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-statusgit 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 | $0.00016 | $0.00729 |
| Opus 5 | $0.00008 | $0.00365 |
| Sonnet 5 | $0.00003 | $0.00146 |
| Haiku 4.5 | $0.00002 | $0.00073 |
Grade A, and why
instinct-status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
本能状态查询命令（Instinct Status Command）
显示所有已学习的本能（Instincts）及其置信度分数，并按领域（Domain）进行分组。
实现方式
使用插件根路径运行本能 CLI：
python3 "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/scripts/instinct-cli.py" status
如果未设置 CLAUDE_PLUGIN_ROOT（手动安装），请使用：
python3 ~/.claude/skills/continuous-learning-v2/scripts/instinct-cli.py status
用法
/instinct-status
/instinct-status --domain code-style
/instinct-status --low-confidence
执行逻辑
- 从
~/.claude/homunculus/instincts/personal/读取所有个人本能文件。 - 从
~/.claude/homunculus/instincts/inherited/读取继承的本能。 - 按领域分组显示，并附带置信度进度条。
输出格式
📊 本能状态 (Instinct Status)
==================
## 代码风格 (Code Style) (4 条本能)
### prefer-functional-style
触发器 (Trigger)：编写新函数时
动作 (Action)：优先使用函数式模式而非类
置信度 (Confidence)：████████░░ 80%
来源 (Source)：会话观察 (session-observation) | 最近更新：2025-01-22
### use-path-aliases
触发器 (Trigger)：导入模块时
动作 (Action)：使用 @/ 路径别名而非相对导入
置信度 (Confidence)：██████░░░░ 60%
来源 (Source)：仓库分析 (repo-analysis) (github.com/acme/webapp)
## 测试 (Testing) (2 条本能)
### test-first-workflow
触发器 (Trigger)：添加新功能时
动作 (Action)：先写测试，再写实现
置信度 (Confidence)：█████████░ 90%
来源 (Source)：会话观察 (session-observation)
## 工作流 (Workflow) (3 条本能)
### grep-before-edit
触发器 (Trigger)：修改代码时
动作 (Action)：先用 Grep 搜索，再用 Read 确认，最后 Edit 编辑
置信度 (Confidence)：███████░░░ 70%
来源 (Source)：会话观察 (session-observation)
---
总计：9 条本能 (4 条个人, 5 条继承)
观察器 (Observer)：运行中 (最近分析：5 分钟前)
参数 (Flags)
--domain <name>: 按领域筛选（如 code-style, testing, git 等）--low-confidence: 仅显示置信度 < 0.5 的本能--high-confidence: 仅显示置信度 >= 0.7 的本能--source <type>: 按来源筛选（session-observation, repo-analysis, inherited）--json: 以 JSON 格式输出，便于程序化调用
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 · 87 lines · 16 tokens per session scan A 0073ed673dd4
instinct-status is a command published in the GitHub repository codelably/harmony-claude-code (42 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 729 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.