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 IgorWarzocha/howaboua-pi-stuff --skill harness-checklistgit clone --depth 1 https://github.com/IgorWarzocha/howaboua-pi-stuffWrote 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/igorwarzocha/howaboua-pi-stuff/harness-checklist)<a href="https://agentmods.dev/skills/igorwarzocha/howaboua-pi-stuff/harness-checklist"><img src="https://agentmods.dev/badge/skills/igorwarzocha/howaboua-pi-stuff/harness-checklist/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/igorwarzocha/howaboua-pi-stuff/harness-checklist"><img src="https://agentmods.dev/badge/skills/igorwarzocha/howaboua-pi-stuff/harness-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.02358 |
| Opus 5.5 | $0.00007 | $0.00943 |
| Sonnet 5.5 | $0.00003 | $0.00472 |
| Haiku 4.5 | $0.00002 | $0.00236 |
Grade A, and why
harness-checklist 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 1mo 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform a read-only audit. Establish what the harness provides natively before assessing its surrounding environment. Do not count native capabilities as additions or recommend rebuilding them by default. Judge capabilities, not package names. Do not install, remove, edit, enable, or reconfigure anything during the audit.
Establish the audit
Use the request and known environment to bound the audit. Select the checks below that bear on the requested workflows; do not turn a focused audit into a full-environment survey. Ask only for missing decisions or context that cannot be recovered from the available evidence.
- Locate the harness working set from supplied paths and environment records; ask only if its location remains unknown. Accept a monorepo, assembly directory, or maintained copy folder containing extensions, skills, context files, tools, profiles, configuration, and user interfaces. If none exists, record the limitation and recommend one. Do not assemble it without a separate request.
- Reconcile three views: capabilities native to the current harness, the exact user and agent toolkit exposed in this session, and additions present in the working set. Inspect current help, active tools and skills, commands and UI, loaders, settings, and model-visible context. Mark capabilities as native, active from the working set, active from another source, available but inactive, generated, obsolete, or absent. A folder does not prove exposure, while one session does not prove the complete available setup.
- When startup exposure or overhead is in scope, run a fresh visible session with the effective setup and one trivial first turn such as
Reply only with hi. Record provider-reported prompt input and inspect the session, active tools, instructions, startup messages, and other model-visible surface. When the harness can safely disable optional layers, repeat with the same model, reasoning, working directory, and prompt as a native baseline. Treat the difference as permanent startup overhead, not a quality score. - Use the reported friction and expected behaviour to identify the affected workflow. Ask only when the mismatch remains unclear.
- Inspect representative project folders or overlays for each in-scope workflow; ask for their location only when it is unknown. Do not call a capability missing until checking its work-specific layer. If the base permanently loads one domain's surface, consider a project folder, launch profile, or overlay.
- Establish what the agent may do silently, report afterward, ask before doing, or hand back from existing user decisions and policy. Ask about unresolved boundaries that affect the workflow rather than reopening settled permissions.
- Compare that contract with instructions, approvals, tool restrictions, extension behaviour, notifications, and recovery controls. Flag unwanted ceremony and invisible automation.
- Trace global and scoped context, skills, extension prompts, tool guidance, and templates through their real load paths. Find conflicts, duplication, shadowing, misplaced scope, and double-loaded behaviour.
- Group extensions, tools, skills, CLIs, scripts, slash commands, and host behaviour by job and audience. Identify one behavioural owner. Prefer a CLI or CLI plus skill when hooks, state, UI, or tool registration add nothing.
For every later check, credit a working capability regardless of whether it is native, configured globally, project-scoped, or exposed only in the current session. Recommend an addition only for a remaining gap.
When a gate requires deeper instruction, tool, extension, cache, or session analysis, load an applicable specialist skill at that gate. Use it only for analysis. Do not implement its remediation procedure during this audit.
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.
- 1mo ago Changed · +2 lines bd6c7294fb3e
- 2mo ago First seen · 128 lines · 17 tokens per session scan A 8da614d882d9
harness-checklist is a skill published in the GitHub repository IgorWarzocha/howaboua-pi-stuff (413 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 2,358 once invoked, about $0.0001 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 2026-08-30.
harness-checklist compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| harness-checklistIgorWarzocha/howaboua-pi-stuff | Skill | 413 | yesterday | MIT | A | 17 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |