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 markoblogo/abvx-agent-skills --skill bounded-evaluationgit clone --depth 1 https://github.com/markoblogo/abvx-agent-skillsWrote 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/markoblogo/abvx-agent-skills/bounded-evaluation)<a href="https://agentmods.dev/skills/markoblogo/abvx-agent-skills/bounded-evaluation"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/bounded-evaluation.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.00558 |
| Opus 5 | $0.00028 | $0.00279 |
| Sonnet 5 | $0.00011 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
bounded-evaluation 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 7d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bounded Evaluation
Use this skill when a change needs a small, explicit evaluation gate.
Use When
- reviewing skill changes, AGENTS.md rules, prompt playbooks, or SET bundle changes;
- comparing two agent outputs or proposals;
- using an LLM judge where bias or weak rubrics could mislead;
- creating activation tests, regression cases, or ship gates for agent behavior;
- deciding whether a repeated workflow improved after a bounded edit.
Evaluation Contract
A bounded eval must state:
- target artifact or behavior;
- candidate outputs or versions;
- rubric dimensions and weights, if any;
- pass/fail threshold;
- fixtures or scenarios;
- judge type: deterministic check, human review, LLM judge, or hybrid;
- known bias risks and mitigation.
Pairwise And Judge Rules
- Compare A/B outputs against the same rubric.
- Swap candidate order when using an LLM judge to detect position bias.
- Require reasons before scores, but treat reasons as evidence to inspect, not truth.
- Mark the eval invalid when swapped order changes the winner without a defensible reason.
- Prefer deterministic checks for syntax, schema, file existence, and command success.
- Keep human approval for high-stakes or ambiguous decisions.
Workflow
- Define the smallest behavior being evaluated.
- Pick 1-5 representative fixtures or scenarios.
- Choose deterministic checks before LLM judging.
- If using pairwise judging, run both candidate orders.
- Record results, invalidations, and decision.
- Feed accepted/rejected outcomes back into the relevant ledger.
Output Shape
Use:
- eval target;
- fixtures;
- rubric;
- judge type;
- results;
- bias checks;
- decision:
accept,reject,revise, orinvalid; - follow-up artifact or ledger update.
Guardrails
- Do not publish benchmark claims without committed fixtures, arms, model, host, run count, and captured artifacts.
- Do not let an LLM judge approve its own rubric changes.
- Do not use pairwise preference as proof of correctness.
- Do not hide invalid or inconsistent judge results.
What ships with it
2 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.
- 7d ago First seen · 75 lines · 55 tokens per session scan A 3e68e07e625c
bounded-evaluation is a skill published in the GitHub repository markoblogo/abvx-agent-skills (15 stars, last pushed 13d ago), licensed MIT. It adds 55 tokens to every session and 558 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 2026-08-30.
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