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 jaktestowac/awesome-copilot-for-testers --skill unslop-answersgit clone --depth 1 https://github.com/jaktestowac/awesome-copilot-for-testersWrote 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/jaktestowac/awesome-copilot-for-testers/unslop-answers)<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/unslop-answers"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/unslop-answers/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/jaktestowac/awesome-copilot-for-testers/unslop-answers"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/unslop-answers.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 | $0.00151 | $0.03709 |
| Opus 5 | $0.00076 | $0.01854 |
| Sonnet 5 | $0.00030 | $0.00742 |
| Haiku 4.5 | $0.00015 | $0.00371 |
Grade A, and why
unslop-answers 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 8d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unslop Answers
Cut AI tells from what you say about your own work.
There are two failure modes and they cost very differently. An unreadable answer wastes a reader's minute. An unearned answer costs them a day, because they act on it: they ship on "tests pass", they close the bug on "fixed", they plan around a config option that does not exist. Fluency makes it worse. An answer with no evidence in it reads better than one full of commands and denominators, because nothing in it is qualified.
So this skill has one rule, and every pattern below is a way of noticing where the rule broke.
Every claim is evidenced, quantified, or marked unverified.
When to Use
Always, on any answer about work you did. Explicitly when:
- reporting a fix, a result, a root cause, or a review finding
- writing a bug report, a PR description, a commit body, or a release recommendation
- quoting a number: coverage, pass rate, flake rate, timing, defect count
- claiming something is absent: no other callers, no other failures, no regressions
- the request says "prove it", "did you run it", "be specific", "no fluff", "stop hedging"
- an earlier answer of yours turned out to be wrong and the reason needs finding
Scope and Handoffs
This skill covers what you say: chat replies, reports, findings, plans, docs, commit messages, issue bodies.
- test code goes to
unslop-tests, which judges whether a test proves anything - a product feature's output goes to
reviewing-ai-output-groundedness, which is a human protocol for auditing a RAG or summarisation feature - the complete list of prose style tells lives in
./resources/style-tells.md, for when the job is editing text rather than reporting work
If a finding is about a test being a lie, that is unslop-tests. If it is about you calling that test verified, it is this skill.
Fast Pass
Most answers are routine. Run these six before sending anything, in under a minute.
- Did I claim it works? Name the command and paste what it printed, or write "changed, not run".
- Is every path, symbol, flag, option, and quote here one I actually saw? Anything typed from memory gets opened and checked, or dropped.
- Does every finding carry a
file:line? - Does every number carry its denominator and its window?
- Did I do less than was asked? Say which part and why, in the first two lines, not the last bullet.
- Would this paragraph read identically on a different project? Then it says nothing about this one. Cut it.
What ships with it
4 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.
- 8d ago First seen · 219 lines · 151 tokens per session scan A 947a1b7c8fc7
unslop-answers is a skill published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 16d ago), licensed MIT. It adds 151 tokens to every session and 3,709 once invoked, about $0.0008 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.
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