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 skills/chachamaru127/claude-code-harness/memorynpx skills add Chachamaru127/claude-code-harness --skill memorygit clone --depth 1 https://github.com/Chachamaru127/claude-code-harnessWhat 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 | $0.00086 | $0.00999 |
| Opus 5 | $0.00043 | $0.00500 |
| Sonnet 5 | $0.00017 | $0.00200 |
| Haiku 4.5 | $0.00009 | $0.00100 |
Grade A, and why
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 3d 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.
What it actually says
Memory Skills
メモリとSSOT管理を担当するスキル群です。
機能詳細
| 機能 | 詳細 |
|---|---|
| SSOT初期化 | See references/ssot-initialization.md |
| Plans.mdマージ | See references/plans-merging.md |
| 移行処理 | See references/workflow-migration.md |
| プロジェクト仕様同期 | See references/sync-project-specs.md |
| メモリ→SSOT昇格 | See references/sync-ssot-from-memory.md |
Unified Harness Memory（共通DB）
Claude Code / Codex / OpenCode 共通の記録・検索は harness_mem_* MCP を優先する。
- 検索:
harness_mem_search,harness_mem_timeline,harness_mem_get_observations - 注入:
harness_mem_resume_pack - 記録:
harness_mem_record_checkpoint,harness_mem_finalize_session,harness_mem_record_event
Claude Code 自動メモリとの関係（D22）
Harness の SSOT メモリ（Layer 2）は Claude Code の自動メモリ（Layer 1）と共存します。
自動メモリは汎用的な学習を暗黙的に記録し、SSOT はプロジェクト固有の意思決定を明示的に管理します。
Layer 1 の知見がプロジェクト全体に重要な場合、/memory ssot で Layer 2 に昇格してください。
実行手順
- ユーザーのリクエストを分類
- 上記の「機能詳細」から適切な参照ファイルを読む
- その内容に従って実行
SSOT昇格
メモリシステム（Claude-mem / Serena）から重要な学びをSSOTに永続化します。
- "Save what we learned" → references/sync-ssot-from-memory.md
- "Promote decisions to SSOT" → references/sync-ssot-from-memory.md
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 54 lines · 86 tokens per session scan A 81f0924d1039
memory is a skill published in the GitHub repository Chachamaru127/claude-code-harness (3,079 stars, last pushed 2d ago), licensed MIT. It adds 86 tokens to every session and 999 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…