Borrowing it
Nothing to install: this file belongs to AHepi/DeepReason. 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/AHepi/DeepReason/main/.claude/skills/dr-reproduce/SKILL.mdgit clone --depth 1 https://github.com/AHepi/DeepReasonWrote 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/ahepi/deepreason/dr-reproduce)<a href="https://agentmods.dev/skills/ahepi/deepreason/dr-reproduce"><img src="https://agentmods.dev/badge/skills/ahepi/deepreason/dr-reproduce/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/ahepi/deepreason/dr-reproduce"><img src="https://agentmods.dev/badge/skills/ahepi/deepreason/dr-reproduce.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 | $0.00042 | $0.00652 |
| Opus 5 | $0.00021 | $0.00326 |
| Sonnet 5 | $0.00008 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
dr-reproduce 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 9d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproduce the cause
Input: DIAGNOSIS.md's falsifiable prediction. Output: REPRO.md plus one runnable artifact that shows the defect NOW (and will show its absence after the fix). You still change no production code.
Choose the cheapest sufficient form, in this order
- Record replay — the defect is already capted in a committed run
root: a script that runs
verify_root(<root>)or walks the log and prints the violating fact. Zero live cost; deterministic. - Offline unit reproduction — construct the minimal state in a
test: register the same problems/artifacts/policies the record
shows and assert the wrong behavior happens. Reuse existing test
helpers (
_prepare_run, controller fixtures,_policybuilders) from the nearesttests/test_*.py; do not invent new scaffolding when a helper exists. - Minimal in-memory check — a 20-line python3 heredoc proving the
mechanism (e.g. round-trip a receipt through
canonical_jsonand show key order changes). Acceptable as evidence, but pair it with form 1 or 2 for the regression artifact.
NEVER reproduce by launching a live provider run. Live runs are for
dr-verify-outcome, at most once, and only if the goal demands it.
Fidelity rules
- The reproduction must mirror the live conditions the record shows, not a convenient simplification. If admission auto-accepted import-role artifacts before cycle 0, your fixture registers those artifacts too. A reproduction that passes for a different reason than the live failure will approve a wrong fix.
- One assertion states the DEFECT (currently failing or currently printing the violation), phrased so it inverts cleanly post-fix.
- Respect frozen-record invariants in fixtures: one manifest sha per capability chain, constant fence seqs within a proposal's chain — fixture WellFormednessError means your fixture is wrong, not the harness.
REPRO.md template
# Reproduction
Form: record-replay | unit-test | in-memory
Artifact: <path (test id) or inline command>
Current output: <paste the failing/violating output, trimmed>
Confirms diagnosis: yes — <one line linking output to mechanism>
Post-fix expectation: <exact output/assertion after a correct fix>
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.
- 9d ago First seen · 64 lines · 42 tokens per session scan A 4bfe20268ba3
dr-reproduce is a skill published in the GitHub repository AHepi/DeepReason (142 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 652 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 2026-08-30.
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