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/loerei/chronicle-mcp/codebase-designnpx skills add loerei/chronicle-mcp --skill codebase-designgit clone --depth 1 https://github.com/loerei/chronicle-mcpWhat 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 | $0.00019 | $0.00807 |
| Opus 5 | $0.00010 | $0.00404 |
| Sonnet 5 | $0.00004 | $0.00161 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
codebase-design 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Design
Design deep modules: maximize behavior behind a small interface, placed at a clean seam, testable through that interface.
Directives
- Strict Terminology: MUST use glossary terms (module, interface, depth, seam, adapter, leverage, locality); NEVER substitute component, service, API, boundary.
- Depth as Leverage: Depth is measured by leverage at the interface (behavior per unit of learned surface), NOT line-of-code ratios.
- The Deletion Test: If deleting a module makes complexity vanish, it was a shallow pass-through; if complexity reappears across callers, it was earning its keep.
- Interface as Test Surface: Callers and tests MUST cross the same seam. If tests must reach past the interface, the module is incorrectly shaped.
- Seam Justification: 1 adapter = hypothetical seam (unnecessary indirection); 2+ adapters = real seam (e.g., production + test stand-in).
Glossary
- Module: Anything with an interface and an implementation (function, class, package, slice). Avoid: unit, component, service.
- Interface: Everything a caller must know to use the module correctly (types, invariants, ordering, errors, performance). Avoid: API, signature.
- Implementation: Internal body of code. Distinct from Adapter (role at seam).
- Depth: Leverage at interface. Deep = small interface + large behavior; Shallow = large interface + thin pass-through.
- Seam: Location where an interface lives and behavior can be altered without editing callers. Avoid: boundary.
- Adapter: Concrete implementation satisfying an interface at a seam.
- Leverage: Capability gain per unit of interface learned (1 implementation serves N callers and M tests).
- Locality: Concentration of change, bugs, and verification in one place.
Deep vs. Shallow Modules
┌─────────────────────┐ ┌─────────────────────────────────┐
│ Small Interface │ │ Large Interface │
├─────────────────────┤ ├─────────────────────────────────┤
│ │ │ Thin Implementation (Avoid) │
│ Deep Implementation│ └─────────────────────────────────┘
│ │
└─────────────────────┘
What ships with it
3 files 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 · 84 lines · 19 tokens per session scan A 85da5a0c201a
codebase-design is a skill published in the GitHub repository loerei/chronicle-mcp (0 stars, last pushed 8d ago), licensed MIT. It adds 19 tokens to every session and 807 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…