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/ai-analyst-lab/ai-analyst-plus/reliabilitynpx skills add ai-analyst-lab/ai-analyst-plus --skill reliabilitygit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plusWrote 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/ai-analyst-lab/ai-analyst-plus/reliability)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plus/reliability"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plus/reliability.svg" alt="Measured on agentmods" 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 | $0.00121 | $0.01253 |
| Opus 5 | $0.00060 | $0.00626 |
| Sonnet 5 | $0.00024 | $0.00251 |
| Haiku 4.5 | $0.00012 | $0.00125 |
Grade A, and why
reliability 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 5d 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.
This is a copy
86% identical to reliability — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Reliability check
Purpose
Run one analytics question several independent times and report whether the answer is stable (every run agrees) or drifting (runs disagree because the question is under-defined). Stability is necessary, not sufficient: a wrong query is perfectly stable. This check needs no ground truth.
Invocation
/reliability "<the question>" [N] — default N = 5.
Example: /reliability "What's our retention rate?"
How to run it
Step 1 — fire N independent runs
Launch N sub-agents in parallel with the Task/Agent tool (N defaults to 5). They must be genuinely independent: each gets a fresh context and sees ONLY the question, never the other runs' answers. Give each sub-agent exactly this brief:
You are answering one analytics question against the active dataset. Load the normal session context first (knowledge-bootstrap: read
.knowledge/active.yaml, then the active dataset'sschema.md,quirks.md, and manifest from the local datasets dir). For the metric dictionary and semantic context, first resolve the context dir withfrom helpers.context_sync import resolve_context_dir(helpers/context_sync.py) and readmetrics/index.yamlandsemantic/from the RESOLVED dir, not from the local copy. Do not read.knowledge/reliability/history before answering. If the metric you're asked about is defined in the dictionary, use that definition exactly. If it is not, decide for yourself how best to define and measure it. Query the real data with the repo connection (from helpers.connection_manager import ConnectionManager). Answer the question: "". Then return ONLY this block:headline: <the single number you'd report>measured: <one line: numerator, denominator, grain, window, any filter>definition_source: <"metric dictionary" if you used a defined metric, else "my own choice">
Do not let the runs share state. Run them concurrently.
Step 2 — record the runs (tracked + auditable)
Write the N results to a timestamped run directory so every check leaves an audit trail:
.knowledge/reliability/<UTC-timestamp>-<question-slug>/runs.json, shaped as
{"question": "<the question>", "runs": [{"run":1,"headline":"...","measured":"...","definition_source":"..."}, ...]}.
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.
- 5d ago First seen · 82 lines · 0 tokens per session scan A 903f795e4f4f
reliability is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plus (19 stars, last pushed 1mo ago), licensed MIT. It adds 121 tokens to every session and 1,253 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to reliability, differing in 40 lines, and is treated as a copy.
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