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/agentic-development/adev-plugin/evalnpx skills add agentic-development/adev-plugin --skill evalgit clone --depth 1 https://github.com/agentic-development/adev-pluginWhat 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 | $0.00047 | $0.04593 |
| Opus 5 | $0.00023 | $0.02296 |
| Sonnet 5 | $0.00009 | $0.00919 |
| Haiku 4.5 | $0.00005 | $0.00459 |
Grade A, and why
adev: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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graduated Evaluation Harness
Score implementation quality across four evaluation layers, producing a graduated quality score (0-100) rather than a single binary pass/fail. Complements /adev:validate with nuanced quality assessment.
Layer 3 is the exception to "graduated": its judged output is a table of per-criterion binary verdicts, aggregated into points only so the total stays comparable across runs. See Layer 3 for why.
Announce at start: "I'm using the adev:eval skill to run the evaluation harness."
Arguments
--spec <path>: evaluate the implementation of a specific spec (required)--layer <N>: run only a specific layer (1-4)--configure: interactive setup of eval configuration--rubric <path>: use a custom rubric for Layer 3, overriding the default. The file must carry the same top-level keys as the default rubric (see Layer 3).--no-infra: skip infrastructure preflight checks (user-only — the agent must never set this flag)
Prerequisites
.context-index/must be initialized./adev:validateshould have passed (eval builds on top of validation, not replaces it).- For Layer 2,
.context-index/samples/should have relevant golden samples. - Eval configuration lives in
.context-index/evals/config.yaml(generated by--configure).
Preflight: Infrastructure Verification
After verifying prerequisites, check whether the spec declares infra_requirements. If so, run the infrastructure preflight before proceeding to evaluation layers.
Layer-aware skip: If --layer 1 or --layer 2 is specified, skip the preflight step (no code execution against external systems in those layers).
--no-infra resolution: Read --no-infra flag from arguments. If not passed, check ADEV_NO_INFRA env var (only exact value 1 activates bypass). Read once at skill entry, convert to options.noInfra. The agent must never set --no-infra or ADEV_NO_INFRA autonomously — if preflight fails, report the failure and wait for user direction.
What ships with it
1 file 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.
- 2d ago First seen · 308 lines · 47 tokens per session scan A 2d38ff9342fb
adev:eval is a skill published in the GitHub repository agentic-development/adev-plugin (11 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 4,593 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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