Borrowing it
Nothing to install: this file belongs to lbx154/Argus. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lbx154/Argus/main/.agents/skills/minimal-rigorous-work/SKILL.mdgit clone --depth 1 https://github.com/lbx154/ArgusWrote 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/lbx154/argus/minimal-rigorous-work)<a href="https://agentmods.dev/skills/lbx154/argus/minimal-rigorous-work"><img src="https://agentmods.dev/badge/skills/lbx154/argus/minimal-rigorous-work.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 5.1 | $0.00032 | $0.00359 |
| Opus 5 | $0.00016 | $0.00179 |
| Sonnet 5 | $0.00006 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
minimal-rigorous-work 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.
What it actually says
Minimal rigorous work
Solve the current task completely with the shortest clear control path.
- Start from the requested behavior, the real entry point, and observable evidence. Do not fix a bug that cannot be reproduced on the current commit.
- Reuse existing contracts and helpers. Do not add unrelated features, refactors, dependencies, roles, layers, configuration, or artifacts.
- Validate external input, authority, persistence, security, money, citations, and irreversible actions. Trust established internal invariants.
- Unless a demonstrated requirement needs them, do not add hashes, UUIDs, random identifiers, retries, backoff, fallback chains, duplicate guards, speculative locks, compatibility layers, wrappers, factories, or placeholder interfaces.
- Keep errors explicit. Do not hide defects behind broad catches, empty results, default values, or success-shaped degradation.
- Use tokens by reducing uncertainty and repeated context: carry one canonical task contract, inspect unchanged inputs once, act when evidence is sufficient, and run one decisive check per claim. Do not hard-code token caps, truncate required context, or add token-shortening functions.
- Record a reusable lesson only when reproduced evidence shows it generalizes. Scope the lesson narrowly; do not create ceremonial Skills, Wiki entries, or validation-only turns.
- Review independently and read-only. Verify the changed user-visible path; broaden checks only when the blast radius is broad.
- Report
BLOCKEDorUNTESTEDhonestly. Stop when the requested outcome is complete.
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 · 33 lines · 32 tokens per session scan A 7bc4b058132f
minimal-rigorous-work is a skill published in the GitHub repository lbx154/Argus (301 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 359 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…