Borrowing it
Nothing to install: this file belongs to DanceNitra/agora. 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/DanceNitra/agora/main/.claude/skills/stress-claim/SKILL.mdgit clone --depth 1 https://github.com/DanceNitra/agoraWrote 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/dancenitra/agora/stress-claim)<a href="https://agentmods.dev/skills/dancenitra/agora/stress-claim"><img src="https://agentmods.dev/badge/skills/dancenitra/agora/stress-claim/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/dancenitra/agora/stress-claim"><img src="https://agentmods.dev/badge/skills/dancenitra/agora/stress-claim.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 | $0.00191 | $0.02051 |
| Opus 5.5 | $0.00076 | $0.00820 |
| Sonnet 5.5 | $0.00038 | $0.00410 |
| Haiku 4.5 | $0.00019 | $0.00205 |
Grade A, and why
stress-claim 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stress-Claim — adversarially red-team our own claim before we ship it
What this does
Takes ONE of our claims (a Crucible verdict, a flagship thesis, a hypothesis, a post draft) and attacks it
the way a hostile expert would, before we publish or build on it. Where verify-claims
checks whether the cited facts are true, this checks whether the argument holds: is it actually
novel or a textbook re-derivation, does it overclaim past the evidence, what is the strongest counter, and
what is the blind spot no lens addressed. It returns a blunt verdict — PUBLISH / REFRAME / KILL — with
the exact fixes. The goal is to find why we're wrong before a sharp audience does (the breakthrough scan
killed 5/6 candidates this way; the nudging/G2G confounds were caught this way).
Self-contained: built-in Agent (general-purpose), WebSearch/WebFetch. No external services. ~5-7 agents.
When to use (before, not after)
Run it BEFORE: recording a Crucible REPRODUCED/FAILED verdict, shipping a flagship/canon claim, opening a
gated outreach that asserts a finding, or committing to build on a result. Pairs with verify-claims
(facts) — run both before any flagship publish; this one is the argument/credibility half.
PRE-DRAFT MODE — the default for a new article (owner, PERMANENT 2026-08-17)
"pred publikovanim noveho clanku pusti skeptika dopredu." For anything NEW — an article, a post, a Crucible entry, a first-contact reply — run this before the draft exists, on the claim and its measurement rather than on prose. Skip Phase 2's full panel; run these three, in one message:
- SHOULD-THIS-EXIST —
Is {CLAIM} textbook, already published, or already stated by the party we are writing to? Search the literature AND, if there is a target repo/thread, ITS OWN issue tracker and prior comments. Return: NEW / TEXTBOOK-CITE-IT / ALREADY-SAID-BY-THEM, with the source. - COUNTER-MEASUREMENT — hand it the probe and the target, never the draft:
Re-run this measurement hostilely and try to refute the number. Is the criterion narrower than the property? Is any comparison a tautology? Did the harness create the state it reports? - ON-FRONTIER —
Does {CLAIM} advance the owner's standing priorities, scored with the SAME board_priority_terms the gates use — not by eye?
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 · 92 lines · 191 tokens per session scan A 69b69048f3dd
stress-claim is a skill published in the GitHub repository DanceNitra/agora (3 stars, last pushed 7d ago), licensed MIT. It adds 191 tokens to every session and 2,051 once invoked, about $0.0008 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.
stress-claim compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stress-claimDanceNitra/agora | Skill | 3 | 7d ago | MIT | A | 191 | Claude Code |
| Evaluationagentic-in/elephant-agent | Skill | 584 | 2mo ago | none | A | 25 | Claude Code, Codex |
| create-custom-graderNVIDIA/SkillEvaluator | Skill | 643 | yesterday | Apache-2.0 | A | 35 | Claude Code, Codex |
| api-callerNVIDIA/SkillEvaluator | Skill | 643 | yesterday | Apache-2.0 | A | 29 | Claude Code, Codex |
| calculatorNVIDIA/SkillEvaluator | Skill | 643 | yesterday | Apache-2.0 | A | 32 | Claude Code, Codex |
| text-analyzerNVIDIA/SkillEvaluator | Skill | 643 | yesterday | Apache-2.0 | A | 42 | Claude Code, Codex |
| company-braincaura-ai/caura | Skill | 549 | yesterday | Apache-2.0 | A | 108 | Claude Code |