Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add transilienceai/communitytools --skill regression-sweepgit clone --depth 1 https://github.com/transilienceai/communitytoolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/transilienceai/communitytools/regression-sweep)<a href="https://agentmods.dev/skills/transilienceai/communitytools/regression-sweep"><img src="https://agentmods.dev/badge/skills/transilienceai/communitytools/regression-sweep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/transilienceai/communitytools/regression-sweep"><img src="https://agentmods.dev/badge/skills/transilienceai/communitytools/regression-sweep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01103 |
| Opus 5 | $0.00022 | $0.00551 |
| Sonnet 5 | $0.00009 | $0.00221 |
| Haiku 4.5 | $0.00004 | $0.00110 |
Grade A, and why
regression-sweep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Index validated findings. Glob
validated/*.jsonand resolve each entry'sFINDING_DIR(underfindings/finding-NNN/). - Per finding:
- Re-run
python3 poc.pywith 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?
- Re-run
- 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 markedREJECTEDlater, but now fires again. Regression.inconclusive— re-run errored (network, target unreachable). Retry next sweep.
- Write report to
artifacts/regression-{YYYYWww}.json+ human-readableregression-{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:
{
"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}
]
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 68 lines · 43 tokens per session scan A 20bbecde1f71
regression-sweep is a skill published in the GitHub repository transilienceai/communitytools (515 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,103 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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