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/ikuaios/ikuai-cli/outputgit clone --depth 1 https://github.com/ikuaios/ikuai-cliWhat 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 | $0.00000 | $0.00386 |
| Opus 5 | $0.00000 | $0.00193 |
| Sonnet 5 | $0.00000 | $0.00077 |
| Haiku 4.5 | $0.00000 | $0.00039 |
Grade A, and why
output 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.
This is a copy
100% identical to output — 0 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
Output Modes
ikuai-cli supports four output modes.
Example fixtures in docs/examples/ are sanitized static samples used by the docs.
Table (default)
Default output is a human-readable table for terminal readability.
Example:
ikuai-cli auth status
JSON
Pass --format json to emit compact single-line JSON for scripts and agents.
Example:
ikuai-cli auth status --format json
Representative fixtures:
docs/examples/auth-status.compact.jsondocs/examples/system-set-response.compact.jsondocs/examples/users-accounts-list.compact.jsondocs/examples/version.compact.json
YAML
Pass --format yaml to emit YAML output, useful for configuration files and token-efficient agent consumption.
Example:
ikuai-cli auth status --format yaml
Raw
Pass --raw to emit the full API envelope including metadata, pagination info, and status codes. Useful for debugging.
Example:
ikuai-cli auth status --raw
Response Shapes
Different command types produce different JSON shapes:
- Read/list commands: return the data payload directly (e.g.,
{"items":[...]}or{"sysinfo":{...}}) - Write/update commands: return
{"message":"saved"}or similar confirmation - Create commands: return
{"message":"success","rowid":42}— therowidfield contains the new resource ID
Rules
- Successful command output goes to stdout.
- Errors go to stderr.
--formatchanges formatting, not payload semantics.--rawincludes the full API response envelope.- New commands should follow the same output expectations as existing commands.
- Example fixtures in
docs/examples/should be updated when output contracts change.
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 · 70 lines · 0 tokens per session scan A 913ec6b8bfa8
output is a command published in the GitHub repository ikuaios/ikuai-cli (11 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 386 tokens. A static security scan graded it A with 0 findings. It is 100% identical to output, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.