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Get Started Free →Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.
.claude/skills/trailofbits-trailmark-variant-neighborhood/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 2 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-22 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-15 | ✓→✓ | = Same ✓ | 2% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 118% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 27% | 0% |
Expand one seed issue into graph-derived variant candidates. This skill generates review targets, not confirmed findings.
trait, hook, handler, adapter, or critical type
variant-analysis, semgrep-rule-creator,static-analysis, or manual review
semgrep-rule-creator directly.
composition workflow.
| Rationalization | Why It Is Wrong | Required Action | |---|---|---| | "Nearby code means variant" | Proximity is only a candidate reason | Rank it as a review target | | "Only exact same names matter" | Variants often share sinks or preconditions, not names | Expand across callers, callees, interfaces, and types | | "Every candidate is a finding" | This skill outputs candidates for review | Avoid vulnerability claims | | "Unreachable candidates can be ignored completely" | They may become reachable after refactors | Rank lower or list as deferred | | "Graph candidates replace semantic pattern work" | Graph structure finds locations, not root-cause semantics | Hand off to variant-analysis, Semgrep, CodeQL, or manual review |
Variant Neighborhood Progress:
- [ ] Step 1: Normalize and bind the seed
- [ ] Step 2: Expand graph neighborhoods
- [ ] Step 3: Rank candidates
- [ ] Step 4: Extract variant pattern guidance
- [ ] Step 5: Emit handoff packetAccept finding text, file/line, function name, or output from trailmark-finding-triage. Bind the seed to a Trailmark node and record the root cause in plain language.
If the seed has no concrete graph binding, stop before inventing variants.
Use the dimensions in references/neighborhood-patterns.md:
Bound expansion to avoid candidate floods.
Rank with references/ranking.md. Prioritize entrypoint-reachable, tainted, boundary-adjacent, high-blast-radius, shared sink, same-interface, and close-distance candidates. Penalize test, mock, generated, vendor, unreachable, and trusted-internal-only candidates.
Summarize what should be searched for syntactically and what requires semantic review. Identify whether follow-up belongs in:
variant-analysissemgrep-rule-creatorstatic-analysis with CodeQL or SARIF-producing toolsUse references/output-format.md. Include ranked candidates, inclusion reasons, exclusions, limitations, and the variant-analysis handoff.
that code is in scope
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-15 | pass→pass | 9,666 | 4,875 | -50% | 1 | 1 | 0% | 1,566 | 1,600 | +2% | 0 | 0 | — |
case-01 | fail→fail | 18,295 | 11,040 | -40% | 1 | 1 | 0% | 2,560 | 2,198 | -14% | 0 | 0 | — |
case-02 | pass→pass | 14,927 | 22,475 | +51% | 1 | 1 | 0% | 2,166 | 4,720 | +118% | 0 | 0 | — |
case-03 | fail→fail | 14,820 | 12,123 | -18% | 1 | 1 | 0% | 2,723 | 3,093 | +14% | 0 | 0 | — |
case-04 | pass→pass | 7,280 | 5,314 | -27% | 1 | 1 | 0% | 1,421 | 1,808 | +27% | 0 | 0 | — |
case-05 | pass→pass | 8,596 | 5,387 | -37% | 1 | 1 | 0% | 1,459 | 1,789 | +23% | 0 | 0 | — |
case-06 | pass→pass | 7,887 | 4,376 | -45% | 1 | 1 | 0% | 1,446 | 1,570 | +9% | 0 | 0 | — |
case-07 | fail→fail | 6,701 | 5,476 | -18% | 1 | 1 | 0% | 817 | 1,489 | +82% | 0 | 0 | — |
case-08 | fail→fail | 6,161 | 7,998 | +30% | 1 | 1 | 0% | 602 | 1,552 | +158% | 0 | 0 | — |
case-09 | fail→fail | 19,913 | 11,247 | -44% | 1 | 1 | 0% | 1,947 | 1,716 | -12% | 0 | 0 | — |
case-10 | pass→pass | 12,966 | 50,145 | +287% | 1 | 1 | 0% | 2,086 | 1,523 | -27% | 0 | 0 | — |
case-11 | pass→pass | 7,907 | 2,913 | -63% | 1 | 1 | 0% | 1,313 | 1,386 | +6% | 0 | 0 | — |
case-12 | pass→pass | 11,722 | 8,425 | -28% | 1 | 1 | 0% | 1,972 | 2,265 | +15% | 0 | 0 | — |
case-13 | pass→pass | 10,776 | 8,075 | -25% | 1 | 1 | 0% | 1,771 | 2,280 | +29% | 0 | 0 | — |
case-14 | pass→pass | 9,863 | 3,988 | -60% | 1 | 1 | 0% | 1,547 | 1,531 | -1% | 0 | 0 | — |
case-16 | pass→pass | 12,021 | 7,136 | -41% | 1 | 1 | 0% | 1,938 | 2,009 | +4% | 0 | 0 | — |
case-17 | pass→pass | 6,208 | 4,519 | -27% | 1 | 1 | 0% | 1,101 | 1,649 | +50% | 0 | 0 | — |
case-18 | fail→pass | 25,125 | 3,134 | -88% | 1 | 1 | 0% | 1,306 | 1,365 | +5% | 0 | 0 | — |
case-19 | pass→pass | 9,346 | 3,869 | -59% | 1 | 1 | 0% | 1,536 | 1,485 | -3% | 0 | 0 | — |
case-20 | pass→pass | 8,261 | 4,844 | -41% | 1 | 1 | 0% | 1,328 | 1,770 | +33% | 0 | 0 | — |
case-21 | pass→pass | 36,684 | 4,486 | -88% | 1 | 1 | 0% | 1,708 | 1,634 | -4% | 0 | 0 | — |
case-22 | fail→pass | 13,180 | 4,644 | -65% | 1 | 1 | 0% | 2,249 | 1,724 | -23% | 0 | 0 | — |
case-23 | pass→pass | 12,416 | 4,635 | -63% | 1 | 1 | 0% | 2,190 | 1,756 | -20% | 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. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 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.