Gajae Code is an external coding-agent harness that works inside repositories or worktrees and guides an agent through planning, review, approval, and code changes. It is for developers who want to use an existing coding-plan subscription while controlling agent work from a terminal, phone, or bot. The catalogue entries provide extensions such as skills, agents, commands, plugins, and MCP integrations for the harness.
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 skills/yeachan-heo/gajae-code/ralplannpx skills add Yeachan-Heo/gajae-code --skill ralplangit clone --depth 1 https://github.com/Yeachan-Heo/gajae-codeWrote 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/yeachan-heo/gajae-code/ralplan)<a href="https://agentmods.dev/skills/yeachan-heo/gajae-code/ralplan"><img src="https://agentmods.dev/badge/skills/yeachan-heo/gajae-code/ralplan.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 | $0.00018 | $0.07657 |
| Opus 5 | $0.00009 | $0.03828 |
| Sonnet 5 | $0.00004 | $0.01531 |
| Haiku 4.5 | $0.00002 | $0.00766 |
Grade A, and why
ralplan 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 6d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralplan (Consensus Planning Alias)
Ralplan is the consensus planning workflow. It triggers iterative planning with Planner, Architect, and Critic agents until consensus is reached, with RALPLAN-DR structured deliberation (short mode by default, deliberate mode for high-risk work).
Usage
/skill:ralplan "task description"
Flags
--interactive: Adds draft-review prompts and one-at-a-time reconciliation. When the final receipt resolvesauto_handoff.effectiveTargettooffwithoutdegradationReason: "planning_stuck", final approval uses anaskworkflow gate; a configured automatic admission is handled by step 8.--deliberate: Forces high-risk deliberation: pre-mortem plus expanded test planning. It may also auto-enable for explicit auth/security, migration, destructive, incident, compliance/PII, or public-API-breakage risk.--architect openai-code/--critic openai-code: Use OpenAI code for that review pass when available; otherwise note the fallback and use default GJC review.gjc.ralplan.autoHandoff: Selects final-plan admission:off(default),ultragoal, orautoresearch. Anautoresearchtarget admits/skill:autoresearchto continue research from the approved plan rather than implement it; it needs no availability probe.PLANNING-STUCKalso resolves every target tooff. Invalid settings reject the final write before any final artifact is persisted. The final receipt's ledger-backed runtime-ownedauto_handoff.effectiveTargetis authoritative across state loss and run switching.--write --stage <type> --stage_n <N> --artifact <markdown file path or markdown string>: Native writer for Planner/Architect/Critic/revision/ADR/final pending-approval markdown under.gjc/_session-{sessionid}/plans/ralplan/<run-id>/; do not edit.gjc/directly.
Corrupt current-session state recovery
For corrupt, tampered, unreadable, or stale current-session ralplan state, run gjc state clear --force --mode ralplan scoped by --session-id, command payload, or GJC_SESSION_ID; it clears only ralplan state for that session.
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.
- 6d ago First seen · 225 lines · 18 tokens per session scan A ed4441940c6b
ralplan is a skill published in the GitHub repository Yeachan-Heo/gajae-code (2,723 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 7,657 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.
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