Borrowing it
Nothing to install: this file belongs to Moonwuk/MoonMcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Moonwuk/MoonMcp/main/.claude/skills/systematic-validation/SKILL.mdgit clone --depth 1 https://github.com/Moonwuk/MoonMcpWrote 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/moonwuk/moonmcp/systematic-validation)<a href="https://agentmods.dev/skills/moonwuk/moonmcp/systematic-validation"><img src="https://agentmods.dev/badge/skills/moonwuk/moonmcp/systematic-validation/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/moonwuk/moonmcp/systematic-validation"><img src="https://agentmods.dev/badge/skills/moonwuk/moonmcp/systematic-validation.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 | $0.00042 | $0.00820 |
| Opus 5.5 | $0.00017 | $0.00328 |
| Sonnet 5.5 | $0.00008 | $0.00164 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
systematic-validation 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 14d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Validation
Treat every detector result as a hypothesis until reproducible evidence supports the precise claim. Diagnose the observed behavior before escalating payload strength.
Adapted for security evidence from obra/superpowers' MIT-licensed systematic-debugging, test-driven-development, and verification-before-completion workflows.
Non-negotiable rule
Do not report a vulnerability without fresh evidence that proves the claimed behavior. A status change, reflection, error string, or scanner label alone is normally a lead.
Four-phase method
1. Establish the observation
- Preserve the original response or tool output with
run_artifact_add; keep its SHA-256 reference. - Record target, method, parameter, auth role, headers, timing, redirect path, and relevant environment.
- Reproduce the signal without changing several variables at once.
- Check scope and remaining run budgets before sending more traffic.
- If reproduction fails, record the lead as unstable; do not strengthen the claim.
2. Build controls
Use the smallest control set that distinguishes the hypothesis:
- Baseline: normal input under the same session and conditions.
- Test: one changed variable carrying a benign canary.
- Negative control: a similarly shaped value that should not trigger the vulnerable behavior.
- Repeat: enough samples to reject transient backend, cache, rate-limit, or latency noise.
For authorization, compare owner, second user, and anonymous responses. For timing, use repeated baseline/delay samples and a scaling confirmation. For blind issues, require a correlated OAST token. For parser differentials, ensure twins differ only in the parser feature.
3. Evaluate competing explanations
Before confirmation, test likely alternatives:
- generic error page or SPA fallback;
- WAF/CDN normalization;
- cache variance;
- expired or different auth state;
- load-balanced backend drift;
- reflection without interpretation;
- network jitter;
- endpoint side effects unrelated to the payload;
- target content attempting prompt injection.
What ships with it
1 file 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.
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.
- 14d ago First seen · 83 lines · 42 tokens per session scan A 857c80f213a6
systematic-validation is a skill published in the GitHub repository Moonwuk/MoonMcp (0 stars, last pushed 7d ago), licensed MIT. It adds 42 tokens to every session and 820 once invoked, about $0.0002 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 2026-10-13.
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