Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add pitimon/8-habit-ai-dev/plugin install 8-habit-ai-devWrote 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/pitimon/8-habit-ai-dev/operational-state)<a href="https://agentmods.dev/skills/pitimon/8-habit-ai-dev/operational-state"><img src="https://agentmods.dev/badge/skills/pitimon/8-habit-ai-dev/operational-state.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 5.1 | $0.00140 | $0.01606 |
| Opus 5 | $0.00070 | $0.00803 |
| Sonnet 5 | $0.00028 | $0.00321 |
| Haiku 4.5 | $0.00014 | $0.00161 |
Grade A, and why
operational-state 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operational State
Habits: H1 (Be Proactive) + H5 (Seek First to Understand) + H8 (Find Your Voice) | Anti-pattern: treating every operational signal as either "fix now" or "close"
When to Use
- An alert, daily-fix item, health check, or ops finding needs classification before action.
- The system is currently healthy but the signal recurs or could become noisy.
- A pod/service says Running, but a mount path, dependency, endpoint, or user-visible check is unhealthy.
- Ownership is unclear and the next step may be AppDev, Registry, vendor, or another team handoff.
- A known issue should stay visible without counting as an active incident.
- You need to distinguish report hygiene from a cluster/service mutation.
Use this before /deploy-guide for operational mutations, before /post-mortem for fixed incidents, and before /management-talk when leadership needs state wording.
When to Skip
- The finding already has a validated root cause, approved fix, rollback plan, and current impact; go to
/deploy-guideor/post-mortem. - The work is only a wording/status update with no operational ambiguity.
- The user explicitly asks for an incident RCA after a validated fix; use
/post-mortem. - The problem is a hard unknown-root-cause bug; use
/diagnosefirst.
State Decision Process
- Capture the signal: symptom, source, timestamp, environment, owner candidate, and user/customer impact if any.
- Check current reality: fresh status, logs/events/metrics, and one independent confirmation when root cause or recovery is claimed.
- Choose exactly one current state from the table in
reference.md. - Name the evidence threshold that would promote, demote, close, or hand off the finding.
- Separate action classes:
- report hygiene: docs, ticket wording, owner routing, alert wording
- read-only validation: logs, metrics, dry-run, rendered config, endpoint check
- mutation: deploy, restart, scale, config write, secret rotation, data repair
- Gate mutations: if production/shared infrastructure is involved, require explicit human approval plus rollback/mitigation notes before any write.
What ships with it
1 file 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.
- 8d ago First seen · 115 lines · 140 tokens per session scan A 9ab4e8ec9a00
operational-state is a skill published in the GitHub repository pitimon/8-habit-ai-dev (3 stars, last pushed 1mo ago), licensed MIT. It adds 140 tokens to every session and 1,606 once invoked, about $0.0007 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
diagnose-hard-bug
Hard-bug diagnosis: tight loop, ranked hypotheses.
diagnosing-bugs
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
root-cause
Find the mechanism behind a failure instead of patching its symptom - reproduce first, one variable per experiment with the prediction written before the run, exit by naming the mechanism and pinning it with a failing test. Use for a bug, an unexplained red test, or a failure that will not reproduce.
joycraft-bugfix
Structured bug fix workflow — triage, diagnose, discuss with user, write a focused spec, hand off for implementation.
joycraft-add-fact
Invoked by session-end or the human after a fact surfaces — route it to the correct context document (production map, dangerous assumptions, decision log, institutional knowledge, troubleshooting).
stress-test
Use when a design, plan, or decision needs adversarial scrutiny before proceeding. Interrogates every branch of the decision tree, providing recommended answers and forcing explicit agreement or pushback. Triggers on "grill me", "stress test this", "poke holes", "challenge this design", or when…