Borrowing it
Nothing to install: this file belongs to ucsandman/DashClaw. 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/ucsandman/DashClaw/main/.claude/skills/repro/SKILL.mdgit clone --depth 1 https://github.com/ucsandman/DashClawWrote 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/ucsandman/dashclaw/repro)<a href="https://agentmods.dev/skills/ucsandman/dashclaw/repro"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw/repro/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/ucsandman/dashclaw/repro"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw/repro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 110 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00173 | $0.01434 |
| Opus 5.5 | $0.00069 | $0.00574 |
| Sonnet 5.5 | $0.00035 | $0.00287 |
| Haiku 4.5 | $0.00017 | $0.00143 |
Grade A, and why
repro scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl`/the SDK against the local dev server (`npm run dev`, port 3000) beats 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.
repro — symptom in, structured bug report out
A raw symptom ("the decisions page is blank") is a weak fix prompt: it forces whoever fixes it to re-derive the environment, the steps, and the expected behavior from scratch, and it leaves nothing behind to prove the bug stays fixed. A structured bug report front-loads that work once, turns into a sharp fix prompt, and — its highest-value output — becomes a failing regression test that pins the defect forever.
This skill produces that report, then offers to scaffold the test. It does not
fix the bug — capturing it cleanly is the whole job; hand the report to the
fix (or to /systematic-debugging) afterward.
The report format — use this exact structure
Emit the report as one contiguous, pasteable block (no leading > quote
markers, no mid-sentence line breaks — Wes pastes these into prompts and
issues):
## BUG: <one-line summary — the observable failure, not the guessed cause>
**Severity:** <blocker | major | minor — blocker = data loss/security/prod down>
**Surface:** <page + click path, or route, or CLI command, or SDK method>
**Environment**
- Commit: <short sha> (<branch>)
- Node: <version> · Platform: <os>
- Package/SDK version: <if relevant>
- Browser: <only if a UI bug>
**Steps to reproduce**
1. <exact, replayable step — real values, not "do the thing">
2. ...
3. ...
**Actual**
<what happens — verbatim error, status code, stack line, screenshot ref>
**Expected**
<what should happen, and why you believe that — cite the spec/route/test>
**Evidence**
- <log snippet, failing assertion, network response, file:line>
**First suspicion (optional)**
<one sentence — a lead, explicitly marked as unverified so it doesn't anchor>
Two rules that keep the report honest:
- Summary = the symptom, never the guessed cause. "Decisions page renders blank" is reproducible and verifiable; "DecisionsLedgerInner crashes on null agent" is a theory that may be wrong and will mislead the fixer. Keep the theory in First suspicion, clearly fenced off.
- Steps must be replayable by someone with none of your context. If a step reads "with the right filter set," it's not a step — name the filter and the value.
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 · 130 lines · 173 tokens per session scan A c395cbe89619
repro is a skill published in the GitHub repository ucsandman/DashClaw (313 stars, last pushed yesterday), licensed MIT. It adds 173 tokens to every session and 1,434 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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