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 RelationalAI/rai-agent-skills --skill rai-predictive-task-generationgit clone --depth 1 https://github.com/RelationalAI/rai-agent-skillsWrote 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/relationalai/rai-agent-skills/rai-predictive-task-generation)<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-predictive-task-generation"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-predictive-task-generation/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/relationalai/rai-agent-skills/rai-predictive-task-generation"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-predictive-task-generation.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 | $0.00222 | $0.04593 |
| Opus 5 | $0.00111 | $0.02296 |
| Sonnet 5 | $0.00044 | $0.00919 |
| Haiku 4.5 | $0.00022 | $0.00459 |
Grade A, and why
rai-predictive-task-generation 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 8d 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAI Predictive Task Table Generator
Summary
What: Produces a prediction task table for a predictive-modeling framework (e.g. GNN) — a Snowflake SQL script that materializes a single labelled table conforming to the schema contract for one of six task types: binary, multiclass, and multilabel classification, regression, link prediction, and repeated link prediction.
When to use: whenever the user wants to create, scaffold, or modify a task table for any of the six task types — "build a task table", "set up a prediction task", "churn prediction task", "link prediction labels" — or has source tables and a prediction goal but doesn't yet know which task type fits.
When NOT to use:
- Building the model's graph/data model from the task table once it exists in Snowflake — that's
rai-predictive-modeling. - Training the model, generating predictions, or evaluating results — that's
rai-predictive-training. - Constructing features — the framework builds those downstream from the task table; this skill only produces the labelled rows and join key.
Overview: this skill has two sub-skills sharing everything below plus the reference files in references/:
| Sub-skill | When | Entry point |
|---|---|---|
guided |
User wants to confirm each decision (table roles, task type, configuration) before any SQL is generated | references/GUIDED.md |
one-shot |
User wants the table built quickly with minimal back-and-forth; requires a live Snowflake connection | references/ONE_SHOT.md |
This top-level SKILL.md asks which mode to use, then hands off to the matching sub-skill for the rest of the session.
Choose a workflow mode
Before anything else — whether the invocation is bare or already includes a data/task description — send this as its own standalone message. Nothing else goes in this message: no greeting, no data request, no SQL.
Predictive Task Table Builder
What ships with it
16 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.
- references/GUIDED.md 60 KB
- references/ONE_SHOT.md 26 KB
- references/task_binary.md 9.7 KB
- references/task_link_prediction.md 7.0 KB
- references/task_multiclass.md 7.0 KB
- references/task_multilabel.md 11 KB
- references/task_regression.md 6.9 KB
- references/task_repeated_link_prediction.md 8.8 KB
- references/utils_auto_execution.md 13 KB
- references/utils_conventions.md 10 KB
- references/utils_cutoff_policies.md 24 KB
- references/utils_one_shot_auto_inference.md 14 KB
- references/utils_output_format.md 10 KB
- references/utils_python_environment.md 3.9 KB
- references/utils_validation.md 9.3 KB
- scripts/validate_schema.py 15 KB runs code
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.
- 8d ago First seen · 238 lines · 222 tokens per session scan A b49b4625f199
rai-predictive-task-generation is a skill published in the GitHub repository RelationalAI/rai-agent-skills (4 stars, last pushed 2d ago), licensed Apache-2.0. It adds 222 tokens to every session and 4,593 once invoked, about $0.0011 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 2026-09-04.
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