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/shdennlin/agent-plugins/specnpx skills add shdennlin/agent-plugins --skill specgit clone --depth 1 https://github.com/shdennlin/agent-pluginsWhat 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 | $0.00546 |
| Opus 5 | $0.00034 | $0.00273 |
| Sonnet 5 | $0.00014 | $0.00109 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
reviewer: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 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec Review
Review feature specifications, proposals, designs, or task lists before implementation starts.
Every invocation fans out across all built-in review angles by default (bound engineering — first pass extracts maximum signal). Flags only narrow that set or layer a fix loop on top.
Usage
Provide the paths to spec files or folders to review:
$spec docs/plans/auth-flow/
$spec proposal.md spec.md tasks.md
With iterative fix loop (agent decides which issues need your call):
$spec docs/plans/auth-flow/ --fix
$spec docs/plan/ --fix -n 5
Narrow to specific angles (still parallel, just fewer):
$spec docs/plan/ --angles "scope,tasks"
Skip codebase exploration:
$spec docs/plan/ --no-explore
If no paths are given, ask which files to review.
Process
- Read the spec files provided
- Explore the codebase for relevant context (code-explorer agent, unless
--no-explore) - Read
.claude/reviewer/rules.yamlat the git root if it exists (harvested project rules); pass its content asproject_rules(empty if absent) - Dispatch the spec-orchestrator agent with codebase context and
fix_enabledderived from--fix - Report findings back
Agent Dispatch
Before dispatching the orchestrator, the code-explorer agent scans the codebase for relevant context based on spec content. Pass --no-explore to skip this step.
Always dispatch reviewer:spec-orchestrator with parameters (paths, fix_enabled, max_iterations, angles, codebase_context, review-angles template content, log_script_path (resolve ${CLAUDE_PLUGIN_ROOT}/scripts/log-findings.sh to an absolute path), project_rules). The orchestrator fans out per angle in parallel and decides whether to also run the composition angle based on whether the scope contains multiple independent spec units. When fix_enabled is true it also runs Steps 5–7, escalating only the issues that need design judgment.
The agent will cd to the git root automatically. Provide it with:
- The list of files/folders to review
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 · 58 lines · 69 tokens per session scan A ba54d11b2f96
reviewer:spec is a skill published in the GitHub repository shdennlin/agent-plugins (2 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 546 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 2026-08-31.
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…