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.
git clone --depth 1 https://github.com/frankxai/Starlight-Intelligence-SystemWrote 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/commands/frankxai/starlight-intelligence-system/talent-burnout-detect)<a href="https://agentmods.dev/commands/frankxai/starlight-intelligence-system/talent-burnout-detect"><img src="https://agentmods.dev/badge/commands/frankxai/starlight-intelligence-system/talent-burnout-detect/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/commands/frankxai/starlight-intelligence-system/talent-burnout-detect"><img src="https://agentmods.dev/badge/commands/frankxai/starlight-intelligence-system/talent-burnout-detect.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00092 | $0.03224 |
| Opus 5 | $0.00046 | $0.01612 |
| Sonnet 5 | $0.00018 | $0.00645 |
| Haiku 4.5 | $0.00009 | $0.00322 |
Grade A, and why
talent-burnout-detect 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 8d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/talent-burnout-detect
Load SIP.md, VOICES.md, agents/starlight-talent.md, skills/people-intelligence/people-dynamics.md, and any prior motivation maps for this person (people-intelligence/talent/motivation-*) and team-context (people-intelligence/culture/). Produce a Burnout Detection Protocol. Hand off to exactly one next move — clinical referral if applicable, otherwise system + individual intervention.
Disclaimer (non-waivable)
This is HR system architecture, not clinical advice. Burnout co-occurs with depression, anxiety, and other clinical conditions but is distinct from them. When signals suggest active depression, anxiety disorder, eating disorder, addiction, or suicidality, refer to a qualified mental health clinician — that referral is the load-bearing next move, not an HR intervention. This is also not legal advice. ADA accommodations and protected-class considerations require jurisdiction-specific compliance and individualized interactive process — validate with qualified counsel.
Input
$ARGUMENTS
Flags
--target <person|team>— individual or team-level burnout detection. Team-level uses aggregate Maslach signal across the unit; still flags any individual-level clinical signal that surfaces.--signal-window <weeks>— observable signal window. <8 weeks: low confidence, flag explicitly. 12+ weeks: high confidence. Single-snapshot burnout detections are refused — burnout is longitudinal by definition.- Optional context: observable signals in plain language — workload pattern, recent transitions, relational withdrawal, manager observations.
Process
-
Disclaim. Open the output with the non-waivable disclaimer. Structurally first. Clinical boundary clearly stated.
-
Locate target. Person or team. Recent transitions (reorg, layoff, scope change, bereavement, new manager). Current workload context. If team-level: cohort size, role mix, manager structure.
-
Gather longitudinal signals across the window.
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.
- 8d ago First seen · 214 lines · 92 tokens per session scan A f781e06af7f4
talent-burnout-detect is a command published in the GitHub repository frankxai/Starlight-Intelligence-System (8 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 3,224 once invoked, about $0.0005 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 2026-09-03.
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