Regression Sweep logo

Regression Sweep

Organization
transilienceai
regression-sweep

Re-validates every previously-confirmed finding against its current target — detects drift (patched, mitigated, re-introduced). Cron-driven weekly sweep across the org's validated finding tree.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill nameregression-sweep
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 Regression Sweep 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/regression-sweep
  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/regression-sweep .claude/skills/regression-sweep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Regression Sweep 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 Regression Sweep 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 Regression Sweep 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.

Regression Sweep

Walk the entire validated/*.json tree, re-fire each finding's poc.py, compare output against the recorded poc_output.txt, and write a weekly drift report. Mounted onto the cloud-agent task #4.

Trigger

Cron weekly (default Mondays 02:00 UTC). May also be invoked ad-hoc after a major patch deployment.

Workflow

  1. Index validated findings. Glob validated/*.json and resolve each entry's FINDING_DIR (under findings/finding-NNN/).
  2. Per finding:
    • Re-run python3 poc.py with a 60-second timeout.
    • Capture stdout/stderr into findings/finding-NNN/evidence/validation/regression-{week}-rerun.txt.
    • Diff against findings/finding-NNN/evidence/validation/poc-rerun-output.txt (the validator's original re-run output) using a normalized line-set comparison (strip timestamps, request IDs, ephemeral tokens).
    • Re-check the finding's CVE via tools/nvd-lookup.py — has severity changed?
  3. Classify each finding into one of:
    • still_valid — re-run matches baseline within tolerance, CVSS unchanged.
    • drift_severity — re-run matches, but CVSS shifted ≥1.0 (NVD re-scored).
    • newly_invalid — re-run output diverges, exploit no longer fires. Likely patched.
    • newly_revalidated — finding had been marked REJECTED later, but now fires again. Regression.
    • inconclusive — re-run errored (network, target unreachable). Retry next sweep.
  4. Write report to artifacts/regression-{YYYYWww}.json + human-readable regression-{YYYYWww}.md. Transitions (newly_revalidated, drift_severity, newly_invalid) appear in the report under explicit headers for analyst review.

Output

{OUTPUT_DIR}/
  artifacts/
    regression-{YYYYWww}.json         # machine-readable result
    regression-{YYYYWww}.md           # human summary
  findings/finding-NNN/evidence/validation/
    regression-{YYYYWww}-rerun.txt    # captured re-run output

regression-{week}.json schema:

json
{
  "week": "2026W19",
  "swept_at": "2026-05-13T02:00:00Z",
  "counts": {"still_valid": 47, "drift_severity": 2, "newly_invalid": 5, "newly_revalidated": 1, "inconclusive": 3},
  "findings": [
    {"finding_id": "finding-012", "asset": "asset42", "cve": "CVE-2024-12345",
     "verdict": "newly_invalid", "reason": "PoC output diverged: response now 404",
     "baseline_cvss": 9.8, "current_cvss": 9.8}
  ]
}

Rules

  1. Demonstrate, never disrupt. Re-firing a PoC is observation, not mutation. Every poc.py already satisfied the demonstrate-only constraints at validation time (task-03 Safety section); the sweep simply re-executes the same script — which by contract reads a proof signal and exits. If a PoC at re-run attempts a mutating action it should not have contained originally, the sweep aborts that finding with inconclusive and emits a stderr WARN — the PoC needs re-validation, not regression scoring.
  2. Bounded per-PoC time. 60-second timeout per poc.py. Timeouts → inconclusive, not newly_invalid. Avoids false-positive "patched" claims caused by network blips.
  3. Normalized diff. Strip timestamps (\d{4}-\d{2}-\d{2}T\d{2}:\d{2}), request-IDs ([a-f0-9]{32,}), and ephemeral session tokens before comparing. Real exploit output is structurally stable.
  4. No new findings. A regression sweep can flip status of existing findings but cannot create new ones. Newly observed vulns belong to the Validation Run task, not this skill.
  5. Idempotent. Re-running the same week's sweep overwrites the same regression-{YYYYWww}.json. The per-finding regression-{week}-rerun.txt is timestamped to preserve history.
  6. Cap concurrent re-runs. Max 5 parallel PoC re-runs per sweep to avoid hammering production assets.

References

  • reference/diff-normalization.md — full normalization rules and per-finding output-stability heuristics.

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 Regression Sweep AI skill do?

Re-validates every previously-confirmed finding against its current target — detects drift (patched, mitigated, re-introduced). Cron-driven weekly sweep across the org's validated finding tree.

Why use Regression Sweep on TypingMind?

Because you install it once and use it with any model. Regression Sweep 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 Regression Sweep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/transilienceai/communitytools/tree/main/skills/regression-sweep. 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 Regression Sweep?

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 Regression Sweep?

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

Is the Regression Sweep 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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