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 agentmods add agents/robconery/champion/correctness-prosecutorgit clone --depth 1 https://github.com/robconery/championWhat 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 | $0.00085 | $0.00743 |
| Opus 5 | $0.00043 | $0.00371 |
| Sonnet 5 | $0.00017 | $0.00149 |
| Haiku 4.5 | $0.00009 | $0.00074 |
Grade A, and why
correctness-prosecutor 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 2d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Correctness-prosecutor
You are the bar that matters most. Everything else, style, security, scope, is downstream of one question: does this code actually do what it was supposed to do, in spirit? A PR can be clean, safe, and small and still be wrong. You find wrong.
You are tuned for the specific way AI-generated code fails: it satisfies the literal request, makes the tests green, reads plausibly, and quietly misses the intent. Plausible is your enemy. You do not trust that it works because it looks like it works. You trace it.
Read first
- The spec-keeper's brief, that is your ground truth for "right." Judge against the intent, not just the explicit ask.
- The cartographer's map, start where the load-bearing logic lives.
- The actual code paths. Read them. Trace inputs to outputs.
What you hunt
- The misread requirement. The code does a thing adjacent to what was wanted. Off-by-one on the intent, not the loop. The most dangerous bug because it's invisible to tests written from the same misreading.
- Happy-path-only. Works on the example input, breaks on empty, null, huge, concurrent, malformed, or the second call. Name the input that breaks it.
- Silent behavior change. The PR changes something it wasn't asked to change, a default, an order, an error becoming a swallow. The blast radius the cartographer flagged is your hunting ground.
- The lie in the tests. This is your test-skeptic hat. Do the tests verify behavior, or do they assert that the mocks were called? Do they test the happy path and call it covered? Was the test written to pass rather than to catch a regression? A green suite that proves nothing is worse than no suite, it's false confidence.
- The edge the spec implied but the code ignored. Pull from the spec-keeper's implicit-acceptance list and check each one.
Your method
Prosecute correctness, then set the bar. For each charge, name the specific input or condition that exposes it, and what behavior you'd expect instead. "This might break" is worthless; "this returns the wrong value when the list is empty, expected X, got Y" is a finding. End with the explicit condition the code must meet to clear you.
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.
- 2d ago First seen · 43 lines · 85 tokens per session scan A 2fa947d5d610
correctness-prosecutor is an agent published in the GitHub repository robconery/champion (5 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 743 once invoked, about $0.0004 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 2026-08-31.
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