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 agentmods add skills/ucsandman/dashclaw/repronpx skills add ucsandman/DashClaw --skill reprogit 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.svg" alt="Measured on agentmods" 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 | $0.00173 | $0.01434 |
| Opus 5 | $0.00086 | $0.00717 |
| Sonnet 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 4d 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.
- 4d ago First seen · 130 lines · 173 tokens per session scan A c395cbe89619
repro is a skill published in the GitHub repository ucsandman/DashClaw (295 stars, last pushed today), licensed MIT. It adds 173 tokens to every session and 1,434 once invoked, about $0.0009 per session on Opus 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.
Other skills, from other repositories
optimizer
Analyzes and automatically optimizes existing agents by improving system prompts and tool configuration.
text2agent
Creates new agents from user requirements by generating Python implementation and mcpconfig.
tiktok_download
Single-file TikTok/Douyin video download and traffic metrics via TikHub API using only httpx; optional persisted raw API JSON plus a stdlib post-processor emitting CSV and simplified JSON. Supports one URL or concurrent batch (max 10 workers). No dependency on any project codebase.
app_evaluator
A professional skill for App Evaluation (evaluating app's performance with score) and App Improvement (giving professional suggestions for improving the app's performance).
last_7_days_news
Search and summarize the latest 7 days of AI news and X discussions using public sources plus browser-based X collection. Use for recent AI news, trends, X discussions, industry briefs, and summaries organized into hot topics, viewpoints, and opportunity areas.
self_evolve
Use for framework-gated self-evolve workflows in AWorld: evolve skills, create trajectory-backed proposals, inspect self-evolve run artifacts, run aworld-cli optimize, or prepare verified apply decisions through aworld.selfevolve gates.