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 skills/harbor-framework/harbor/rewardkitnpx skills add harbor-framework/harbor --skill rewardkitgit clone --depth 1 https://github.com/harbor-framework/harborWhat 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 | $0.00046 | $0.02704 |
| Opus 5 | $0.00023 | $0.01352 |
| Sonnet 5 | $0.00009 | $0.00541 |
| Haiku 4.5 | $0.00005 | $0.00270 |
Grade A, and why
rewardkit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help the user write task verifiers with Reward Kit. Reward Kit is a lightweight Python package that turns a directory of criteria files into a reward score. Each criterion is a Python function call or a TOML judge file; folders become separate rewards.
Setup in a Harbor task
Put criteria alongside test.sh in the task's tests/ directory:
tests/
├── test.sh
├── checks.py # programmatic criteria
└── judge.toml # optional LLM/agent judge
tests/test.sh:
#!/bin/bash
uvx --from 'harbor-rewardkit==0.1.*' rewardkit /tests
This runs all criteria in /tests/ against the workspace at /app and writes
/logs/verifier/reward.json. Defaults match Harbor's conventions — no extra config needed.
If judge criteria need API keys, pass them through task.toml:
[verifier.env]
ANTHROPIC_API_KEY = "${ANTHROPIC_API_KEY}"
Ask whether Reward Kit should run in the agent's shared environment or in a separate verifier environment. Prefer a separate verifier environment when judge prompts, grading dependencies, API keys, or clean-room checks should not be available to the agent:
[environment]
network_mode = "no-network" # Agent env baseline — offline during agent.run()
[verifier]
environment_mode = "separate"
[verifier.environment]
network_mode = "public" # Verifier env baseline — LLM judge API calls
docker_image = "python:3.12-slim"
In shared mode, the verifier runs in the agent container and inherits
[environment].network_mode. Put [verifier].network_mode only when verify()
needs different network access than the agent phase (a phase override, not a
baseline). If agent and verifier need different baselines without runtime
switching, use environment_mode = "separate" and set
[verifier.environment].network_mode.
Judge criteria that call external APIs need a public baseline or allowlist on
the verifier environment. Programmatic checks that only read local files can use
no-network.
In separate mode, tests/ is the verifier image build context and must provide
/tests/test.sh at runtime; Harbor does not upload tests/ into the running
verifier container.
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 · 313 lines · 46 tokens per session scan A 22d9ec44ca74
rewardkit is a skill published in the GitHub repository harbor-framework/harbor (4,782 stars, last pushed 2d ago), licensed Apache-2.0. It adds 46 tokens to every session and 2,704 once invoked, about $0.0002 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 2026-08-30.
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