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Ti Ingest

Organization
transilienceai
ti-ingest

Threat-intel signal ingest — converts a CVE + affected-asset + claim payload into a queued engagement-scope row for the validation pipeline.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill nameti-ingest
Stars
565
Forks
81
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Ti Ingest 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/transilienceai/communitytools/tree/main/skills/ti-ingest
  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/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/ti-ingest .claude/skills/ti-ingest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ti Ingest 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 Ti Ingest 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 Ti Ingest 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.

Threat-Intel Signal Ingest

Convert a threat-intel signal into a queued engagement-scope row that the Exploitability Validation Run (cloud-agent task #3) consumes.

Trigger

Two modes:

  • Prompt-invoked: an analyst runs the skill against a signal.json they have on hand.
  • Scheduled poll: the cloud-agent runtime polls {OUTPUT_DIR}/inbox/ on a cron (default every 15 minutes) and processes any new signal files it finds.

Workflow

  1. Read the payload — tools/ti-ingest.py --in <path-or-stdin>.
  2. Validate schema — required keys: signal_id, cve (one or more), assets (one or more), claim. Optional: confidence, source, references.
  3. Enrich with NVD — for every CVE in the payload, run tools/nvd-lookup.py <CVE> and attach {score, severity, cwe} to the row.
  4. De-dup — skip rows whose (asset, cve) pair already exists in queue/scope-*.json. Re-queue only if the prior row's status is REJECTED AND the TI signal carries a higher confidence than the last attempt.
  5. Write queue row — one JSON file per (asset, cve) pair at queue/scope-{ts}-{asset_id}-{cve}.json. The Validation Run task picks these up.

Output

{OUTPUT_DIR}/
  inbox/
    signal-{ts}.json        # raw payload, kept for audit
  queue/
    scope-{ts}-{asset}-{cve}.json

Per scope row:

json
{
  "scope_id": "scope-20260513-asset42-CVE-2024-12345",
  "signal_id": "ti-2026-0042",
  "asset": {"id": "asset42", "url": "https://app.example.com", "tier": "revenue"},
  "cve": "CVE-2024-12345",
  "nvd": {"score": 9.8, "severity": "CRITICAL", "cwe": "CWE-79"},
  "claim": "Reflected XSS via search parameter",
  "confidence": "high",
  "source": "vendor-advisory",
  "references": ["https://..."],
  "queued_at": "2026-05-13T10:00:00Z",
  "status": "queued"
}

Rules

  1. Idempotent. Re-running on the same payload must not produce duplicate queue rows.
  2. No execution. Ingest never runs PoCs. It only queues scope for downstream tasks.
  3. Audit trail. Raw payload is always copied to inbox/ before any transformation.
  4. NVD failures are non-blocking. If nvd-lookup errors, write the row with nvd: null and nvd_error: "..."; do not drop the signal.
  5. Asset must be in inventory. Cross-check asset.id against artifacts/org-surface.json (from Org Recon Refresh task). Unknown assets get a row but flagged unknown_asset: true so the validator skips them.

References

  • reference/ingest-schema.md — full input/output JSON schemas.

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 Ti Ingest AI skill do?

Threat-intel signal ingest — converts a CVE + affected-asset + claim payload into a queued engagement-scope row for the validation pipeline.

Why use Ti Ingest on TypingMind?

Because you install it once and use it with any model. Ti Ingest 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 Ti Ingest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/ti-ingest. 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 Ti Ingest?

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 Ti Ingest?

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

Is the Ti Ingest AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT 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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