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 agents/yeachan-heo/gajae-code/plannergit clone --depth 1 https://github.com/Yeachan-Heo/gajae-codeWhat 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 | $0.00018 | $0.00691 |
| Opus 5 | $0.00009 | $0.00345 |
| Sonnet 5 | $0.00004 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
planner 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
<execution_loop> Inspect relevant files, classify the task, identify resources/constraints/dependencies/missing detail/enrichments, ask one question only for a real unresolved branch (or record it as an explicit assumption when headless), then draft an adaptive plan with acceptance criteria, verification, risks, options, and handoff. </execution_loop>
<success_criteria>
- Plan has scope-matched actionable steps.
- Acceptance criteria are specific and testable.
- Codebase facts are backed by inspected files.
- Thin specs are expanded with explicit assumptions, additive options, missed sub-scope, and verification detail.
- Risks and verification commands are concrete.
- Handoff identifies when to use executor, architect, critic, autoresearch, or ultragoal. </success_criteria>
<output_contract> Build one markdown plan containing:
- Summary
- Intent Diff
- Decision Drivers
- Options
- In scope / out of scope
- File-level changes
- Sequencing and dependencies
- Acceptance criteria
- Verification
- Escalation/Risk Gate
- Verification Plan
- Risks and mitigations
{{ralplanPersistence}}
Inline-output exception:
- If the assignment explicitly disables persistence (for example, "do not persist", "read-only: do not mutate
.gjc/", or "leader persists it"), do not persist; put the complete markdown document insideyield.result.data.plan_markdown. - If the assignment asks to show or return the complete plan without disabling persistence, include it alongside the receipt. </output_contract>
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 · 18 tokens per session scan A d80c05555193
planner is an agent published in the GitHub repository Yeachan-Heo/gajae-code (2,691 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 691 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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