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 Jamie-BitFlight/claude_skills --skill evaluate-sdlc-layersgit clone --depth 1 https://github.com/Jamie-BitFlight/claude_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/jamie-bitflight/claude_skills/evaluate-sdlc-layers)<a href="https://agentmods.dev/skills/jamie-bitflight/claude_skills/evaluate-sdlc-layers"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/evaluate-sdlc-layers/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/jamie-bitflight/claude_skills/evaluate-sdlc-layers"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/evaluate-sdlc-layers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 141 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 142 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00095 | $0.01574 |
| Opus 5 | $0.00048 | $0.00787 |
| Sonnet 5 | $0.00019 | $0.00315 |
| Haiku 4.5 | $0.00010 | $0.00157 |
Grade A, and why
evaluate-sdlc-layers 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate SDLC Layers
Systematically evaluate the SDLC Layer Separation Architecture implementation and support iterative improvement. Treats the implementation as first-pass until validated.
Arguments
--dry-run— Run all checks, produce report only. Do not apply fixes.--fix— After evaluation, apply safe fixes for broken references, missing metadata, or obvious gaps. Report what was changed.- (no args) — Evaluate and produce report; offer to fix or delegate fixes.
Evaluation Checklist
Run each check and record PASS / FAIL / SKIP with evidence.
1. Cross-Reference Validation
For each linked path in plugins/development-harness/docs/sdlc-layers/ and related docs:
-
sam-definition.md— exists atplugins/development-harness/skills/work-backlog-item/references/sam-definition.md -
plugins/development-harness/CLAUDE.md— exists -
stateless-agent-methodology/research/arl/PROVENANCE.md— exists (sibling repo or configured path) - Layer 0 docs →
TASK_FILE_FORMAT.md— exists atplugins/development-harness/docs/TASK_FILE_FORMAT.md - Layer 1 →
language-manifest-schema.md,role-resolution-protocol.md— exist in development-harness - Layer 2 →
plugins/development-harness/docs/sdlc-layers/layer-2/— exists with README, schema, pilot profiles - Layer-0 redirect stubs (
artifact-conventions.md,task-file-format.md,sam-pipeline.md,arl-touchpoints.md) contain redirect pointers to canonical locations. Validate each redirect target exists.
Evidence: List each path checked and result (exists / 404 / wrong content).
2. Doc Completeness
- Layer 0 content files (6): README, rt-ica-gate, verification-protocol, evidence-discipline, orchestrator-discipline, context-fit-complexity
- Layer 0 redirect stubs (4): sam-pipeline, arl-touchpoints, artifact-conventions, task-file-format — each must contain a redirect pointing to its canonical skill reference location
- Layer 1: All 6 docs present (README, layer-1-overview, language-manifest-template, linting-discovery-protocol, workflow-pattern-taxonomy, harness-role-mapping)
- Layer 2: README, layer-2-overview, stack-profile-schema, stack-profile-template; pilot profiles python-fastapi, python-cli
- ARL: arl-meta-layer.md, arl-human-probing-design.md
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.
- 9d ago First seen · 143 lines · 95 tokens per session scan A 5875c4106620
evaluate-sdlc-layers is a skill published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed yesterday), licensed MIT. It adds 95 tokens to every session and 1,574 once invoked, about $0.0005 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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