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 FluxonLab/Skillry --skill 08-runtime-diagnosticsgit clone --depth 1 https://github.com/FluxonLab/SkillryWrote 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/fluxonlab/skillry/08-runtime-diagnostics)<a href="https://agentmods.dev/skills/fluxonlab/skillry/08-runtime-diagnostics"><img src="https://agentmods.dev/badge/skills/fluxonlab/skillry/08-runtime-diagnostics/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/fluxonlab/skillry/08-runtime-diagnostics"><img src="https://agentmods.dev/badge/skills/fluxonlab/skillry/08-runtime-diagnostics.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 | $0.00026 | $0.03227 |
| Opus 5.5 | $0.00010 | $0.01291 |
| Sonnet 5.5 | $0.00005 | $0.00645 |
| Haiku 4.5 | $0.00003 | $0.00323 |
Grade C, and why
runtime-diagnostics scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 26d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- **`rm -rf node_modules` as a reflex.** Sometimes right, but it hides the actual mismatch and wastes minutes; check `npm ci --dry-run` first to see what is really out of sync. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
7. **Trace external dependency connectivity directly.** Ping the DB (`psql "$DATABASE_URL" -c '\conninfo'`, `redis-cli -u "$REDIS_URL" ping`) and HTTP deps (`curl -s -o /dev/null -w '%{http_code}\n' "$API/health"`). Repo The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 26d ago First seen · 163 lines · 26 tokens per session scan C ce7231d43931
runtime-diagnostics is a skill published in the GitHub repository FluxonLab/Skillry (6 stars, last pushed 23d ago), with no licence file. It adds 26 tokens to every session and 3,227 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 2 findings (recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-20.
Other skills, from other repositories
skill-repair
Diagnose and fix failing or degraded skills automatically - systemic-first triage, per-category playbooks, and a verification plan.
self-improve
Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed.
aeon-doctor
Static config-correctness linter for this instance - catches the silent-failure class (unquoted schedules, duplicate keys, unconfigured skills, mode typos, broken requires/MCP refs) that no run-based health skill can see. Notifies only on problems.
manage-skills
A maintenance workflow for checking whether project verification skills still cover the code and rules that changed during a session.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
monitoring-observability
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards. Use when instrumenting services, designing logs/traces/metrics, defining SLOs and alerts, or debugging production behavior from telemetry.