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/agenkin/telemetrydeck-analytics/reportnpx skills add agenkin/telemetrydeck-analytics --skill reportgit clone --depth 1 https://github.com/agenkin/telemetrydeck-analyticsWrote 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/agenkin/telemetrydeck-analytics/report)<a href="https://agentmods.dev/skills/agenkin/telemetrydeck-analytics/report"><img src="https://agentmods.dev/badge/skills/agenkin/telemetrydeck-analytics/report.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 | $0.00018 | $0.00490 |
| Opus 5 | $0.00009 | $0.00245 |
| Sonnet 5 | $0.00004 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
report 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 3d 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.
What it actually says
Report
Write a compact markdown analytics report that answers the user's question in $ARGUMENTS. This skill is the high-level wrapper: pick the right sub-query (recipe or raw TQL), run it, and turn the numbers into a narrative.
Process
- Parse the question in
$ARGUMENTS. Identify: metric (count vs. users), dimension (if any), window, comparison target. - Pick the command:
- DAU/MAU trend →
tdq dau/tdq mau - Breakdown by one dimension →
tdq groupby <dim> - Which events fire →
tdq events - Funnel / retention / multi-dim groupby / derived metric → write raw TQL and run via
tdq query -(consultskills/analytics/tql/index.mdfor syntax).
- DAU/MAU trend →
- Run it — use the Bash tool. Capture the table output.
- Write the report using this structure:
## <question restated as a header>
**Top-line:** <one sentence with the key number and window>
**Method:** `<the exact `tdq …` invocation you ran>`
**Result:**
<markdown table — paste the CLI output directly>
**Interpretation:** <1–3 sentences: what the numbers mean, any trend or outlier, compared-to-what>
**Caveats:** Opt-in cohort only (TelemetryDeck samples self-selected users). <add channel skew / partial-period / small-sample notes if applicable>
- Where to save: inline in the conversation by default. If the user asks for a file, ask them where to save it; write to the absolute path they give. Suggest
~/Documents/TelemetryDeck/YYYY-MM-DD-<slug>.md. Never create new top-level folders in the current working directory.
Defaults
- Window: last 30 days unless the question implies otherwise.
- Format:
table(markdown-ready). - Always include the opt-in-cohort caveat.
- For "trending" / "growing" / "vs. last period" phrasings, add
--compare prior-period.
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.
- 3d ago First seen · 44 lines · 18 tokens per session scan A 78ecf2282d51
report is a skill published in the GitHub repository agenkin/telemetrydeck-analytics (2 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 490 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
motion-canvas
Motion Canvas framework reference covering project setup, core concepts (generators, signals, refs, scene hierarchy, timing, utilities), and 2D components (shapes, paths, text, media, layout, camera, transitions, custom components). Use when building or editing Motion Canvas scenes.
outsystems-mcp
You are connected to OutSystems over the MCP HTTP transport. OutSystems is a cloud-native low-code platform where apps are built from OML (OutSystems Model Language), a binary format describing entities, screens, actions, and logic. Every tool call carries the harness's validated OAuth bearer; tenant + user identity…
motion-canvas-agent
Agent tooling for Motion Canvas — seek, screenshot, scene graph inspection, settings control, and rendering via HTTP API. Requires a browser with the editor open.
animation-basics
Animation theory covering the 12 principles of animation and timing guidance. Use when designing motion, choosing easing approaches, or improving animation quality.
creating-explainers
Use when creating an interactive explainer - a single self-contained HTML page with hand-built Canvas figures. Handles source-file explainers, topic-driven research explainers, and mixed intake where files provide the spine and research adds support. Trigger phrases include "make an explainer", "turn this paper into…
fact-checking-explainers
Use to fact-check an explainer against its sources before delivery. Trigger on "fact-check this explainer", "verify the claims", "check this against its sources", research-time source checks, or the post-draft gate. Ensures every checkable claim traces to a cited source or, for code, the real implementation. Required…