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/sananthanarayan/skilldrop/observability-plannpx skills add sananthanarayan/skilldrop --skill observability-plangit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/observability-plan)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/observability-plan"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/observability-plan.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 | $0.00097 | $0.02010 |
| Opus 5 | $0.00048 | $0.01005 |
| Sonnet 5 | $0.00019 | $0.00402 |
| Haiku 4.5 | $0.00010 | $0.00201 |
Grade A, and why
observability-plan 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 5d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
observability-plan
Designs the telemetry that makes a service debuggable at 3am and detectable before users complain — SLOs measured from the user's experience, alerts that fire only when a human must act, and the three pillars each used for what they're good at. Expands the one-line observability requirement from nfr-spec into a full operating plan; upstream of incident-comms and postmortem-generator (you can't communicate or analyze an incident you couldn't detect). Distinct from success-metrics (business outcomes) — this is operational health.
How to respond
-
Start from user journeys, not from the resource dashboard. The first artifact is the list of critical journeys ("log in", "place order", "load feed") — because an SLI measures what the user experiences, and CPU graphs measure what's easy. Ask at most 2 questions, spent on the most critical journey and what "down" means to a user there. Resource metrics (the golden signals) still get covered — but as saturation/cause signals, never as the SLO.
-
Define SLIs as good-events / valid-events from the user's side (catalog in
reference.md) — availability (success rate), latency (a threshold, measured at a percentile: "% of requests < 300ms"), and where relevant freshness, correctness, throughput. ✅ "SLI: proportion of checkout requests returning 2xx within 1s, measured at the load balancer" — ❌ "SLI: average CPU" (the user never feels your CPU). Latency SLIs are a threshold count, not an average — averages hide the tail that hurts. -
Set SLOs with an explicit error budget and window. Each SLI gets a target over a rolling window: "99.9% over 28 days." That target is the error budget (0.1% = ~40 min/28d) — the quantity that governs both alerting and how aggressively you ship. State the budget in human terms (minutes/month) and name the error-budget policy: what happens when it's spent (freeze risky releases, redirect to reliability) — an SLO with no policy is a number nobody defends. Pick targets honestly (99.9% is not free; see
nfr-spec's down-for-an-hour calibration) — more nines is exponentially more cost.
What ships with it
5 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.
- 5d ago First seen · 71 lines · 97 tokens per session scan A 714a090a036a
observability-plan is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 21d ago), licensed MIT. It adds 97 tokens to every session and 2,010 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 2026-08-31.
Other skills, from other repositories
openlore-brainstorm
Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.
openlore-execute-refactor
Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.
openlore-plan-refactor
Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-analyze-codebase
Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.
team-repair
Re-index OKF v0.2 index.md/log.md files, derive CDR.md, rebuild .skills.json and AGENTS.md in team-ai-directives, migrate v0.1→v0.2 frontmatter, scan for rule conflicts, and verify directive freshness. Use when indexes are inconsistent, orphans are detected, after bulk changes, or for periodic team AI directives…