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Ln 71 Operations Investigator

Community
levnikolaevich
ln-71-operations-investigator

Diagnoses incidents from operational evidence and proposes recovery; does not change live systems.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-71-operations-investigator
Stars
565
Forks
84
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by levnikolaevich on GitHub. Read the source before you install it.

Installation

Install the Ln 71 Operations Investigator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/operations-suite/skills/ln-71-operations-investigator .claude/skills/ln-71-operations-investigator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 71 Operations Investigator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ln 71 Operations Investigator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ln 71 Operations Investigator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Operations Investigator

Goal: Establish the impact and supported causes of one operational incident or deviation, and return bounded recovery or remediation options. Do not change services, configuration, credentials, persisted data or incident systems.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Incident boundaryUser report, service ownership and operational objectivesExplicit bounded assumptions and missing evidence
Operational evidenceAuthorized read-only metrics, logs, traces and deployment/change historySanitized exports with timestamps and provenance
Hypothesis checksExisting telemetry queries and safe offline reproductionStatic causal trace; no production probes or load without authority

Domain Rules

  • Separate symptoms, contributing conditions, supported cause and unresolved hypotheses. Correlation with a deployment is not causal proof.
  • Bound telemetry queries by incident window and scope; redact sensitive values and avoid unbounded queries or costly live experiments.
  • Urgency does not authorize recovery mutations. Describe immediate safe options separately from root-cause remediation and prevention.

Checklist

1. Establish the Incident

  • Resolve affected service, environment, users, symptoms, time window, timezone, severity evidence and investigation authority.
  • Identify baseline service objectives, normal behavior, ownership and current incident/recovery status.
  • Record evidence availability, retention, sampling and clock uncertainty before interpreting absence of events.
  • Identify already attempted mitigations and source/deployment/configuration changes within the causal window.

2. Collect and Correlate Evidence

  • Build a timeline from observed events with source identities and timestamps; distinguish event time from ingestion time.
  • Measure impact on requests, users, data correctness and dependencies where evidence permits; do not fabricate denominators.
  • Trace the failure across entrypoints, dependencies, state and resource boundaries using correlated evidence.
  • Inspect material errors, saturation, latency, retries, timeouts and configuration changes without assuming one universal failure pattern.
  • Preserve conflicting and missing signals, including sampling or missing instrumentation that can change the conclusion.

3. Test Explanations and Recovery Options

  • Rank plausible causal explanations and identify an observation that could refute each material candidate.
  • Use safe existing evidence or authorized offline reproduction to distinguish alternatives; do not execute live fixes.
  • Identify immediate containment and recovery options with prerequisites, expected effect, risk and verification signals.
  • Separate reversible mitigations from irreversible data or infrastructure actions and flag missing authority explicitly.
  • Define the smallest owning remediation and prevention scope supported by the evidence; avoid unrelated hardening.

4. Report Operational Findings

  • State whether the cause is supported, narrowed to hypotheses, or unknown, with evidence strength and residual ambiguity.
  • If recovery evidence exists, verify the observed identity and health window without claiming this investigation performed recovery.
  • Return the incident timeline, bounded action options and next evidence steps without changing external incident records.
  • Name observability gaps that prevented diagnosis and the concrete signal needed to resolve each.

Verdict

  • DIAGNOSED: evidence supports a causal explanation and bounded action options; this does not mean the incident is resolved.
  • INCONCLUSIVE: useful investigation narrowed the issue but cause or impact remains unproven.
  • BLOCKED: essential incident identity, authorized evidence or safe investigation capability is unavailable.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Incident/environment, impact and measurement limits, timeline, source identities, causal and rejected hypotheses, recovery options and verification, observed current status and exact remaining evidence needs.

Frequently asked questions

What does the Ln 71 Operations Investigator AI skill do?

Diagnoses incidents from operational evidence and proposes recovery; does not change live systems.

Why use Ln 71 Operations Investigator on TypingMind?

Because you install it once and use it with any model. Ln 71 Operations Investigator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ln 71 Operations Investigator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/operations-suite/skills/ln-71-operations-investigator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 71 Operations Investigator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ln 71 Operations Investigator?

As many as you like. As long as a model supports skills, you can use Ln 71 Operations Investigator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 71 Operations Investigator AI skill free?

Yes. It is published on GitHub by levnikolaevich under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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