Privacy & Control
// List memory contents
mcp__claude-flow__memory_usage({
"action": "list",
"namespace": "sessions"
})
// Delete specific memory
mcp__claude-flow__memory_usage({
"action": "delete",
"key": "session-123",
"namespace": "sessions"
})
// Backup memory
mcp__claude-flow__memory_backup({
"path": "./backups/memory-backup.json"
})
Manual control:
# View stored memory
ls .claude-flow/memory/
# Disable memory
export CLAUDE_FLOW_MEMORY_PERSIST=false
Benefits
- 🧠 Contextual awareness
- 📈 Cumulative learning
- ⚡ Faster task completion
- 🎯 Personalized optimization
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.
- 2d ago First seen · 90 lines · 0 tokens per session scan A 2c174e08416e
session-memory is a command published in the GitHub repository shyftlabs/continuum (84 stars, last pushed 11d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 425 tokens. A static security scan graded it A with 0 findings. It is 100% identical to session-memory, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
port-commits
把已落地、可追溯的一组 commit，用 cherry-pick（最稳妥） 同步到目标分支（常见：main → hc-0730）。.
auto-goal
goal 래퍼 — /goal 생성, 상태 확인, 완료/blocked handoff를 goal tool 또는 slash command로 연결합니다.
auto-plan
SPEC 작성 — 코드베이스 분석 후 EARS 요구사항, 구현 계획, 인수 기준을 생성합니다.
auto-verify
프론트엔드 UX 검증 — Playwright 기반 비주얼 검증을 실행합니다.
doctor
Diagnose installation health. Check Node, CDP bridge, rn-fast-runner (iOS), rn-android-runner (Android), maestro-runner, simulators, Metro, CDP, injected helpers, ffmpeg, physical devices, plugin version, Vercel rules sync. Reports what's missing — does NOT modify your project.
market-radar
Proactive external competitive / market intelligence scan. Routes to market-radar agent. Default = periodic sweep; focused = topic / market / competitor deep-dive.