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/codelably/harmony-claude-code/instinct-exportgit clone --depth 1 https://github.com/codelably/harmony-claude-codeWhat 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 | $0.00022 | $0.00723 |
| Opus 5 | $0.00011 | $0.00362 |
| Sonnet 5 | $0.00004 | $0.00145 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
instinct-export 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.
What it actually says
直觉导出（Instinct Export）命令
将直觉（Instincts）导出为可共享的格式。非常适用于：
- 与团队成员共享
- 迁移到新机器
- 为项目规范（Conventions）贡献内容
用法
/instinct-export # 导出所有个人直觉
/instinct-export --domain testing # 仅导出测试相关的直觉
/instinct-export --min-confidence 0.7 # 仅导出高置信度的直觉
/instinct-export --output team-instincts.yaml
执行流程
- 从
~/.claude/homunculus/instincts/personal/读取直觉 - 根据标志位（Flags）进行过滤
- 脱敏处理（剥离敏感信息）：
- 移除会话 ID（Session IDs）
- 移除文件路径（仅保留模式匹配符 Pattern）
- 移除早于“上周”的时间戳
- 生成导出文件
输出格式
创建一个 YAML 文件：
# Instincts Export
# Generated: 2025-01-22
# Source: personal
# Count: 12 instincts
version: "2.0"
exported_by: "continuous-learning-v2"
export_date: "2025-01-22T10:30:00Z"
instincts:
- id: prefer-functional-style
trigger: "when writing new functions"
action: "Use functional patterns over classes"
confidence: 0.8
domain: code-style
observations: 8
- id: test-first-workflow
trigger: "when adding new functionality"
action: "Write test first, then implementation"
confidence: 0.9
domain: testing
observations: 12
- id: grep-before-edit
trigger: "when modifying code"
action: "Search with Grep, confirm with Read, then Edit"
confidence: 0.7
domain: workflow
observations: 6
隐私考量
导出内容包括：
- ✅ 触发模式（Trigger patterns）
- ✅ 动作（Actions）
- ✅ 置信度分数（Confidence scores）
- ✅ 域（Domains）
- ✅ 观察次数（Observation counts）
导出内容 不包括：
- ❌ 实际代码片段
- ❌ 文件路径
- ❌ 会话转录文本
- ❌ 个人身份标识符
标志位（Flags）
--domain <name>: 仅导出指定的域（Domain）--min-confidence <n>: 最低置信度阈值（默认值：0.3）--output <file>: 输出文件路径（默认值：instincts-export-YYYYMMDD.yaml）--format <yaml|json|md>: 输出格式（默认值：yaml）--include-evidence: 包含证据文本（默认值：排除）
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 · 92 lines · 22 tokens per session scan A 4534cb21f72c
instinct-export is a command published in the GitHub repository codelably/harmony-claude-code (42 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 723 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.