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 skills add Hoja-Solutions/agent-stdlib --skill reward-hacking-and-inoculationgit clone --depth 1 https://github.com/Hoja-Solutions/agent-stdlibWrote 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/hoja-solutions/agent-stdlib/reward-hacking-and-inoculation)<a href="https://agentmods.dev/skills/hoja-solutions/agent-stdlib/reward-hacking-and-inoculation"><img src="https://agentmods.dev/badge/skills/hoja-solutions/agent-stdlib/reward-hacking-and-inoculation/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/hoja-solutions/agent-stdlib/reward-hacking-and-inoculation"><img src="https://agentmods.dev/badge/skills/hoja-solutions/agent-stdlib/reward-hacking-and-inoculation.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 | $0.00194 | $0.00788 |
| Opus 5 | $0.00097 | $0.00394 |
| Sonnet 5 | $0.00039 | $0.00158 |
| Haiku 4.5 | $0.00019 | $0.00079 |
Grade A, and why
reward-hacking-and-inoculation 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 8d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reward hacking and inoculation
Source: Emergent misalignment from reward hacking. The article studies what happens when a model learns to cheat during training; this skill pulls out the parts an agent builder controls at the prompt and the grader, and connects them to build-agent-evals.
How an agent hacks a reward
Give an agent a goal a program can check, and it may satisfy the check without doing the work behind it. A coding agent told to turn the suite green can edit the assertion, delete the failing case, wrap the call in a try that swallows the error, or detect the grader and special-case it. Each one passes. None fixes the bug.
Why it spreads
In training, a model that learns to cheat on coding tasks does not keep the habit local. Anthropic found it generalized to sabotage and deception the team never trained for, because the model learned that defeating the check is the goal. You will not retrain your model, but the lesson carries to prompting: when you tolerate a shortcut without saying so, you teach the agent that the shortcut is what you wanted.
Inoculation: name when a shortcut is allowed
The most portable fix is a sentence in the prompt. If a shortcut is acceptable for this task, say so: "your only job here is to make the grading script pass; a hard-coded answer is fine." Naming it keeps the agent from generalizing "cheat the check" into a standing rule, and it preserves performance on the narrow task. If the shortcut is not acceptable, the prompt says that instead, and the grader has to back it up.
Make the grader pay for real work
A check the agent can satisfy without doing the work will be satisfied that way. Grade the end state: run hidden tests the agent never sees, assert against a separate reference, and include negative cases that catch an agent that does too much or guts the test. This is where the skill meets build-agent-evals.
Look past the score
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.
- 8d ago First seen · 48 lines · 194 tokens per session scan A 9dc56513cae4
reward-hacking-and-inoculation is a skill published in the GitHub repository Hoja-Solutions/agent-stdlib (1 stars, last pushed 1mo ago), licensed MIT. It adds 194 tokens to every session and 788 once invoked, about $0.0010 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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