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 timurgaleev/vibestack --skill connect-reviewgit clone --depth 1 https://github.com/timurgaleev/vibestackWrote 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/timurgaleev/vibestack/connect-review)<a href="https://agentmods.dev/skills/timurgaleev/vibestack/connect-review"><img src="https://agentmods.dev/badge/skills/timurgaleev/vibestack/connect-review/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/timurgaleev/vibestack/connect-review"><img src="https://agentmods.dev/badge/skills/timurgaleev/vibestack/connect-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 18 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00098 | $0.08311 |
| Opus 5 | $0.00049 | $0.04156 |
| Sonnet 5 | $0.00020 | $0.01662 |
| Haiku 4.5 | $0.00010 | $0.00831 |
Grade A, and why
connect-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to invoke
Use when: "review the contact flow", "review this Connect solution", "is the Lex bot well designed", "why is the voice bot slow", "review the IVR", "check the phone assistant before go-live".
Preamble
eval "$(~/.vibestack/bin/vibe-slug 2>/dev/null)" 2>/dev/null || SLUG="unknown"
_LEARN_FILE="${VIBESTACK_HOME:-$HOME/.vibestack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
_LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
echo "LEARNINGS: $_LEARN_COUNT entries loaded"
if [ "$_LEARN_COUNT" -gt 5 ] 2>/dev/null; then
~/.vibestack/bin/vibe-learnings-search --limit 5 2>/dev/null || true
fi
else
echo "LEARNINGS: none yet"
fi
{{include lib/snippets/session-host.md}}
{{include lib/snippets/decision-brief.md}}
{{include lib/snippets/working-protocols.md}}
{{include lib/snippets/state-protocols.md}}
User-invocable
When the user types /connect-review, run this skill.
Step 0: Find the inputs
Locate the five inputs in the repo before reviewing anything. Use Glob and Grep; do not guess from file names alone.
| Input | How to find it |
|---|---|
| Contact flows | JSON exports: files containing "Version": "2019-10-30" and "Actions". IaC: aws_connect_contact_flow, aws_connect_hours_of_operation, aws_connect_instance (Terraform) or CfnContactFlow (CDK). |
| Lex V2 bot | Export zips unpacked to BotLocales/<locale>/Intents/*/Intent.json and Slots/*/Slot.json, or aws_lexv2models_bot, aws_lexv2models_intent, aws_lexv2models_slot, aws_lexv2models_bot_locale. |
| Lambda source | Handlers referenced by InvokeLambdaFunction in flows, by fulfillmentCodeHook / dialogCodeHook in intents, or aws_lambda_function / SAM AWS::Serverless::Function. |
| Prompts | Files under prompts/, names matching *prompt*, or a system/systemPrompt string near converse(, invoke_model(, InvokeModel, ConverseCommand. |
| Session schema | aws_dynamodb_table or AWS::DynamoDB::Table with a ttl/expiresAt attribute, plus any sessionAttributes writes in Lambda code. |
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.
- 5d ago First seen · 438 lines · 98 tokens per session scan A 75edbbcce301
connect-review is a skill published in the GitHub repository timurgaleev/vibestack (6 stars, last pushed 6d ago), licensed MIT. It adds 98 tokens to every session and 8,311 once invoked, about $0.0005 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 2026-09-03.
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