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 commands/modellix/modellix-plugin/downloadgit clone --depth 1 https://github.com/Modellix/modellix-pluginWhat 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 | $0.00015 | $0.00325 |
| Opus 5 | $0.00008 | $0.00162 |
| Sonnet 5 | $0.00003 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
download 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. If the download fails with a private or reserved network error (common when a local proxy maps `file.modellix.ai` into `198.18.0.0/15`), retry with `--allow-private-network` for trusted Modellix CDN hosts, or fetch `r What it actually says
description: Download Modellix task results to a local directory before the resource URLs expire. argument-hint: [optional task id] [optional output directory]
Download Modellix results. Target: $ARGUMENTS
Read-only against the API: downloading costs nothing and never resubmits a task.
- Resolve the task id: use the one in
$ARGUMENTS, otherwise take the most recent successful entry frommodellix-cli task history --limit 20 --jsonand say which one you picked. - Download to
./outputsunless$ARGUMENTSnames a directory:
modellix-cli task download <task_id> --output-dir ./outputs --json
Existing files are preserved by default; pass --overwrite only when the user asks for it.
- If the task is not in a successful terminal state yet, stop and use
/modellix:tasksto wait — do not submit a new task. - If the download fails with a private or reserved network error (common when a local proxy maps
file.modellix.aiinto198.18.0.0/15), retry with--allow-private-networkfor trusted Modellix CDN hosts, or fetchresult.resources[].urlwith curl and name filesmodellix-{model_slug}-{timestamp}.{ext}(slashes in the slug become hyphens). - Resource URLs expire in about 7 days. If they are already gone, say so plainly instead of silently regenerating.
- Report the local file paths and byte counts.
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 · 23 lines · 0 tokens per session scan A 25a8911c52d5
download is a command published in the GitHub repository Modellix/modellix-plugin (1 stars, last pushed 21d ago), licensed MIT. It adds 15 tokens to every session and 325 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other commands, from other repositories
critic
Deep-review a specific cluster with CellTypePilot's Annotation Critic.
sdlc-implement
Standalone issue implementation with Ralph Loop — implements a single Linear issue or free-form spec using fullstack-engineer + self-correcting retry loop.
sdlc
Master orchestrator for the complete 5-phase SDLC pipeline. Runs all phases sequentially with approval gates and handoffs between each phase. Organizes artifacts into handoffs/, docs/, and projects/ directories.
sdlc-diagnose
Automated Root-Cause Analysis (RCA) and surgical fix engine for massive enterprise codebases (up to 1.5M+ files).
sdlc-security
Automatically audit, patch vulnerabilities, upgrade dependencies, and implement enterprise security protocols (Zero-Trust, mTLS, OWASP fixes).
language-mode-commands
Sets the output language for all system responses. Supports any language the underlying model can produce fluently. Persists across sessions.