Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add bdfinst/agentic-dev-team/plugin install dev-teamWrote 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/bdfinst/agentic-dev-team/agent-eval)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/agent-eval"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/agent-eval/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/bdfinst/agentic-dev-team/agent-eval"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/agent-eval.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 | $0.00059 | $0.06577 |
| Opus 5.5 | $0.00024 | $0.02631 |
| Sonnet 5.5 | $0.00012 | $0.01315 |
| Haiku 4.5 | $0.00006 | $0.00658 |
Grade B, and why
agent-eval scanned grade B with 1 finding 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 24d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
"Cannot find eval fixtures. Expected at `.claude/evals/fixtures/`." How it starts
The opening of the file, as written. The whole thing — 582 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Eval
Role: orchestrator. This skill dispatches fixtures to agents and grades results — it does not review code itself.
You have been invoked with the /agent-eval skill. Run review agents
against eval fixtures and grade the results.
Monorepo-relative by design (#1637). Every scripts/eval_*.py/citation_lint.py call in this skill is intentionally bare, not a dangling-path defect: none of these scripts ship under plugins/dev-team/scripts/ (verified mechanically — see tests/repo/test_agent_implemented_by_resolves.py's INTENTIONAL_BARE_INVOCATION set), so no ${CLAUDE_PLUGIN_ROOT} path could ever resolve one. They operate on this repo's own evals/ corpus, explicitly not shipped — see repo CLAUDE.md's Repository Structure — and the repo-root eval runners (scripts/run-full-eval.sh, scripts/eval-changed.sh, scripts/run-ablation.sh) plus .github/workflows/agent-eval.yml all materialize evals/fixtures/evals/expected into .claude/evals/ via a $PWD-relative symlink before invoking this skill — meaningful only from this checkout's own root. The allowed-tools frontmatter above additionally hardcodes these bare strings as Bash permission patterns, which would stop matching if qualified.
Orchestrator constraints
- Do not review or design yourself. Delegate reviews to
/review-agentand advisory analysis to the skill under eval (e.g.test-design-advisor). Your job is dispatching and grading. - Grade deterministically. Compare agent JSON output against expected JSON using exact criteria (status match, count ranges, keyword checks). Do not apply judgment.
- Minimize context per agent. Pass only the fixture file to the
agent — not the expected results, not other fixtures, not prior
transcripts. One narrow exception: a
test-reviewdispatch also receives its fixture's owntest_review_mechanics.pymechanical pre-phase result (Step 3 below, #2169 follow-up) — computed from the fixture file itself, not from grading data, so it does not weaken this constraint's intent. - Track results. Save transcripts for saturation detection. Do not modify fixtures or expected files.
- Be concise. Output the report table and failure details. No narration of each fixture run — just the grades.
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.
- 24d ago Changed · +42 lines 5ab8124b2cc5
- 1mo ago First seen · 540 lines · 59 tokens per session scan B c55320b339e2
agent-eval is a skill published in the GitHub repository bdfinst/agentic-dev-team (295 stars, last pushed 4d ago), licensed MIT. It adds 59 tokens to every session and 6,577 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.