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 commands/mckruz/claude-code-sdlc/sdlc-evalsgit clone --depth 1 https://github.com/MCKRUZ/claude-code-sdlcWrote 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/commands/mckruz/claude-code-sdlc/sdlc-evals)<a href="https://agentmods.dev/commands/mckruz/claude-code-sdlc/sdlc-evals"><img src="https://agentmods.dev/badge/commands/mckruz/claude-code-sdlc/sdlc-evals.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 | $0.00000 | $0.01235 |
| Opus 5 | $0.00000 | $0.00617 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
sdlc-evals 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 3d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sdlc-evals — Author the Versioned Golden Set for an LLM-Powered Spec
Author the golden set — the acceptance criteria for probabilistic behavior — for a spec on an
llm_powered channel. Bizreq owns the scenarios, Data owns the data; the two are authored
collaboratively. This command is a thin wrapper around the existing eval-builder harness skill:
it adds no new script, authors no CI YAML, and writes a versioned golden-set.yaml next to the
spec. Interview-driven like /sdlc-coach at the collaboration and calibration steps — it proposes; a
named human decides. Works inside an SDLC project or standalone.
Instructions
-
Resolve context:
- Workflow mode (default):
.sdlc/state.yamlexists. The repo root is the directory containing.sdlc/; read the target spec and itsgolden-scenarios.md(SCEN-NN) anddata-contract.md. - Standalone mode (
--repo <path>, or no.sdlc/found): operate on the given repo with provisional context; take scenarios/data from the paths given or asked for.
- Workflow mode (default):
-
Confirm the channel is
llm_powered: Read the target spec'schannel:field andchannels/<channel>.yaml. Ifllm_powered: true, evals are required acceptance criteria — the descriptor'seval_hooksname which dimensions become golden-set cases. Ifllm_poweredis false or the spec has no channel, tell the human evals are advisory here and stop unless--forceis given. -
Gather the inputs (collaborative): Assemble the cases from Bizreq's
SCEN-NNgolden scenarios (the behaviors that must hold) and Data's representative data (real inputs from the manual pre-release checks and the bug/support queue, not invented cases), plus the descriptor'seval_hooks. Ask focused questions to fill gaps — which failures matter, what the reference answer is for each. -
Wrap the
eval-builderskill (no new script): Follow theeval-builderskill's procedure (harness/skills/eval-builder/SKILL.md, or the target repo's installed.claude/skills/eval-builder) — start from real failures small (20–50 tasks), pick graders deterministic-first (state_check/transcript_constraintover anllm_rubric, and grade the output, not the trajectory), compose multidimensional success where needed, and write the versionedgolden-set.yamlnext to the spec using the skill's shape. The skill owns the output path and the template — do not hardcode either here (it writes undereval-datasets/specs/<feature>/).
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.
- 3d ago First seen · 77 lines · 0 tokens per session scan A bf527c4f8ac8
sdlc-evals is a command published in the GitHub repository MCKRUZ/claude-code-sdlc (4 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,235 tokens. 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 2026-08-31.
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