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 skills add amirkiarafiei/subagent-cli-skills --skill junie-cligit clone --depth 1 https://github.com/amirkiarafiei/subagent-cli-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amirkiarafiei/subagent-cli-skills/junie-cli)<a href="https://agentmods.dev/skills/amirkiarafiei/subagent-cli-skills/junie-cli"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/subagent-cli-skills/junie-cli.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00078 | $0.01399 |
| Opus 5 | $0.00039 | $0.00700 |
| Sonnet 5 | $0.00016 | $0.00280 |
| Haiku 4.5 | $0.00008 | $0.00140 |
Grade A, and why
junie-cli 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Junie CLI (subagent/task delegation)
Use Junie CLI to run a separate long-horizon pass over the repo: code reviews, multi-step implementation, or broad refactors—similar to handing a task to a subagent. You stay orchestrator: smaller prompts, less context burn.
When to use Junie CLI
- Automated Code Reviews: Use the specialized
--reviewagent for deep diff-aware reviews. - Large or multi-step work: several files, phases, or checkpoints.
- Conflict Resolution: Specialized
--mergeand--rebasemodes for resolving git conflicts. - Parallel mental lane: you continue planning or reviewing while Junie runs a bounded task.
- User explicitly asks for Junie or “use Junie for this.”
When not to use
- Small / single-step tasks answerable with one or two edits or a short explanation.
- Tight feedback loops where the user wants rapid back-and-forth refinement in one thread.
- Secrets or policy-sensitive flows—avoid piping credentials; redact before delegating.
- Already-loaded context where duplicating the whole plan adds no value—handle locally.
- Low ROI (Return on Investment): If the task is "needle-in-a-haystack" (requires high precision over a single line) or if the time to compose the Handoff Table exceeds the time to simply edit the file locally. Delegation should only be used when the "mental offloading" outweighs the "handoff overhead."
Delegation and context (critical)
Isolated subagent context saves tokens but splits the story: Junie does not see the main session's full thread. Poor handoffs cause misread subtasks, conflicting assumptions (stack, style, APIs), and wasted edits.
When composing the single Junie prompt, treat it as passing enough shared state, not just a title:
| Include | Why |
|---|---|
| Original goal | Same north star as the user—not only the immediate micro-task. |
| Decisions already made | Framework, patterns, naming, auth approach, “use X not Y”—anything that would otherwise be guessed wrong. |
| Scope | Paths, modules, and explicit out of scope / do-not-touch areas. |
| Constraints | Performance, a11y, compatibility, review gates, “no new deps,” etc. |
| Verification | Explicit command (e.g. npm test, lint) the subagent must run and pass before returning. |
| Expected output | e.g. “summarize then list files changed,” “report only—no edits,” or “apply edits with minimal diff.” |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 93 lines · 78 tokens per session scan A d262580e106e
junie-cli is a skill published in the GitHub repository amirkiarafiei/subagent-cli-skills (5 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,399 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
review
Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.
work
Execute an approved wish plan — orchestrate subagents per task group with fix loops, validation, and review handoff.
project-context
Use PowerContext project memory and handoff tools through MCP when continuing prior work, recalling decisions, maintaining durable memory, or transferring work across tasks, sessions, or agents.
brainstorm
Explore ambiguous or early-stage ideas interactively — tracks wish-readiness and crystallizes into a design for wish.
wish
Convert an idea into a structured wish plan with scope, acceptance criteria, and execution groups for work.