Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Borda/AI-Rignpx agentmods add skills/borda/ai-rig/challenge-resolveWrote 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/skills/borda/ai-rig/challenge-resolve)<a href="https://agentmods.dev/skills/borda/ai-rig/challenge-resolve"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/challenge-resolve/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/skills/borda/ai-rig/challenge-resolve"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/challenge-resolve.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 | $0.00047 | $0.01970 |
| Opus 5.5 | $0.00019 | $0.00788 |
| Sonnet 5 | $0.00009 | $0.00394 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
challenge-resolve 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before asking, read User Questions.
Challenge and Resolve
Read, apply ../../shared/adversarial-loop.md before dispatch or edits. That shared procedure owns the algorithm; this entrypoint owns Codex artifacts, the closing gate. Read evidence-contract.md before source capture or reviewer dispatch. Also read ../../shared/native-skill-contract.md and ../../shared/specialist-orchestration.md for authority, recurrence, reviewer admission, evidence limits.
Input Schema
{
"goal": "required review-and-fix objective",
"scope_files": ["bounded files or diff scope"],
"specification": "required behavior or acceptance criteria",
"symptom": "reported failure, or explicit preventive review",
"caller_run": "optional existing workflow run to resume",
"done_when": "independent final review is clean and normal checks pass"
}
Use default max 3 review rounds, including initial W_0. In-scope feasible fixes are authorized by a review-and-fix request; a structural finding does not authorize scope expansion, public API changes, commits, installs, network access or publication. A caller's stricter scope or admission still applies.
Workflow
01: Establish scope and evidence ownership
Read ../../shared/helper-cli-contract.md; create a run with create_run.py --skill challenge-resolve. Record caller_run when supplied, without overwriting its artifacts. Retain baseline source and acceptance evidence. Write loop-report.md with Scope, Rounds, Findings, Recovery, and Verification sections. Identify implementation author and allowed reviewer route before dispatch.
02: Run the shared bounded procedure
Follow the shared five-step procedure: challenge and collect findings; report the old/new table once after each validated round; resolve every feasible in-scope finding; escalate unresolved security, critical or high; repeat until clean, three rounds, plateau or a non-converging score. Retain loop-ledger.json, loop-actions.json, round-<index>.diff, current snapshot current.diff and each independent report. Read adversarial_loop.py --help, validate the ledger after each round, and invoke adversarial_loop.py --ledger <run-directory>/loop-ledger.json --progress only after a newly completed challenge, before the next fix, review or stop. Show its full cumulative stderr table with Iteration | Critical | High | Medium | Low | Nits | Weighted score and literal old + new cells. Never show a placeholder progress table before a validated round or repeat the table in unrelated status updates. Keep the final canonical Results table unchanged. Structural or repeated findings alone do not stop an authorized feasible fix; scope, authority, recurrence, plateau, missing independence, stale evidence and exhausted rounds retain their shared stop rules. A fixed-pending-verification finding remains open. After resolution/escalation, bind every open finding to an action and run adversarial_loop.py --ledger <run-directory>/loop-ledger.json --actions <run-directory>/loop-actions.json before another challenge or final handoff. The helper validates recorded actions, not their truth; the parent must inspect feasibility and fix evidence.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 79 lines · 47 tokens per session scan A 2a2efa72820b
challenge-resolve is a skill published in the GitHub repository Borda/AI-Rig (28 stars, last pushed 3d ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,970 once invoked, about $0.0002 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 2026-09-24.
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