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 skills add Yeachan-Heo/gajae-code --skill autoresearchgit 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/autoresearch)<a href="https://agentmods.dev/skills/yeachan-heo/gajae-code/autoresearch"><img src="https://agentmods.dev/badge/skills/yeachan-heo/gajae-code/autoresearch.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.03151 |
| Opus 5 | $0.00012 | $0.01576 |
| Sonnet 5 | $0.00005 | $0.00630 |
| Haiku 4.5 | $0.00002 | $0.00315 |
Grade A, and why
autoresearch 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch Workflow
Use when the user asks for autoresearch, or gives a bounded research goal whose deliverable is a defensible verdict rather than code ("find out", "investigate", "benchmark and draw a conclusion").
Usage
/skill:autoresearch "<research goal>"
/skill:autoresearch --spec .gjc/_session-{sessionid}/specs/deep-interview-<slug>.md
Invoke this workflow as /skill:autoresearch; the durable state behind it is driven by the gjc autoresearch runtime command.
Purpose
autoresearch runs one goal-directed research mission: it interleaves web research with data/environment experimentation and ends on a single structured, best-effort verdict. The verdict receipt carries a structured status, evidence[], caveats[], and the evaluator identity that issued it. The mission is research, NOT implementation: its durable outputs are findings, evidence, run records, and a verdict — never product code.
All mission state persists per session under .gjc/_session-{sessionid}/autoresearch/ and survives across gjc autoresearch invocations. The global ~/.gjc/autoresearch store is never written.
Always-used command examples
Use these exact gjc autoresearch commands before spending tool calls rediscovering syntax:
gjc autoresearch --spec <deep-interview-spec-path>
gjc autoresearch "<goal>"
gjc autoresearch
gjc autoresearch read --json
gjc autoresearch clear
intake --spec <path>(or the bare--specflag) — spec intake from a persisted deep-interview spec; asks zero questions."<goal>"or bare invocation — cold intake; goal, constraints, and deliverables must be clarified before research begins.read --json— current mission artifact plus the append-only ledger snapshot.clear— retire the mission artifact and its working set, recordingmission_clearedin the ledger. This never touches the sessionpythonREPL kernel; reset that with thepythontool's ownclearaction.
Use when
Use when the user wants a bounded research mission whose output is a defensible verdict: a question that needs evidence from the web, local data, or both before any conclusion is drawn ("does X hold for this dataset?", "which approach benchmarks best?", "what changed between these two releases?"); an explicit request to run
autoresearch; or a goal whose acceptance is a structured verdict with evidence and caveats.
What ships with it
2 files 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 · 136 lines · 24 tokens per session scan A babcb5bea9cf
autoresearch is a skill published in the GitHub repository Yeachan-Heo/gajae-code (2,737 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 3,151 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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