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Trailmark Finding Triage

OrganizationPopular
trailofbits
trailmark-finding-triage

Performs graph-assisted triage of a single security finding, SARIF result, weAudit annotation, suspicious function, or report excerpt using Trailmark reachability, entrypoint paths, taint, privilege-boundary, blast-radius, caller/callee, and neighborhood evidence. Use when deciding whether one candidate issue is reachable, prioritizing a finding before PoC work, preparing evidence for exploit validation, or checking whether a static-analysis result is actionable.

Overview

Publishertrailofbits
Repositoryskills
Skill nametrailmark-finding-triage
Stars
7.3K
Forks
624
Bundled files
5
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Trailmark Finding Triage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/trailofbits/skills/tree/main/plugins/trailmark/skills/trailmark-finding-triage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/trailmark/skills/trailmark-finding-triage .claude/skills/trailmark-finding-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trailmark Finding Triage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Trailmark Finding Triage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Trailmark Finding Triage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Trailmark Finding Triage

Build a concise graph evidence packet for one candidate finding. This skill answers whether the affected code is reachable, what graph evidence supports or weakens the claim, and what manual review is still required before calling the issue exploitable.

When to Use

  • Triage one static-analysis result before spending PoC time
  • Check whether a manual finding is entrypoint-reachable
  • Build an evidence packet for PoC work
  • Review a single suspicious function discovered during manual audit
  • Decide whether one issue should be promoted, deprioritized, or treated as part of a broader chain analysis

When NOT to Use

  • Multiple weak findings might compose into a stronger chain. Use a chain or composition workflow instead.
  • The user wants a full audit. Use an audit or design-review workflow instead.
  • The user wants remediation verification for a known finding. Use a remediation-review workflow instead.
  • The target is a PR or branch diff. Use graph-evolution plus a differential review workflow.
  • No concrete finding, function, file/line, or suspicious sink exists yet. Use discovery skills first.

Rationalizations to Reject

RationalizationWhy It Is WrongRequired Action
"The scanner says high severity, so reachability is obvious"Static findings need graph and code context before promotionBind the finding to a graph node and check entrypoint paths
"No entrypoint path means impossible"It may mean parser, proxy, or dynamic dispatch limitationsReport the limitation separately from reachability
"An auth check appears on the path, so the issue is safe"The check may enforce the wrong predicate or be bypassed by another pathTreat validation/auth as review targets, not proof
"One reachable path is enough for a PoC claim"The path still needs attacker-controlled inputs and compatible preconditionsSeparate graph reachability from exploitability
"This is probably a chain"Single-finding triage stops at one candidateHand off related findings to a composition workflow

Workflow

Finding Triage Progress:
- [ ] Step 1: Normalize the candidate
- [ ] Step 2: Build or reuse the Trailmark graph
- [ ] Step 3: Bind the candidate to graph node(s)
- [ ] Step 4: Analyze reachability, taint, boundaries, and blast radius
- [ ] Step 5: Decide and emit the evidence packet

Step 1: Normalize the Candidate

Accept file/line, function name, SARIF result, weAudit annotation, Markdown finding excerpt, or a manual claim. Normalize it to:

  • title
  • source type
  • file path and line range if present
  • function or node hint
  • suspected source, sink, or asset
  • claimed impact

If there is no concrete code anchor, stop and ask for one.

For input handling details, see references/input-normalization.md.

Step 2: Build Or Reuse The Graph

Use the public trailmark skill workflow. Prefer an existing fresh exported graph or .trailmark/ artifact when present. Otherwise build a graph with language="auto" or the target's explicit language list, then run engine.preanalysis().

Record the Trailmark version or feature probes used. Feature-gate Trailmark 0.4-only APIs with hasattr() or CLI help checks.

Step 3: Bind The Candidate

Bind by file and line overlap first, then function name plus file. If several nodes match, list every candidate and select the narrowest enclosing node as primary. If no node matches, report a binding limitation instead of guessing.

SARIF and weAudit users should reuse the audit-augmentation workflow for matching and then inspect the annotated node.

Step 4: Analyze Graph Evidence

Run the query recipe in references/query-recipes.md:

  • entrypoint paths to the bound node
  • trust level of each path when available
  • membership in tainted, privilege_boundary, and high_blast_radius subgraphs
  • direct callers and callees
  • high-impact downstream sinks
  • sibling or nearby nodes worth manual review

Do not treat graph reachability as proof of exploitability.

Step 5: Decide And Handoff

Produce one verdict:

VerdictMeaning
PromoteGraph evidence supports reachability and plausible impact
Needs manual reviewEvidence is suggestive but not decisive
DeprioritizeNo reachable path or only trusted/internal paths found
BlockedBinding or Trailmark analysis failed

Write the evidence packet using references/output-format.md.

Hand off promoted PoC-worthy issues to the user's PoC workflow. Hand off related findings to a composition workflow. Hand off repeatable root causes to trailmark-variant-neighborhood, variant-analysis, or a custom Semgrep/CodeQL rule workflow.

Example Prompts

  • "Use Trailmark finding triage on src/Vault.sol:148; I think withdraw can bypass the balance update."
  • "Triage this SARIF result before I spend PoC time: semgrep:error unchecked-transfer in contracts/Bridge.sol line 91."
  • "This report excerpt claims parse_packet is attacker reachable. Build the Trailmark evidence packet and tell me what is still missing."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Trailmark Finding Triage AI skill do?

Performs graph-assisted triage of a single security finding, SARIF result, weAudit annotation, suspicious function, or report excerpt using Trailmark reachability, entrypoint paths, taint, privilege-boundary, blast-radius, caller/callee, and neighborhood evidence. Use when deciding whether one candidate issue is reachable, prioritizing a finding before PoC work, preparing evidence for exploit validation, or checking whether a static-analysis result is actionable.

Why use Trailmark Finding Triage on TypingMind?

Because you install it once and use it with any model. Trailmark Finding Triage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Trailmark Finding Triage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/trailmark/skills/trailmark-finding-triage. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Trailmark Finding Triage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Trailmark Finding Triage?

As many as you like. As long as a model supports skills, you can use Trailmark Finding Triage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Trailmark Finding Triage AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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