Telegram Footer Patch

PassAudited by ClawScan on May 10, 2026.

Overview

The skill openly patches OpenClaw’s Telegram reply code to add a footer; it is sensitive because it changes installed runtime files, but the behavior is disclosed, user-directed, and includes dry-run, backup, validation, and rollback guidance.

This looks like a transparent, purpose-built patching skill rather than malicious code. Still, it changes OpenClaw’s installed runtime files, so inspect the scripts, run dry-run first, back up the system or use staging, verify the exact files touched, restart only when ready, and confirm the revert script works.

Findings (3)

Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.

What this means

Running the patch can change how OpenClaw sends Telegram replies and could break delivery if the wrong bundle is patched.

Why it was flagged

The skill intentionally modifies OpenClaw's installed runtime bundle files. This is disclosed and purpose-aligned, but it is a high-impact local change that can affect gateway behavior.

Skill content
it **writes into your OpenClaw installation directory**
Recommendation

Run the dry-run first, use a staging/container environment if possible, confirm backups are created, and test rollback before relying on it in production.

What this means

A user who runs the smoke test will apply the patch to local OpenClaw bundles, not merely perform a passive check.

Why it was flagged

The smoke-test workflow does not only inspect files; it runs the patch script and changes candidate OpenClaw bundle files. SKILL.md also discloses this apply step.

Skill content
python3 "$PATCH_SCRIPT" --dist "$DIST" --auto-discover
Recommendation

Treat the smoke test as a patch-and-verify workflow. Run it only after reviewing the target dist path and being ready to revert.

What this means

Telegram private-chat replies may expose session metadata such as model provider, model name, thinking level, and token/context usage to the chat recipient.

Why it was flagged

The injected delivery helper reads OpenClaw's local session store to retrieve model, thinking level, and token/context values for the footer.

Skill content
await fs.readFile(`${homeDir}/.openclaw/agents/main/sessions/sessions.json`, "utf8")
Recommendation

Install only if you are comfortable showing that metadata in Telegram private chats, and avoid using it where session metadata is considered sensitive.