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/zhmxiaowo/opencode-simple/instinct-exportgit clone --depth 1 https://github.com/zhmxiaowo/opencode-simpleWhat 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 | $0.00014 | $0.00397 |
| Opus 5 | $0.00007 | $0.00198 |
| Sonnet 5 | $0.00003 | $0.00079 |
| Haiku 4.5 | $0.00001 | $0.00040 |
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 yesterday.
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
95% identical to instinct-export — 4 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
Instinct Export Command
Exports instincts to a shareable format. Perfect for:
- Sharing with teammates
- Transferring to a new machine
- Contributing to project conventions
Usage
/instinct-export # Export all personal instincts
/instinct-export --domain testing # Export only testing instincts
/instinct-export --min-confidence 0.7 # Only export high-confidence instincts
/instinct-export --output team-instincts.yaml
/instinct-export --scope project --output project-instincts.yaml
What to Do
- Detect current project context
- Load instincts by selected scope:
project: current project onlyglobal: global onlyall: project + global merged (default)
- Apply filters (
--domain,--min-confidence) - Write YAML-style export to file (or stdout if no output path provided)
Output Format
Creates a YAML file:
# Instincts Export
# Generated: 2025-01-22
# Source: personal
# Count: 12 instincts
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.8
domain: code-style
source: session-observation
scope: project
project_id: a1b2c3d4e5f6
project_name: my-app
---
# Prefer Functional Style
## Action
Use functional patterns over classes.
Flags
--domain <name>: Export only specified domain--min-confidence <n>: Minimum confidence threshold--output <file>: Output file path (prints to stdout when omitted)--scope <project|global|all>: Export scope (default:all)
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.
- yesterday First seen · 65 lines · 14 tokens per session scan A 6153ef42431f
instinct-export is a command published in the GitHub repository zhmxiaowo/opencode-simple (2 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 397 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to instinct-export, differing in 4 lines, and is treated as a copy.
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.