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Analyze Findings

  • 46 installs
  • 126 repo stars
  • Updated August 4, 2026
  • seqra/opentaint

Helps with ai & agent building tasks.

About

analyze-findings is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • analyze-findings
  • AI & Agent Building
  • AI-coding skill

Analyze Findings by the numbers

  • 46 all-time installs (skills.sh)
  • +1 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #7,629 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/seqra/opentaint --skill analyze-findings

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Listed on Skillselion
Installs46
repo stars126
Last updatedAugust 4, 2026
Repositoryseqra/opentaint

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Skill: Analyze Findings

A finding file bundles all of one rule's results. Read each result's code flow, split the bundle into distinct vulnerabilities, and give each a TP/FP verdict on its own evidence

Inputs

From the caller; if omitted, fall back to the default. Ask only when a required input is missing and has no sensible default

  • Findings to triage <findings> — the finding tracking file(s); each bundles all of one rule's SARIF results in sarif_hashes
  • SARIF report <report.sarif> — the raw scan output holding the code-flow traces. Default: .opentaint/results/report.sarif

Workflow

1. One result at a time — STOP checklist

For each hash in the bundle, before any verdict:

  • found its SARIF result via sarif_hashes and read the raw codeFlows[]
  • walk every step, source → hops → sink, confirming it's the same tainted value end to end
  • judging each result on its own trace — no verdict shared across results just because they share the rule

2. Split the bundle into logical findings

The results in the file all fired one rule, but may be several different vulnerabilities. Keep results that are the same vulnerability (same sink, same essential flow) together as one finding; move genuinely distinct ones (different sink, or a different flow) into their own finding file with a new finding_name and their sarif_hashes

3. Classify and record

Verdict each logical finding from its flow:

  • TP — the source is attacker-controlled, the sink is genuinely dangerous with that input, and nothing sanitizes it in between
  • FP — a sanitizer/validator neutralizes it, the source isn't actually attacker-controlled (config, constant, server-set), the sink is safe for this input (parameterized, escaped), or the path is infeasible. Record which one

Set verdict and append the reasoning to notes, below the analyzer report already seeded there

Output

  • Each logical finding in its own file with verdict set and the rationale in notes
  • A brief summary to the caller: one line per finding — name, verdict, one-clause reason

Tracking

Editing an existing finding touches only verdict and notes. A split also creates a new finding file — give it the full shape, copying rule_id from the bundle and moving over the results' sarif_hashes and their analyzer report:

finding_name: <new-slug>              # a fresh docker-like name for the split-off vuln
sarif_hashes: [<moved hash>, ...]     # hashes matching this logical vulnerability
rule_id: java/security/sqli.yaml:sqli # same rule as the bundle it came from
verdict: TP                           # pending | TP | FP
notes: >
  <analyzer report for these results — moved from the bundle>
  triage: @RequestParam orderBy is attacker-controlled; reaches ${} in SelectProvider unsanitized → TP
poc: pending
poc_script: null

Gotchas

  • Bulk verdicts are the most common triage error — many results under one shared rationale with the traces unread. One trace, one judgment
  • A rule's bundle is not one finding — split distinct vulnerabilities apart, but keep true duplicates (same sink and flow) together as one finding with multiple sarif_hashes

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