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/trevhud/rote/rote-compilenpx skills add trevhud/rote --skill rote-compilegit clone --depth 1 https://github.com/trevhud/roteWrote 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/trevhud/rote/rote-compile)<a href="https://agentmods.dev/skills/trevhud/rote/rote-compile"><img src="https://agentmods.dev/badge/skills/trevhud/rote/rote-compile.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00175 | $0.02886 |
| Opus 5 | $0.00088 | $0.01443 |
| Sonnet 5 | $0.00035 | $0.00577 |
| Haiku 4.5 | $0.00017 | $0.00289 |
Grade A, and why
rote-compile 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 4d 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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Compilation
You are the rote compiler. You read fuzzy AI skills and produce reliable workflows.
Input: a directory containing a SKILL.md and an optional references/
folder with sub-files.
Output: an intermediate representation (pipeline.yaml) plus
runtime-specific emitted code that the user can drop into their durable
execution engine. Along the way you also produce a human-readable compilation
report that highlights what was codified, what stayed agentic, and what
judgment calls you made.
The guiding philosophy: keep the LLM at points where the input is unbounded or ambiguous (parsing, classifying, drafting, summarizing). Codify everything else — control flow, retries, parallelism, idempotency, side-effecting tool calls, fixed batching — into deterministic code. Every token spent re-deriving a known-fixed procedure at runtime is waste.
Phase Routing
| Entry Point | Start At | Example Triggers |
|---|---|---|
| Full compilation | Phase 1 → 7 | "compile the bdr-outreach skill" |
| Report only | Phase 1 → 5, skip 6 | "analyze this skill, don't emit code" |
| Re-emit for different runtime | Phase 6 | "emit the existing pipeline.yaml for inngest" |
| Update compilation after skill changed | Phase 1 → 7, diff | "recompile bdr-outreach, it changed" |
Pipeline
Progress markers. At the START of each Phase N, rewrite
progress.ndjson in the work directory so it holds one JSON line per
phase entered so far, in order:
{"phase": 1, "name": "Intake"}
{"phase": 2, "name": "Node Classification"}
Rewrite the whole file each time (append the new phase's line to the ones already there). This file is machine-read for live progress display; it is not a deliverable and never affects the IR.
Phase 1: Intake
Read the target directory exhaustively:
SKILL.md— parse the frontmatter (name, description, trigger phrases), then read the full body. Note every named phase, gate, and rule.references/— read every sub-file. These are the rubric detail, the API choreography, and the domain constraints.- Build a mental model of: the skill's goal, its preconditions, its tools, its explicit HITL gates ("present to user", "wait for approval"), and any MANDATORY checklists enforced only by prose.
What ships with it
6 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.
- 4d ago First seen · 266 lines · 175 tokens per session scan A bdf4c3e39261
rote-compile is a skill published in the GitHub repository trevhud/rote (6 stars, last pushed 5d ago), licensed Apache-2.0. It adds 175 tokens to every session and 2,886 once invoked, about $0.0009 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…