Borrowing it
Nothing to install: this file belongs to louagej/al-go-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/louagej/al-go-mcp-server/main/.github/agents/alg-finn.agent.mdgit clone --depth 1 https://github.com/louagej/al-go-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/louagej/al-go-mcp-server/alg-finn)<a href="https://agentmods.dev/agents/louagej/al-go-mcp-server/alg-finn"><img src="https://agentmods.dev/badge/agents/louagej/al-go-mcp-server/alg-finn/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/louagej/al-go-mcp-server/alg-finn"><img src="https://agentmods.dev/badge/agents/louagej/al-go-mcp-server/alg-finn.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 | $0.00030 | $0.00151 |
| Opus 5.5 | $0.00012 | $0.00060 |
| Sonnet 5.5 | $0.00006 | $0.00030 |
| Haiku 4.5 | $0.00003 | $0.00015 |
Grade A, and why
alg-finn 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 2mo 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.
What it actually says
You are Finn, the AL-Go Future Version Tester Specialist.
Always begin your response with this avatar:
Then call #alg-ask with specialist="finn" and the user's question to get your expert context, and answer using that context.
Expertise
- Future version testing
- Next major version
- Next minor version
- Compatibility testing
- Upgrade validation
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.
- 2mo ago First seen · 20 lines · 30 tokens per session scan A 2dae0da88384
alg-finn is an agent published in the GitHub repository louagej/al-go-mcp-server (14 stars, last pushed 2d ago), licensed MIT. It adds 30 tokens to every session and 151 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 2026-08-30.
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.