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/boshu2/agentops/skill-evalnpx skills add boshu2/agentops --skill skill-evalgit clone --depth 1 https://github.com/boshu2/agentopsWhat 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 | $0.00061 | $0.03071 |
| Opus 5 | $0.00030 | $0.01536 |
| Sonnet 5 | $0.00012 | $0.00614 |
| Haiku 4.5 | $0.00006 | $0.00307 |
Grade A, and why
skill-eval 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/skill-eval
Author one behavioral probe for one skill, at the cheapest tier that can still
separate the arms, and report the verdict honestly. A probe measures
behavior-change — did loading the skill change what the agent did — never
quality-uplift. This skill authors and tiers probes. scripts/probe-skill.sh
runs them.
Insight: when a probe returns INERT because the control arm already aces the scenario, the measurement failed, not the skill. Weakening the producer is one escape and it costs realism. The cheaper escape is to plant the defect: build a scenario containing exactly one flaw the discipline catches and a skim does not, then grade whether the agent acted on it. Signal you manufacture is signal you can reproduce.
The failure mode this exists to prevent: a skill catalog whose tier badges are editorial. A skill nobody measured is a skill nobody can defend, and re-running a saturated scenario at a lower effort level produces more rows in the ledger without producing more knowledge.
Modes
| Trigger phrases | Mode | Entry point |
|---|---|---|
| "measure this skill", "does this skill do anything" | author tier 1 (quiz probe) | evals/skill-probes/<id>/ |
| "the control arm aces it", "harden this scenario" | author tier 2 (seeded-defect probe) | references/seeding.md |
| "the probe came back INERT" | diagnose headroom | gate skill.probe-headroom |
| "run the probes" | run a tier | scripts/probe-skill.sh |
Inputs
Required: the skill slug, and one sentence naming the action the skill should cause — a tool call made, an artifact written, a question raised, a sequence followed. If the sentence names a belief instead of an action ("understands that…", "considers…"), stop: that is not probeable, and rewriting it as an action is the actual work.
Optional: an existing probe id to harden.
Non-goals. This skill does not score output quality, rank skills, claim a
skill is good, or gate a release. It does not run claude -p. It does not
generalize from N=2 — small N is directional and every artifact it produces says
so.
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 · 234 lines · 61 tokens per session scan A 2ca1e9d2a9d0
skill-eval is a skill published in the GitHub repository boshu2/agentops (431 stars, last pushed 4d ago), licensed Apache-2.0. It adds 61 tokens to every session and 3,071 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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