GraphJin is a compiler and runtime that gives AI agents a governed graph-based way to work with databases, files, source code, workflows, and other systems through GraphQL and MCP. It is also used as a GraphQL-to-database service, API gateway, Go library, and real-time subscription server. The catalogue add-ons connect agents to GraphJin or support its agent workflow.
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 dosco/graphjin --skill graphjin-envgit clone --depth 1 https://github.com/dosco/graphjinWrote 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/dosco/graphjin/graphjin-env)<a href="https://agentmods.dev/skills/dosco/graphjin/graphjin-env"><img src="https://agentmods.dev/badge/skills/dosco/graphjin/graphjin-env/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dosco/graphjin/graphjin-env"><img src="https://agentmods.dev/badge/skills/dosco/graphjin/graphjin-env.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 56 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00061 | $0.01266 |
| Opus 5.5 | $0.00024 | $0.00506 |
| Sonnet 5.5 | $0.00012 | $0.00253 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
graphjin-env scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s localhost:8090/health How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GraphJin Agent Environment
Use this skill when a user wants to train or measure an agent against a GraphJin environment: running the container, driving graded episodes, collecting trajectories, or interpreting a reward.
For creating and running evaluation suites against a project, use the
graphjin-eval skill instead. This one is about the environment as a training
target.
This skill deliberately states no task counts, no flag defaults and no
measured figures. Those live in /health and at
https://graphjin.com/environment/. Read them from the running server rather
than from here — a skill that carries no facts cannot carry stale ones.
Rules
- Read
/healthbefore anything else, and record it with any number you report. A reward is only comparable against another from the same world under the same contract. The fields that decide it:reward_version,reward_profile,dataset.catalog_hash,dataset.data_anchor,capabilities.suite_fingerprint, andbuild.version. - Check
capabilities.catalog_matchbefore a long run. If it isfalse, the suite's oracles were verified against a different schema and every episode is being graded against the wrong answers. If it is absent, there was nothing to compare. - Never compare an external-mode reward with a hosted one. An external agent's token use never reaches the server, so the efficiency term is unmeasured rather than zero.
- Never present a small difference as a result. The same binary run twice
against the same suite flips a meaningful number of tasks. Use
training/measure.py, which prints a confidence interval and the resolution floor, and quote both. - A
GJ_ENV_variable the server does not read is a startup error, not a default. If the server refuses to start, read the error — it names the variable. - Without a temperature, a sampling group returns n identical answers. The stack pins sampling to zero. If a group comes back with identical rewards, check this before concluding anything about the model.
- Never hand-edit
eval/suite.yml,eval/suite.split.json,world-pack.json, or any file under.graphjin-evals/. Each carries a fingerprint something downstream compares against. - Never spend provider tokens without the user's approval. Commands that call a
model require
--yesand print the call count first; surface that preview.
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.
- 1mo ago First seen · 113 lines · 61 tokens per session scan A 826e1f2e2c0e
graphjin-env is a skill published in the GitHub repository dosco/graphjin (3,213 stars, last pushed 2d ago), licensed Apache-2.0. It adds 61 tokens to every session and 1,266 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-02.
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