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 jmagly/aiwg --skill reproducibility-validategit clone --depth 1 https://github.com/jmagly/aiwgWrote 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/jmagly/aiwg/reproducibility-validate)<a href="https://agentmods.dev/skills/jmagly/aiwg/reproducibility-validate"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/reproducibility-validate.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 180 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 181 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00025 | $0.01602 |
| Opus 5 | $0.00013 | $0.00801 |
| Sonnet 5 | $0.00005 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
reproducibility-validate 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 4d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproducibility Validate
You run a workflow multiple times and compare outputs to produce a similarity score and pass/fail verdict, confirming that the workflow produces consistent results across executions.
Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "is this workflow stable" → run reproducibility validation with defaults
- "check if results are consistent" → run reproducibility validation
- "does this run the same way every time" → run reproducibility validation
- "test determinism" → run reproducibility validation
- "compare workflow outputs" → run reproducibility validation
Trigger Patterns Reference
| Pattern | Example | Action |
|---|---|---|
| Default validation | "validate reproducibility of onboarding-flow" | Run aiwg reproducibility-validate onboarding-flow |
| Custom run count | "validate with 5 runs" | Run aiwg reproducibility-validate <id> --runs 5 |
| Custom threshold | "validate with 99% threshold" | Run aiwg reproducibility-validate <id> --threshold 0.99 |
| Full options | "3 runs, 90% threshold" | Run aiwg reproducibility-validate <id> --runs 3 --threshold 0.90 |
Behavior
When triggered:
-
Extract intent:
- What is the workflow ID or name to validate?
- How many runs? (default: 3)
- What similarity threshold must be met to pass? (default: 0.95)
-
Run the command:
# Default: 3 runs, 0.95 threshold aiwg reproducibility-validate <workflow-id> # Custom run count aiwg reproducibility-validate <workflow-id> --runs 5 # Custom threshold aiwg reproducibility-validate <workflow-id> --threshold 0.99 # Both aiwg reproducibility-validate <workflow-id> --runs 5 --threshold 0.99 -
Scoring method:
- Structured outputs (JSON, YAML): exact match required — score is 0 or 1 per artifact
- Text outputs (Markdown, prose): semantic similarity score (0.0–1.0) computed across runs
- Overall score: weighted average across all artifacts in the workflow
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.
- 4d ago First seen · 182 lines · 25 tokens per session scan A c3bab5bb9efe
reproducibility-validate is a skill published in the GitHub repository jmagly/aiwg (209 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 1,602 once invoked, about $0.0001 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 2026-09-03.
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