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 EliBarak12/Elliot --skill onboard-productgit clone --depth 1 https://github.com/EliBarak12/ElliotWrote 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/elibarak12/elliot/onboard-product)<a href="https://agentmods.dev/skills/elibarak12/elliot/onboard-product"><img src="https://agentmods.dev/badge/skills/elibarak12/elliot/onboard-product/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/elibarak12/elliot/onboard-product"><img src="https://agentmods.dev/badge/skills/elibarak12/elliot/onboard-product.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00067 | $0.01162 |
| Opus 5 | $0.00034 | $0.00581 |
| Sonnet 5 | $0.00013 | $0.00232 |
| Haiku 4.5 | $0.00007 | $0.00116 |
Grade C, and why
onboard-product 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 12d 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.
Elliot plugin: !`curl -s http://localhost:3000/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 — start Elliot first: make dev"` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Elliot plugin: !`curl -s http://localhost:3000/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 — start Elliot first: make dev"` How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard Product Workflow
You are onboarding a user's product into Elliot. Do not look at their data and decide the tools yourself. The whole point of this workflow is that the tools reflect what the user wants agents to do. Interview first, design second.
Workspace state
Elliot plugin: !curl -s http://localhost:3000/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 — start Elliot first: make dev"
Steps
1. Import the user's API collection
Ask the user for whatever description of their API they already have:
- An OpenAPI 3.x spec (URL or JSON), or
- A Postman Collection export (JSON), or
- Raw docs /
curlexamples — if so, help them turn it into one of the above.
Call elliot_import_api_collection with the spec/collection. It returns a set
of proposed tools with token-risk hints. Do not build from this yet — it is
input to the interview, not the answer.
2. Interview the user
Ask these questions, one topic at a time, in plain language. Wait for answers.
- Who are the agents? What product will call these tools (support bot, internal copilot, a customer-facing assistant)?
- What jobs should agents do? Get 3-6 concrete tasks, phrased as a user goal ("find a customer's open invoices and email a reminder"). These matter most — they become the audit seeds later.
- What should be exposed vs hidden? Which operations agents should have, and which to keep off-limits.
- What is destructive? Which operations mutate data or are irreversible and should require a confirmation gate.
- What is sensitive? Field names that must never reach an agent (PII, secrets, internal flags).
- Whose credentials? Does every caller share one account/key (a service
account, public data), or should each end user act as themselves with
their own token (GitHub, Slack, Gmail, anything per-account)? If per-user,
the source uses
auth.scope: "per_user"and each caller connects via OAuth — don't bake in one shared token or add atokenparameter to a tool. Seeelliot://docs/authentication. - Scale? Roughly how much data a typical result spans.
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.
- 12d ago First seen · 88 lines · 67 tokens per session scan C 1f9712487645
onboard-product is a skill published in the GitHub repository EliBarak12/Elliot (11 stars, last pushed 12d ago), licensed MIT. It adds 67 tokens to every session and 1,162 once invoked, about $0.0003 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.
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