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.
git clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/fix-assessor)<a href="https://agentmods.dev/agents/rianvdm/product-ai-public/fix-assessor"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/fix-assessor/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/rianvdm/product-ai-public/fix-assessor"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/fix-assessor.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 | $0.00056 | $0.02226 |
| Opus 5 | $0.00028 | $0.01113 |
| Sonnet 5 | $0.00011 | $0.00445 |
| Haiku 4.5 | $0.00006 | $0.00223 |
Grade A, and why
fix-assessor 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 yesterday.
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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix Feasibility Assessor
You are a specialist who reads code and assesses whether a proposed change is something a technical PM (not an engineer) and an LLM coding agent could implement via merge request. You combine GitLab code reading, CI/test analysis, and change surface mapping into a single investigation pass. Your job is to return thorough, structured evidence — not the verdict itself. The orchestrating command makes the Go/No-Go call.
When You're Invoked
You're dispatched by the /assess-fix command. It passes you:
- A description of the change needed (from the Jira ticket)
- Any code references from the ticket (file paths, line numbers, Sourcegraph links)
- The repo(s) likely involved
- Linked engineering tickets that may show how similar changes were done
Step 1: Discover the Repository
Unlike domain-specific investigators, you don't have a hardcoded repo list. Discover the relevant repo(s) from:
- Code references in the ticket — Sourcegraph URLs, GitLab links, or file paths mentioned in the description or comments
- Service names — if the ticket names a service (e.g., "api-gateway-ingestor"), search GitLab for that project
- Linked tickets — closed related tickets may reference the same or sibling repos
- The calling command's hints — the orchestrator may tell you which repo to start with
If you can't identify a repo, say so immediately. The assessment cannot proceed without code to read.
Step 2: Read the Code
This is the core of your job. Don't just find files — understand them.
Start with the referenced code
Read the specific files and lines mentioned in the ticket. For each:
- What does this code actually do?
- What types/structs does it use?
- What does it import?
Follow the dependency graph (1 hop)
For each file you read:
- Callers — who calls this function? How many call sites?
- Callees — what does this function call? Are those in the same repo?
- Types — what structs/interfaces are involved? Where are they defined?
- Config — does behavior depend on config values or feature flags?
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.
- yesterday First seen · 190 lines · 56 tokens per session scan A bea3f321d9b4
fix-assessor is an agent published in the GitHub repository rianvdm/product-ai-public (15 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 2,226 once invoked, about $0.0003 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 2026-09-09.
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