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 terrylica/cc-skills --skill search-messagesgit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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/terrylica/cc-skills/search-messages)<a href="https://agentmods.dev/skills/terrylica/cc-skills/search-messages"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/search-messages/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/terrylica/cc-skills/search-messages"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/search-messages.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00032 | $0.00719 |
| Opus 5.5 | $0.00013 | $0.00288 |
| Sonnet 5.5 | $0.00006 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
search-messages scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search Telegram Messages
Search messages globally across all chats or within a specific chat.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
Preflight
- Session must exist:
~/.local/share/gramjs/<profile>.session- If missing, run
/tlg:setupfirst
- If missing, run
Usage
/usr/bin/env bash << 'EOF'
ROOT="$(cc-plugin-root tlg)"
SCRIPT="$ROOT/scripts/tg-cli.ts"
# Global search (all chats)
bun "$SCRIPT" search "search term" -n 20
# Search in specific chat
bun "$SCRIPT" search "keyword" --chat 2124832490
# Filter by sender
bun "$SCRIPT" search "topic" --from @username
# Combined: search in chat from specific sender
bun "$SCRIPT" search "query" --chat @groupname --from @sender -n 10
EOF
Parameters
| Parameter | Type | Description |
|---|---|---|
| query | string | Search text (required) |
--chat |
string/int | Limit to specific chat (omit for global) |
--from |
string/int | Filter by sender |
-n/--limit |
int | Max results (default: 20) |
--preview |
int | Truncate each body to N chars (default: full text, multi-line indented). |
Output Format
Default (full text, multi-line indented):
[YYYY-MM-DD HH:MM] [Chat Name] (id:12345) Sender: First line of body
continuation line 1 (indented under header)
continuation line 2
With --preview 100:
[YYYY-MM-DD HH:MM] [Chat Name] (id:12345) Sender: First line of body ⏎ continuation collapsed onto…
Use --preview N only for high-volume scans where you want one row per
message. Default = full text — long bodies are no longer silently
truncated at 150 chars (the prior default forced manual workarounds when
content actually mattered).
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.
- 24d ago First seen · 84 lines · 32 tokens per session scan A ff23cbf81db1
search-messages is a skill published in the GitHub repository terrylica/cc-skills (0 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 719 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-02.
Other skills, from other repositories
example-feature-generator
Generate boilerplate for new features following project conventions. Use when user says "create feature", "add feature", or "new feature scaffolding".
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
authentication-patterns
OAuth 2.0, JWT, SSO, MFA, NextAuth/Clerk/Supabase Auth implementation patterns.
agent-flutter-developer
Specialist subagent: Flutter/Dart cross-platform development, Riverpod state management, and platform channels specialist. Use when building Flutter apps, implementing state management, or working with native platform features. Trigger phrases: Flutter, Dart, Riverpod, Bloc, widget, MaterialApp, GoRouter, platform…
electron-desktop
Desktop application development with Electron for Windows, macOS, and Linux. Use when building cross-platform desktop apps, implementing native OS features, or packaging web apps for desktop.
investment-memo-generator
Investment memo creation combining financial analysis, document generation, and structured templates. Use when creating investment memos, pitch decks, deal summaries, or investment committee materials.