Borrowing it
Nothing to install: this file belongs to padmarajnidagundi/Playwright-AI-Agent-POM-MCP-Server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/padmarajnidagundi/Playwright-AI-Agent-POM-MCP-Server/main/.github/chatmodes/🎭 planner.chatmode.mdgit clone --depth 1 https://github.com/padmarajnidagundi/Playwright-AI-Agent-POM-MCP-ServerWrote 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/agents/padmarajnidagundi/playwright-ai-agent-pom-mcp-server/planner)<a href="https://agentmods.dev/agents/padmarajnidagundi/playwright-ai-agent-pom-mcp-server/planner"><img src="https://agentmods.dev/badge/agents/padmarajnidagundi/playwright-ai-agent-pom-mcp-server/planner/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/agents/padmarajnidagundi/playwright-ai-agent-pom-mcp-server/planner"><img src="https://agentmods.dev/badge/agents/padmarajnidagundi/playwright-ai-agent-pom-mcp-server/planner.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 | $0.00018 | $0.01027 |
| Opus 5.5 | $0.00007 | $0.00411 |
| Sonnet 5.5 | $0.00004 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
🎭 planner 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 1mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 110 lines · 18 tokens per session scan A d1b768314491
🎭 planner is an agent published in the GitHub repository padmarajnidagundi/Playwright-AI-Agent-POM-MCP-Server (39 stars, last pushed 4d ago), with no licence file. It adds 18 tokens to every session and 1,027 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 2026-09-14.
Other agents, from other repositories
test-case-result-validator
Compares old vs new instruction outputs against original codebase, scores 8 quality categories, emits pass/fail JSON verdict for CI/CD validation pipeline.
step-generator
Generate test steps from natural language descriptions. Use when: (1) User describes a test scenario in plain English, (2) Complex UI interactions need step-by-step breakdown, (3) User wants to add steps but doesn't know the exact format.
test-analyzer
Deep analysis of E2E test failures and results. Use when: (1) A test has failed and needs root cause analysis, (2) User wants to understand why a step failed, (3) Complex multi-step failures need investigation.
FAI Deterministic Agent Reviewer
Deterministic Agent reviewer — reproducibility testing, guardrail completeness audit, anti-sycophancy verification, schema validation review, and confidence calibration checks.
timps_red_team_agent
Plan and execute an OWASP-style red-team engagement: threat model, adversarial test cases, payloads, runnable exploit harness, and a remediation checklist. Use the timpsredteamagent MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.
timps_test_data_agent
Generate realistic, edge-case-covering seed / fixture data for any JSON schema, SQL DDL, or plain-text description. Use the timpstestdataagent MCP tool to perform this task. Do not answer directly — delegate to this sub-agent.