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/elibarak12/elliot/deploynpx skills add EliBarak12/Elliot --skill deploygit clone --depth 1 https://github.com/EliBarak12/ElliotWhat 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 | $0.00042 | $0.00782 |
| Opus 5 | $0.00021 | $0.00391 |
| Sonnet 5 | $0.00008 | $0.00156 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade C, and why
deploy scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
Runtime health: !`curl -s http://localhost:3001/health 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('status','unknown'))" 2>/dev/null || echo "not running"` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Runtime health: !`curl -s http://localhost:3001/health 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('status','unknown'))" 2>/dev/null || echo "not running"` How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy Connector Workflow
Pre-flight status
!ls connectors/*.connector.json 2>/dev/null || echo "(no connectors)"
Runtime health: !curl -s http://localhost:3001/health 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(d.get('status','unknown'))" 2>/dev/null || echo "not running"
Deploy pipeline (must complete in order)
1. Build
Call elliot_build_connector so the connector reflects the latest tools.
Lint, eval, and export all operate on this built connector.
2. Lint
Call elliot_lint_connector (no arguments — it lints the built connector).
Do not proceed if any errors are reported. Fix all issues, call
elliot_build_connector again, re-lint, and confirm zero errors.
3. Eval
If an eval suite exists: call elliot_run_eval.
All cases must pass. Fix failures before deploying.
4. Ship it
On Elliot Cloud (the marketplace default) — one call: elliot_cloud_publish.
It runs a publish-time smoke gate first (a cache-safe runtime build + tools/list
- a per-tool execute of each auto-callable READ tool), so a connector that would
404 on every agent call is blocked instead of shipped. On success it deploys to a
stable public MCP URL on your tenant and returns it. If it blocks (
lint_errors,missing_secrets,smoke_failed), fix and re-publish. Choose who can call it withauth_mode:api_key(default, shared key),personal(your workspace only),third_party_oauth(each user connects their own upstream account), oropen(anyone, no key — non-sensitive data only). This is the whole deploy on Cloud — skip the local steps below.
Running Elliot locally instead:
elliot_export_connector— write the connector file (path, or the.elliot/connector.jsondefault).elliot_start_runtime— launch the connector-runtime subprocess. Passconnector_pathif you exported to a non-default path, or it loads the wrong (or no) connector. Confirm withelliot_runtime_logs; get the agent URL withelliot_get_connection_config; stop later withelliot_stop_runtime. (These tools are local-only — they aren't served on Elliot Cloud.)
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 · 64 lines · 42 tokens per session scan C 03de5ae444ca
deploy is a skill published in the GitHub repository EliBarak12/Elliot (11 stars, last pushed 6d ago), licensed MIT. It adds 42 tokens to every session and 782 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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Agent skill for release-manager - invoke with $agent-release-manager.
agent-release-swarm
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agent-pagerank-analyzer
Agent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer.
agent-code-analyzer
Agent skill for code-analyzer - invoke with $agent-code-analyzer.
agent-collective-intelligence-coordinator
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator.