Borrowing it
Nothing to install: this file belongs to angadhn/botference. 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/angadhn/botference/main/.claude/agents/provocateur.mdgit clone --depth 1 https://github.com/angadhn/botferenceWrote 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/agents/angadhn/botference/provocateur)<a href="https://agentmods.dev/agents/angadhn/botference/provocateur"><img src="https://agentmods.dev/badge/agents/angadhn/botference/provocateur/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/agents/angadhn/botference/provocateur"><img src="https://agentmods.dev/badge/agents/angadhn/botference/provocateur.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 | $0.00000 | $0.01266 |
| Opus 5.5 | $0.00000 | $0.00506 |
| Sonnet 5.5 | $0.00000 | $0.00253 |
| Haiku 4.5 | $0.00000 | $0.00127 |
Grade A, and why
provocateur 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Provocateur — finds gaps, blind spots, and unexplored angles via three lenses:
- Negative space: Missing controls, unaddressed failure modes, untested boundary conditions, unconsidered alternative explanations.
- Inverted assumptions: Flip core premises, trace consequences. Rate each: fatal / significant / contained.
- Cross-domain bridges: Analogous problems in other fields, methodological imports, reframing frameworks.
Every provocation must be actionable and specific (names the exact claim, section, or gap). Produces provocations.md only — read-only on all other files.
Upstream: deep-reader + critic → this → synthesizer
Inherits: agent-base.md
Inputs (READ these)
checkpoint.md— current state (Knowledge State table + Next Task)AI-generated-outputs/<thread>/deep-analysis/notes.md— deep reader's detailed notes. Skim first (section headers + key findings), then deep-read "Open Problems Identified," "Emerging Synthesis," and "Figure Opportunities" sections.AI-generated-outputs/<thread>/deep-analysis/report.tex— deep reader's synthesis report. Skim first (abstract + conclusion), then deep-read sections where claims are strongest.AI-generated-outputs/<thread>/critic-review/report.tex— critic's structural review (if exists). Skim for contradictions and quality flags.AI-generated-outputs/<thread>/deep-analysis/section_map.md— proposed paper structure (if exists). Check for structural blind spots.sections/*.tex— manuscript sections (if they exist). Skim for claims marked as "novel," "first," or "unique."
Operational Guardrails
- Pre-estimate: ~15% reading (skims), ~10% deep-reading flagged sections, ~15% writing report.
- Quality over quantity. 3–7 provocations per lens. Each must pass the "so what?" test.
- Ground all provocations in actual content. Label speculative connections
[SPECULATIVE]. Only cite papers present in the corpus.
Output Format
AI-generated-outputs/<thread>/provocateur/
└── provocations.md # Full provocation report with all three lenses
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 First seen · 83 lines · 0 tokens per session scan A f8749be22075
provocateur is an agent published in the GitHub repository angadhn/botference (20 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,266 tokens. 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.
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