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 skills add reatlat/fullstory-claude-plugin --skill deploy-radargit clone --depth 1 https://github.com/reatlat/fullstory-claude-pluginWrote 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/reatlat/fullstory-claude-plugin/deploy-radar)<a href="https://agentmods.dev/skills/reatlat/fullstory-claude-plugin/deploy-radar"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/deploy-radar/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/reatlat/fullstory-claude-plugin/deploy-radar"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/deploy-radar.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 | $0.00038 | $0.00879 |
| Opus 5 | $0.00019 | $0.00439 |
| Sonnet 5 | $0.00008 | $0.00176 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
deploy-radar 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 10d 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.
Deploy Radar
Post-deploy health check: compare key metrics before and after a deploy to catch regressions, new errors, and conversion changes.
When to Use
- "I just deployed — did anything break?"
- "Check if the 3pm deploy caused any issues"
- "Compare error rates before and after the July 15 deploy"
- "Did the checkout change improve or hurt conversion?"
Workflow
Step 1: Define the deploy window
Get the deploy timestamp from the user. If they say "3pm today" or "just deployed", pin to the nearest hour.
Define two time windows:
- Before: e.g., 24 hours before deploy (or last 7 days if it was a big release)
- After: e.g., from deploy time to now
Ask: "I'll compare the 24 hours before and after the deploy. That OK, or do you want a wider window?"
Step 2: Check errors
Build an error metric and compute it for both windows:
fullstory:build_metric(query="console errors and network failures", output_type="single_number")
fullstory:compute_metric(metric_id, time_range=before_window)
fullstory:compute_metric(metric_id, time_range=after_window)
Also check for new errors that didn't exist before:
fullstory:build_metric(query="console errors", output_type="top_n")
→ compute for after window, compare to before window
→ flag error types that appear only in the after window
Step 3: Check frustrations
Call fullstory:get_opportunities for the after window. For each, call fullstory:get_opportunity_stats. Check if any opportunity's rate-of-change vs the before window shows a spike.
Quick sanity check: rage click count before vs after. If it doubled, the deploy introduced friction.
Step 4: Check conversion
Pick 1-2 key funnels (checkout, signup):
fullstory:compute_metric(funnel_metric_id, time_range=before_window)
fullstory:compute_metric(funnel_metric_id, time_range=after_window)
Step 5: Report
## Deploy Health — v2.3.0 (Aug 4 15:00 UTC)
### Errors
- Before: 12 errors (24h window)
- After: 14 errors (+17%) 🟡
- New: TypeError on /checkout (3 occurrences, did not exist before) 🔴
### Frustrations
- Rage clicks: 87 before → 92 after (+6%) 🟢 (within normal variance)
- Dead clicks: stable
- No new opportunity signals detected 🟢
### Conversion
- Checkout completion: 21% before → 20% after (-5%) 🟡
- Signup completion: 64% → 63% (-2%) 🟢
### Verdict: Deploy is mostly clean. The new TypeError on /checkout is worth investigating — 3 users in 24h, looks like a null check regression. Want me to dig into those sessions?
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.
- 10d ago First seen · 92 lines · 38 tokens per session scan A 8efa4a8ccfc4
deploy-radar is a skill published in the GitHub repository reatlat/fullstory-claude-plugin (62 stars, last pushed 28d ago), licensed MIT. It adds 38 tokens to every session and 879 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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