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/jl-cmd/claude-dev-env/grokifynpx skills add jl-cmd/claude-dev-env --skill grokifygit clone --depth 1 https://github.com/jl-cmd/claude-dev-envWrote 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/jl-cmd/claude-dev-env/grokify)<a href="https://agentmods.dev/skills/jl-cmd/claude-dev-env/grokify"><img src="https://agentmods.dev/badge/skills/jl-cmd/claude-dev-env/grokify.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 | $0.00016 | $0.01370 |
| Opus 5 | $0.00008 | $0.00685 |
| Sonnet 5 | $0.00003 | $0.00274 |
| Haiku 4.5 | $0.00002 | $0.00137 |
Grade A, and why
grokify 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 3d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grokify
Principle
One paste-ready handoff turns this session's work into a plan a Grok Build session can execute alone. Grok gets no access to this conversation, so the handoff carries everything: repos, findings, constraints, the phased plan, and a Claude advisor Grok reaches through the claude CLI.
Gotchas
- Grok Build can use
spawn_subagent,--agent/ agent definitions, and can read skills under the user's Claude config paths. Skill evals measure these whenGROK_CAPABILITY_EVALS=1(seeevals/README.md). - The grokify handoff's Claude-tier advisor is an out-of-process
claude -pbind/resume. Never write Claude Agent-tool,session-advisor, or SendMessage protocol into the handoff for that advisor path — product design for a Claude-model advisor, not a claim that Grok lacks agents. - Grok does not expose a Claude/GSD Workflow tool. When that tool is required, workflow orchestration stays with Claude.
- A
--resumeafter a usage-limit failover to another binary fails, because a session store belongs to the binary that minted it. The handoff must tell Grok to treat that failure as starting over: re-send the charter plus a compact recap, capture the newsession_id. - Conversation-relative phrases ("as discussed", "the plan above", "the earlier choice") are dead text to Grok — every statement stands on its own.
- Copy findings' measured numbers and
file:linecitations into the handoff exactly, and label each figure measured, bounded, or unverified. - The advisor session starts empty. The bind step must pipe the findings, constraints, and plan into the charter, or every later consult is answered from nothing.
claudesessions are project-scoped by working directory. The handoff must tell Grok to run every bind and every--resumewith the cwd set to the repo root it names — a bind launched from the Grok sandbox cwd files the session under that other project, and a later resume from the real worktree reportsNo conversation found with session ID. Treat a session-not-found error as a wrong-cwd or expired-session signal, not a model failure.--output-format jsonreturns a JSON array of events, not one object.session_idcomes from any event; the reply text is thetype == "result"event's.resultfield. A parser expecting one top-level object reports a missing session id on a working bind.- The charter travels as a file piped to stdin, or as one clean argv string. Half-escaped multiline shell expansion mangles the prompt in transit and the advisor sees a fragment.
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.
- 3d ago First seen · 67 lines · 16 tokens per session scan A 4b19584004f2
grokify is a skill published in the GitHub repository jl-cmd/claude-dev-env (5 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 1,370 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 2026-08-31.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
auditing-subgroup-fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…