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 sfc-gh-dflippo/snowflake-dbt-demo --skill testbed-generatorgit clone --depth 1 https://github.com/sfc-gh-dflippo/snowflake-dbt-demoWrote 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/sfc-gh-dflippo/snowflake-dbt-demo/testbed-generator)<a href="https://agentmods.dev/skills/sfc-gh-dflippo/snowflake-dbt-demo/testbed-generator"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/testbed-generator/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/sfc-gh-dflippo/snowflake-dbt-demo/testbed-generator"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/testbed-generator.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 | $0.00282 | $0.04764 |
| Opus 5.5 | $0.00113 | $0.01906 |
| Sonnet 5.5 | $0.00056 | $0.00953 |
| Haiku 4.5 | $0.00028 | $0.00476 |
Grade A, and why
testbed-generator 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 1mo 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testbed generator — mine + validate + enrich + compile + generate
Drives the deterministic scai testbed mine, validate, enrich, compile, and
generate phases for the whole workload. The data lives in the CLI's opaque state; this
skill only sequences subcommands and surfaces their JSON. It replaces production
data, not source access — a source connection is still required downstream to
capture the baseline.
The core phases are independent and resumable: mine (init → list-unsolved
→ whole-workload branch drill-down) inventories the constraint/branch coverage;
validate reads the mined state and reports readiness (fk gaps / type conflicts
/ unsatisfied constraints) — ready: false is a report, not a failure; enrich
is the reasoning + propose/validate loop that turns unsolved constraints into
enrichment JSON (see the Orchestrate phase below); compile materializes the
data-coupling clusters + coordination spec that data generation consumes;
generate (readiness-gated) writes the CSV pool + manifest deliverable. Each
phase is deterministic — re-running it is safe and cheap.
On Entry
Tell the user:
"Running the testbed mine → validate → enrich → compile → generate phases: I'll inventory the branch/constraint coverage your converted workload needs and show the categorized unsolved-constraint view, report readiness (fk gaps / type conflicts / unsatisfied constraints), run the enrichment loop that turns the unsolved constraints into enrichment JSON (assemble → propose → validate, with a bounded retry/iterate budget), compile the data-coupling / coordination spec and show its cluster summary, then — readiness permitting — generate the CSV pool + manifest and show the row counts. This reads the conversion artifacts and the testbed state; the enrich phase mutates that state (through
propose-enrichments) and only the generate phase writes data files."
Scope
- In scope:
init→list-unsolved(mine) → surface the categorized view;validate→ surface the readiness view;compile→ surface the cluster summary; the two-pass ENRICH reasoning (structural then value prompts) that turns unsolved constraints into enrichment JSON (§enrich below);generate(readiness-gated) → surface row counts + CSV/manifest deliverable; resume each phase; classify failures. - In scope (orchestrate): assemble the per-type fragments into the 8-array envelope, drive
propose-enrichments/validatewith a bounded retry/iterate budget, emit observability counts (SNOW-3782435). - Out of scope:
generate's CSV internals; the per-object test-YAML bridge. - Phase-scoped runs: each driver subcommand (
mine,validate,enrich,compile,generate) can be run and stopped independently — e.g. runvalidateto inspect readiness without generating data.
What ships with it
25 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.
- prompts/critics/joins_critic.md 1.8 KB
- prompts/critics/spec_critic.md 1.5 KB
- prompts/critics/verdict-contract.md 3.2 KB
- prompts/structural/anti_join_tables.md 1.8 KB
- prompts/structural/correlated_groups.md 2.4 KB
- prompts/structural/fk_chains.md 3.0 KB
- prompts/structural/temporal_alignment.md 2.0 KB
- prompts/structural/temporal_window_bindings.md 3.6 KB
- prompts/value/branch_values.md 2.0 KB
- prompts/value/flag_for_llm.md 2.0 KB
- prompts/value/inferred_enum.md 1.8 KB
- prompts/value/must_include.md 2.8 KB
- prompts/value/null_fraction_override.md 1.8 KB
- prompts/value/predicate_fills.md 3.6 KB
- pyproject.toml 256 B
- scripts/assembler.py 9.9 KB runs code
- scripts/critic_backbone.py 22 KB runs code
- scripts/critic_benchmark.py 4.1 KB runs code
- scripts/critic_gate.py 11 KB runs code
- scripts/critic_index.py 17 KB runs code
- scripts/critic_normalize.py 6.7 KB runs code
- scripts/manifest.py 9.6 KB runs code
- scripts/recovery.py 2.1 KB runs code
- scripts/run_pipeline.py 48 KB runs code
- scripts/subcommands.py 10 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.
- 1mo ago First seen · 195 lines · 282 tokens per session scan A 494781695d4f
testbed-generator is a skill published in the GitHub repository sfc-gh-dflippo/snowflake-dbt-demo (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 282 tokens to every session and 4,764 once invoked, about $0.0011 per session on Opus 5.5. 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 2026-09-10.
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