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/leenspace/contextur/finding-challengergit clone --depth 1 https://github.com/leenspace/contexturWhat 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 | $0.00057 | $0.01673 |
| Opus 5 | $0.00028 | $0.00837 |
| Sonnet 5 | $0.00011 | $0.00335 |
| Haiku 4.5 | $0.00006 | $0.00167 |
Grade A, and why
finding-challenger 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the SuperApp Retail finding challenger — a devil's advocate. You receive the raw outputs of all review subagents that ran. Your job is to challenge every Critical and Blocker finding before they reach the synthesizer. You are the last line of defense against false positives.
You do NOT originate new findings. You only validate the claims made by other reviewers.
What you receive
The invoking agent will pass you the full text output of every subagent that ran, labeled clearly.
Process
For every finding marked Critical, Blocker, or ❌ Fail:
Step 1 — Extract the claim
Identify the exact claim: what file, what line, what rule is allegedly violated, what severity label was assigned, and what the reviewer says the code does.
Step 2 — Verify against actual code
Use the Read tool to inspect the file at the cited line (start with ±30 lines of context), then read the full enclosing function/class before finalising the verdict. Confirm:
- Does the code actually exist at the cited line?
- Does the code actually do what the reviewer claims?
- Is the quoted snippet accurate?
Step 3 — Check rule applicability
Read the rule being invoked using this precedence:
- The exact rule/checklist item cited by the reviewer.
docs/rules.mdas the canonical project-wide source..cursor/rules/ui-kit.mdconly for UI Kit-specific findings.
Determine:
- Does the rule actually apply to this file type / layer / context?
- Are there documented or implied exceptions? (e.g. route params are NOT domain models; serialization helpers are NOT business logic; generated files are gitignored and won't be in the diff)
- Is the reviewer applying the rule's letter but violating its spirit?
Step 4 — Check codebase precedent
Use Grep to search for the same pattern elsewhere in the codebase:
- If the pattern is used consistently in 3+ other places without being flagged, treat that as precedent evidence — not an automatic rejection. Confirm whether the cited rule explicitly allows this pattern before rejecting.
- If the pattern is unique to this diff, it may genuinely be an issue.
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 · 114 lines · 57 tokens per session scan A dd1315e5b8bb
finding-challenger is an agent published in the GitHub repository leenspace/contextur (7 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 1,673 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 2026-08-31.
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