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/luohaothu/everything-codex/instinct-statusgit clone --depth 1 https://github.com/Luohaothu/everything-codexWhat 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 | $0.00592 |
| Opus 5 | $0.00008 | $0.00296 |
| Sonnet 5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
instinct-status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
本能状态命令
显示所有已学习的本能及其置信度分数，按领域分组。
实现
使用插件根路径运行本能 CLI：
python3 "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/scripts/instinct-cli.py" status
或者，如果未设置 CLAUDE_PLUGIN_ROOT（手动安装），则使用：
python3 ~/.claude/skills/continuous-learning-v2/scripts/instinct-cli.py status
用法
/instinct-status
/instinct-status --domain code-style
/instinct-status --low-confidence
操作步骤
- 从
~/.claude/homunculus/instincts/personal/读取所有本能文件 - 从
~/.claude/homunculus/instincts/inherited/读取继承的本能 - 按领域分组显示它们，并带有置信度条
输出格式
📊 Instinct Status
==================
## Code Style (4 instincts)
### prefer-functional-style
Trigger: when writing new functions
Action: Use functional patterns over classes
Confidence: ████████░░ 80%
Source: session-observation | Last updated: 2025-01-22
### use-path-aliases
Trigger: when importing modules
Action: Use @/ path aliases instead of relative imports
Confidence: ██████░░░░ 60%
Source: repo-analysis (github.com/acme/webapp)
## Testing (2 instincts)
### test-first-workflow
Trigger: when adding new functionality
Action: Write test first, then implementation
Confidence: █████████░ 90%
Source: session-observation
## Workflow (3 instincts)
### grep-before-edit
Trigger: when modifying code
Action: Search with Grep, confirm with Read, then Edit
Confidence: ███████░░░ 70%
Source: session-observation
---
Total: 9 instincts (4 personal, 5 inherited)
Observer: Running (last analysis: 5 min ago)
标志
--domain <name>：按领域过滤（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>：按来源过滤（session-observation、repo-analysis、inherited）--json：以 JSON 格式输出，供编程使用
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 17fe7921d05d
instinct-status is a command published in the GitHub repository Luohaothu/everything-codex (24 stars, last pushed 21d ago), licensed MIT. It adds 16 tokens to every session and 592 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.