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 skills add code-yeongyu/lazyclaudecode --skill ultragoalgit clone --depth 1 https://github.com/code-yeongyu/lazyclaudecodeWrote 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/code-yeongyu/lazyclaudecode/ultragoal)<a href="https://agentmods.dev/skills/code-yeongyu/lazyclaudecode/ultragoal"><img src="https://agentmods.dev/badge/skills/code-yeongyu/lazyclaudecode/ultragoal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/code-yeongyu/lazyclaudecode/ultragoal"><img src="https://agentmods.dev/badge/skills/code-yeongyu/lazyclaudecode/ultragoal.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00024 | $0.03589 |
| Opus 5 | $0.00012 | $0.01795 |
| Sonnet 5 | $0.00005 | $0.00718 |
| Haiku 4.5 | $0.00002 | $0.00359 |
Grade C, and why
ulw-loop scanned grade C with 2 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 9d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
5. CLEAN (PAIRED, NEVER SKIP): tear down every runtime artifact step 3 spawned BEFORE recording — server PIDs (`kill`, verify `kill -0` fails), `tmux` sessions (`tmux kill-session -t ulw-qa-<criterion>`; confirm `tmux ls Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **HTTP call** — hit the live endpoint with `curl -i` (or a Playwright APIRequestContext); capture status line + headers + body. How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Expert goal orchestration agent. Plan multi-goal work that survives across turns and sessions. Use GPT-5.x style: outcome-first, evidence-bound, atomic decisions, no nested branching prose.
Goal
Deliver every goal in .omo/ultragoal/goals.json end-to-end.
Prove EVERY success criterion with captured observable evidence from a real-usage scenario you actually ran (HTTP call / tmux / browser use / computer use — see the Manual-QA channels below).
TESTS ALONE NEVER PROVE DONE. A green test suite is supporting evidence, not completion proof.
Audit each pass, fail, block, steering change, and checkpoint in .omo/ultragoal/ledger.jsonl.
Manual-QA channels (PICK ONE PER CRITERION — ACTUALLY RUN IT)
For every criterion, build a real-usage scenario through ONE of these four channels and run it yourself before recording PASS. The full test suite being green is NEVER verification on its own.
- HTTP call — hit the live endpoint with
curl -i(or a Playwright APIRequestContext); capture status line + headers + body. - tmux —
tmux new-session -d -s ulw-qa-<criterion>, drive withsend-keys, dump viatmux capture-pane -pS -E -; transcript is the artifact. - Browser use — drive the real page via Playwright / puppeteer / Chromium; capture action log + screenshot path.
- Computer use — OS-level GUI automation (computer-use agent, AppleScript, xdotool, etc.) against the running app; capture action log + screenshot.
Auxiliary surfaces (pure CLI stdout / DB state diff / parsed config dump) satisfy CLI- or data-shaped criteria but NEVER replace a channel scenario for user-facing behavior. --dry-run, printing the command, "should respond", and "looks correct" never count.
Artifacts
.omo/ultragoal/brief.md: original brief and durable constraints..omo/ultragoal/goals.json: goals with embeddedsuccessCriteriaper goal..omo/ultragoal/ledger.jsonl: append-only audit trail.- Read artifacts before resuming, steering, or checkpointing.
- Never invent state outside
.omo/ultragoalartifacts oromo ultragoal status --json.
What ships with it
2 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.
- 9d ago First seen · 199 lines · 24 tokens per session scan C d267094bf626
ulw-loop is a skill published in the GitHub repository code-yeongyu/lazyclaudecode (18 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 3,589 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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