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 mashhoorahdal/shipd-olympus-skill --skill skillgit clone --depth 1 https://github.com/mashhoorahdal/shipd-olympus-skillWrote 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/mashhoorahdal/shipd-olympus-skill/skill)<a href="https://agentmods.dev/skills/mashhoorahdal/shipd-olympus-skill/skill"><img src="https://agentmods.dev/badge/skills/mashhoorahdal/shipd-olympus-skill/skill/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/mashhoorahdal/shipd-olympus-skill/skill"><img src="https://agentmods.dev/badge/skills/mashhoorahdal/shipd-olympus-skill/skill.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 | $0.00079 | $0.02803 |
| Opus 5.5 | $0.00032 | $0.01121 |
| Sonnet 5.5 | $0.00016 | $0.00561 |
| Haiku 4.5 | $0.00008 | $0.00280 |
Grade A, and why
shipd-olympus 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 2mo 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shipd Olympus Submissions
Overview
An Olympus submission = repo@commit + problem description + hidden tests + golden solution + Dockerfile. Tasks must be HARD enough that state-of-the-art agents fail at least half their runs — a one-file fix is an instant rejection. The approved shape: a new "family" of related functionality mirroring something the repo already has, 200+ effective solution LOC, 40+ deterministic tests.
1. Repo eligibility (check ALL before investing)
gh repo view OWNER/REPO --json stargazerCount,licenseInfo,pushedAt,primaryLanguage
- License: permissive only (MIT/Apache/BSD). GitHub API
"other"≠ fail — read the LICENSE file (some repos show "other" but are Apache-2.0; others show "other" but are GPL → disqualified). - Stars: 500+. Activity: commit within 12 months (verify
pushedAtarithmetic — "3 months ago" passes, don't confuse with a 3-month rule). - Language: TS/JS/Python/Go/Rust/C++/Java. Pin commit = head of default branch.
- In the platform form, paste the exact
https://github.com/owner/repoURL — its search surfaces forks (0 stars) and some repos are blocked outright.
2. Task design — aim below the 50% bar
The platform rejects tasks agents pass more than half the time. Design for failure up front; don't hope the batch comes back hard.
Hard is designed, not found. Searching issue trackers for "hard problems" mostly surfaces chores (docs, typings, build config). Instead: pick a complex subsystem in an eligible repo (parser, type dispatcher, scheduler, query planner) and design a feature family that must integrate with it correctly.
Difficulty archetypes, ranked by difficulty gained per spec word spent:
- Convention traps — the spec picks ONE of several plausible conventions and agents implement a neighboring one. Quantile interpolation has ~9 textbook variants; rounding modes, date arithmetic, and string collation are similar minefields. One sentence of spec, an entire wrong-implementation class of failures.
- Exact-boundary rules — one sentence with several failure seams (e.g. weighted median: exact-half cumulative weight averages with the next larger distinct value; duplicates merge; fractional weights break expand-and-recount tricks).
- Non-inferable semantic substitution — the new function differs from its obvious sibling in one deliberate way (divisor uses total weight, not element count). Agents copy the sibling and fail.
- Cross-cutting invariants — one rule enforced at N call sites; agents patch two of four.
- Long wiring chains — the feature must register in 5+ places (factory list, type definitions, docs registry, parser exposure); missing one fails a test non-obviously. Also lifts the LOC and file-count medians the platform requires.
- Parser/compiler surface — grammar, precedence, round-trip printing. Hardest per run, but the spec cost is high; use when the repo's parser is the complex subsystem.
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.
- 2mo ago First seen · 146 lines · 79 tokens per session scan A 9a8c027cdd6c
shipd-olympus is a skill published in the GitHub repository mashhoorahdal/shipd-olympus-skill (7 stars, last pushed 3mo ago), licensed MIT. It adds 79 tokens to every session and 2,803 once invoked, about $0.0003 per session on Opus 5.5. 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.
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workflow-patterns
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go-testing
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writing-livekit-scenarios
Creates and maintains the scenarios a LiveKit agent simulation runs, and wires the agent to consume them. Use when the user asks "what should I test", "generate simulation scenarios", "write scenarios for my agent", "add a scenario for X", "organize my scenario files", "my simulations are flaky", "the scenario hits my…
running-livekit-simulations
Runs LiveKit agent simulations and acts on the results. Use when the user says "run my simulations", "regression test my agent before deploying", "run the scenarios", "use lk agent simulate", "did my agent pass", "why did this scenario fail", "run simulations in CI", "test the audio pipeline", "check turn-taking and…
designing-tests
Designs and implements testing strategies for any codebase. Use when adding tests, improving coverage, setting up testing infrastructure, debugging test failures, or when asked about unit tests, integration tests, or E2E testing.
shared/tdd
A test-driven development guide, where tests are written before the production code and drive the implementation.