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Get Started Free →Use when about to record, claim, rate the severity of, or report any security finding — before marking anything [CONFIRMED] or writing it into the report
.claude/skills/hypnguyen1209-finding-discipline/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 88% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 68% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 43% | 0% |
The Iron Law: No [CONFIRMED] without proof. A finding is a claim about real, demonstrated impact — not a status code, a reflected string, or a hunch. This is the offensive equivalent of test-driven development: the proof is the test, and the finding does not exist until it passes.
Violating the letter of this rule is violating its spirit.
[CONFIRMED] — impact demonstrated AND grounded in an evidence artifact that exists on disk.[POSSIBLE] — reachable but the class proof bar is not yet met. Keep digging; do not report as real.[INFO] — true but no security impact at the claimed severity.A status code is not impact. See skills/references/finding-evidence-standards.md: SSRF needs the internal response; IDOR needs another principal's data; RCE needs command output; XSS needs script execution; open redirect needs an external destination.
Before any finding is recorded or reported:
python skills/vulnerability-analysis/scripts/validate_findings.py --findings f.json --evidence ./evidence — it rejects ungrounded findings and per-class false positives.
finding-validator agent (PASS / KILL / DOWNGRADE).[CONFIRMED] survivor goes in the report. Severity must match what was demonstrated (CVSS honest).See skills/references/finding-validation-runtime.md for the 7-question gate.
If a function calls another, read the callee — do not infer what it does from its name. sanitize(), is_safe(), validate_input() are exactly where vulnerabilities hide; a name is a claim by the author, not evidence. The same applies to a config key, a decorator, or a CVE title: open the thing before you reason about it. An unread dependency in a data-flow claim is a hole.
Every claim in a finding carries a confidence grounded in what you can quote, not in how it feels:
decompiled instruction) supports the claim.
State the assumption; do not hide it.
fact.
This is orthogonal to the [CONFIRMED]/[POSSIBLE]/[INFO] tier (which is about impact); confidence is about how well-grounded the claim is. A High-confidence claim of a Low-impact bug is still Low.
[POSSIBLE]All of these mean: it is [POSSIBLE], not [CONFIRMED]. Get the evidence.
| Excuse | Reality | |--------|---------| | "Pattern-matched, clearly vulnerable" | Pattern ≠ exploit. Demonstrate impact or it's POSSIBLE. | | "I'll attach evidence later" | No artifact on disk = ungrounded = REJECTED now. | | "Blind RCE counts" | No output / no OOB confirmation = POSSIBLE, not CONFIRMED. | | "Severity is obviously Critical" | CVSS must reflect demonstrated impact, not the worst case. | | "The validator is overkill here" | Untested findings are how false positives ship. Run it. | | "The function is called sanitize, so it's safe" | A name is the author's claim, not proof. Read the callee. | | "The solver timed out, so it's not exploitable" | A tool limit is feasibility:null (manual), never false. |
A short list of CONFIRMED findings beats a long list of POSSIBLEs. Killing a false positive is success.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 12,745 | 19,248 | +51% | 1 | 1 | 0% | 1,823 | 3,067 | +68% | 0 | 0 | — |
case-02 | pass→pass | 34,957 | 20,101 | -42% | 1 | 1 | 0% | 2,379 | 3,411 | +43% | 0 | 0 | — |
case-03 | fail→fail | 23,136 | 16,663 | -28% | 1 | 1 | 0% | 2,054 | 2,636 | +28% | 0 | 0 | — |
case-04 | fail→pass | 16,523 | 7,250 | -56% | 1 | 1 | 0% | 2,620 | 2,285 | -13% | 0 | 0 | — |
case-05 | pass→pass | 8,324 | 5,713 | -31% | 1 | 1 | 0% | 1,464 | 1,905 | +30% | 0 | 0 | — |
case-06 | pass→pass | 11,024 | 7,954 | -28% | 1 | 1 | 0% | 1,709 | 1,850 | +8% | 0 | 0 | — |
case-07 | fail→pass | 13,375 | 16,094 | +20% | 1 | 1 | 0% | 2,181 | 2,245 | +3% | 0 | 0 | — |
case-08 | pass→pass | 7,883 | 4,676 | -41% | 1 | 1 | 0% | 1,309 | 1,788 | +37% | 0 | 0 | — |
case-09 | pass→pass | 14,266 | 7,054 | -51% | 1 | 1 | 0% | 2,097 | 2,112 | +1% | 0 | 0 | — |
case-10 | pass→pass | 10,937 | 13,609 | +24% | 1 | 1 | 0% | 1,611 | 1,604 | -0% | 0 | 0 | — |
case-11 | pass→pass | 10,544 | 5,802 | -45% | 1 | 1 | 0% | 1,555 | 1,823 | +17% | 0 | 0 | — |
case-12 | pass→pass | 11,468 | 6,975 | -39% | 1 | 1 | 0% | 1,942 | 2,141 | +10% | 0 | 0 | — |
case-13 | pass→pass | 10,505 | 4,453 | -58% | 1 | 1 | 0% | 1,450 | 1,711 | +18% | 0 | 0 | — |
case-14 | pass→pass | 8,275 | 6,062 | -27% | 1 | 1 | 0% | 1,181 | 1,445 | +22% | 0 | 0 | — |
case-15 | fail→pass | 8,069 | 6,343 | -21% | 1 | 1 | 0% | 1,108 | 2,084 | +88% | 0 | 0 | — |
case-16 | pass→pass | 9,821 | 4,539 | -54% | 1 | 1 | 0% | 1,456 | 1,719 | +18% | 0 | 0 | — |
case-17 | pass→pass | 14,763 | 12,654 | -14% | 1 | 1 | 0% | 1,253 | 2,175 | +74% | 0 | 0 | — |
case-18 | pass→pass | 11,378 | 6,138 | -46% | 1 | 1 | 0% | 1,800 | 2,023 | +12% | 0 | 0 | — |
case-19 | pass→pass | 6,536 | 4,579 | -30% | 1 | 1 | 0% | 867 | 1,589 | +83% | 0 | 0 | — |
case-20 | pass→pass | 11,888 | 12,582 | +6% | 1 | 1 | 0% | 2,731 | 3,829 | +40% | 0 | 0 | — |
case-21 | pass→pass | 5,630 | 5,491 | -2% | 1 | 1 | 0% | 905 | 1,921 | +112% | 0 | 0 | — |
case-22 | pass→pass | 22,592 | 25,214 | +12% | 1 | 1 | 0% | 3,809 | 5,072 | +33% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.