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/brain-bootstrap/claude-code-brain-bootstrap/cross-layer-checknpx skills add brain-bootstrap/claude-code-brain-bootstrap --skill cross-layer-checkgit clone --depth 1 https://github.com/brain-bootstrap/claude-code-brain-bootstrapWhat 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 | $0.00042 | $0.00394 |
| Opus 5 | $0.00021 | $0.00197 |
| Sonnet 5 | $0.00008 | $0.00079 |
| Haiku 4.5 | $0.00004 | $0.00039 |
Grade A, and why
cross-layer-check 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.
What it actually says
Cross-Layer Consistency Check
Verify that a symbol (field name, enum value, status code) exists across all layers of the monorepo.
Usage
Run the bundled script:
bash ${CLAUDE_SKILL_DIR}/scripts/cross-layer-check.sh $ARGUMENTS
Examples
# Check a field name (substring match)
bash ${CLAUDE_SKILL_DIR}/scripts/cross-layer-check.sh invoiceNumber
# Check an enum value (exact word match)
bash ${CLAUDE_SKILL_DIR}/scripts/cross-layer-check.sh PAYMENT_SENT --exact
# Check a new DTO field
bash ${CLAUDE_SKILL_DIR}/scripts/cross-layer-check.sh paymentDueDate
What It Checks
The script searches across all source directories in order, reporting hits per layer:
- Backend / API source code
- Frontend source code
- Shared packages / libraries
- Test files
- Migrations / database schemas
- Configuration and documentation
Interpreting Results
- ✅ Layer: N hits — Symbol found in this layer
- ❌ Layer: no hits — Symbol NOT found — possible gap
- ⚠️ WARNING (< 3 layers) — Likely incomplete implementation
When to Use
- After adding a new field to a DTO
- After adding a new status code or enum value
- During MR review (Review Protocol point #2: cross-layer consistency)
- When the
/reviewcommand flags potential cross-layer gaps
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.
- 3d ago First seen · 56 lines · 42 tokens per session scan A 2b7c8c84a11c
cross-layer-check is a skill published in the GitHub repository brain-bootstrap/claude-code-brain-bootstrap (11 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 394 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.
Other skills, from other repositories
implement
TRIGGER when: user asks to implement, fix, build, or work on something — whether from a docs/wip plan OR a standalone task (bug fix, GitHub issue, one-off change). Examples: "work on task 1", "fix this bug", "implement feature X from the issue". Provides structured execution with profile detection, dependency…
review-spec
Use after implementing tasks or mid-feature to verify code matches design docs and ensure they are in sync. Detects spec deviations, missing implementations, doc inconsistencies, and outdated docs in design and implementation documentation.
chain-of-verification
Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning.
review-code
Code review of current git changes with an expert senior-engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements. Use when performing code reviews.
review-design
Review design, implementation, and task documents produced by design. Evaluates document quality, internal consistency, and technical soundness. Use after design completes and before starting implement.
design
Use in pre-implementation (idea-to-design) stages to understand spec/requirements and create a correct implementation plan before writing actual code. Turns ideas into a fully-formed PRD/design/specification and implementation-plan. Creates design docs and task lists in docs/wip/.