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/brolag/neural-claude-code/specnpx skills add brolag/neural-claude-code --skill specgit clone --depth 1 https://github.com/brolag/neural-claude-codeWhat 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 | $0.00069 | $0.01229 |
| Opus 5 | $0.00034 | $0.00615 |
| Sonnet 5 | $0.00014 | $0.00246 |
| Haiku 4.5 | $0.00007 | $0.00123 |
Grade A, and why
spec 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec
Turn intent and repository evidence into an approvable implementation contract. Never write implementation code.
This skill is the Claude Code port of the Neural Codex spec gate. Same contract, slash invocation.
1. Right-size and research
Skip ceremony for an obvious change under roughly 25 lines with no new interface, ambiguity, or risk.
Otherwise:
- Find the newest related
plans/**/unknowns-map.mdand read it first. - Read repository guidance, the relevant code, tests, documentation, and useful history.
- Reuse established patterns before proposing new ones.
- If discovery was skipped, record that fact and clarify only architecture-changing ambiguity (AskUserQuestion, at most 3).
Treat resolved discovery decisions as inputs. Preserve unresolved blockers instead of guessing around them.
If the user cannot yet say what "good" looks like, route to /discover instead of guessing a plan.
2. Lock the contract
Declare every introduced or changed interface before implementation:
name(parameters) -> result [new|adapt|reuse] -> path
Interfaces include functions, types, commands, files, routes, events, schemas, and durable artifact shapes. A new interface that duplicates an existing one is a planning defect.
For every relevant security or trust boundary, add an @invariant with its CWE and concrete mitigation. Cover path handling, secret exposure, command execution, authorization, destructive side effects, and validation integrity when applicable.
Keep every invariant testable at its boundary. Do not treat a general security promise as a replacement for a concrete mitigation and verification command.
3. Decompose and route
Give every subtask a stable ID and a [tier:] model-routing tag:
cheap: mechanical edits, boilerplate, and narrow testsmid: normal implementation requiring local judgmenthard: architecture, security, ambiguity, or tricky debuggingbatch: long-running multi-step or operational work
Add [needs: S1, S2] only for real dependencies. No dependency tag means the work may execute independently. Validate that every referenced ID exists and that the graph is acyclic.
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 · 124 lines · 69 tokens per session scan A f34a9feb142f
spec is a skill published in the GitHub repository brolag/neural-claude-code (11 stars, last pushed 13d ago), licensed MIT. It adds 69 tokens to every session and 1,229 once invoked, about $0.0003 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
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…