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Gait Session Tracking

Community
automateyournetwork
gait-session-tracking

GAIT session lifecycle management - branch creation, turn recording, audit logging for every NetClaw operation. Use when starting a new NetClaw session, recording a health check or config change, pinning a pre-change baseline, or viewing the audit trail for a troubleshooting session.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill namegait-session-tracking
Stars
680
Forks
189
Bundled files
Instructions only
LicenseApache-2.0
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 automateyournetwork on GitHub. Read the source before you install it.

Installation

Install the Gait Session Tracking 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.
    https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/gait-session-tracking
  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/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/gait-session-tracking .claude/skills/gait-session-tracking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gait Session Tracking 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 Gait Session Tracking 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 Gait Session Tracking 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.

GAIT Session Tracking

This skill is mandatory. Every NetClaw session MUST begin with gait_branch and end with gait_log.

How to Call the Tools

The GAIT MCP server provides the lifecycle tools below (discover tools/list for the complete installed inventory). Call them via mcp-call:

Check Repository Status

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_status '{}'

Returns current branch, uncommitted changes, and repository state.

Initialize a New GAIT Repository

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_init '{"path":"/absolute/path/to/project"}'

Creates a new GAIT repository if one does not already exist. Supply the actual absolute project path; do not run against the filesystem root. Run once during initial setup, then verify gait_status names the intended root.

Create a New Branch (SESSION START)

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_branch '{"name":"health-check-r1-2026-02-21"}'

Creating a branch does not switch to it. Call gait_checkout with the same name and verify gait_status before recording turns. Every session begins here. Use a descriptive branch name that includes the action type, target device(s), and date. Examples:

  • health-check-r1-2026-02-21
  • ospf-troubleshoot-core-2026-02-21
  • config-deploy-acl-update-2026-02-21
  • netbox-reconcile-site-hq-2026-02-21
  • security-audit-dmz-2026-02-21

Switch to an Existing Branch

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_checkout '{"name":"health-check-r1-2026-02-21"}'

Use this to resume a previous session or switch context between parallel investigations.

Record an AI Turn (PRIMARY RECORDING TOOL)

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"User asked to check CPU on R1","assistant_text":"Ran show processes cpu sorted. CPU 5-min avg: 12%. Status: HEALTHY.","artifacts":[{"path":"show_proc_cpu_r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."}]}'

Record a turn after every significant action. Each turn captures:

  • user_text: What was asked or what triggered the action
  • assistant_text: What data was collected and what the result was
  • artifacts: List of {"path": "...", "content": "..."} objects (optional; use sanitized content only)

View Commit History (SESSION END)

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_log '{}'

Every session ends here. Display the full audit log before concluding. This provides the user with a complete record of everything that happened.

Show Commit Details

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_show '{"commit":"HEAD"}'

Inspect a specific commit to see its full content. Use HEAD, HEAD~1, or a commit hash.

Pin Important Commits

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_pin '{"commit":"HEAD","last":false,"note":"pre-change-baseline"}'

Mark critical moments in a session so they can be easily found later. Common pin labels:

  • pre-change-baseline -- state before any modifications
  • post-change-verified -- state after changes are validated
  • critical-finding -- an important discovery during investigation
  • rollback-point -- safe state to return to if needed

Summarize and Squash Turns

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Session summary","assistant_text":"Summarize findings, changes, verification and remaining gaps.","note":"session-summary"}'

Do not squash immutable operational audit history. Record an additive summary using gait_record_turn instead. The server exposes squashing for other use cases; its availability is not authorization to rewrite this project's audit trail.

Mandatory Session Lifecycle

Every NetClaw session follows this exact lifecycle:

1. Session Start -- Create Branch

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_branch '{"name":"ACTION-TYPE-TARGET-DATE"}'
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_checkout '{"name":"ACTION-TYPE-TARGET-DATE"}'
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_status '{}'

2. During Session -- Record Every Turn

After each meaningful action (show command, config change, API call, verification), record a turn:

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"WHAT_WAS_ASKED","assistant_text":"WHAT_DATA_COLLECTED_AND_WHAT_CHANGED_AND_VERIFICATION_RESULT","artifacts":[]}'

Record format guidelines:

  • user_text: State clearly what the user asked or what triggered the action
  • assistant_text: Include three parts:
    1. What data was collected (commands run, API responses)
    2. What changed (config applied, ticket created, NetBox updated)
    3. Verification result (HEALTHY/WARNING/CRITICAL, pass/fail, before/after diff)
  • artifacts: List any files generated (logs, configs, diagrams, reports)

3. Session End -- Display Log

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_log '{}'

Always show the session log to the user so they have a complete record.

Recording Examples by Skill Type

Health Check Turn

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Run full health check on R1","assistant_text":"Collected: show version, show processes cpu sorted, show processes memory sorted, show ip interface brief, show interfaces, show ntp associations, show logging. Results: CPU 12% HEALTHY, Memory 45% HEALTHY, Interfaces 4/5 up WARNING (Gi2 down), NTP synced HEALTHY, no critical log patterns. Overall: WARNING.","artifacts":[{"path":"health-report-r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."}]}'

Configuration Change Turn

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Apply ACL update to block 192.168.50.0/24 on R1 Gi1","assistant_text":"Pre-change: captured running-config. Applied: ip access-list extended BLOCK-LIST, permit/deny entries. Post-change: verified ACL in show access-lists, tested with ping from blocked subnet -- dropped as expected. Change verified successfully.","artifacts":[{"path":"pre-change-config-r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."},{"path":"post-change-config-r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."},{"path":"acl-diff.txt","content":"Sanitized evidence reference; raw evidence retained locally."}]}'

Troubleshooting Turn

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Investigate OSPF adjacency failure between R1 and R3","assistant_text":"Checked show ip ospf neighbor on R1 -- R3 missing. Checked show ip ospf interface on both -- area mismatch: R1 area 0, R3 area 1 on shared link. Root cause identified: area misconfiguration on R3 Gi0/1.","artifacts":[{"path":"ospf-neighbor-r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."},{"path":"ospf-interface-r1.txt","content":"Sanitized evidence reference; raw evidence retained locally."},{"path":"ospf-interface-r3.txt","content":"Sanitized evidence reference; raw evidence retained locally."}]}'

NetBox Reconciliation Turn

bash
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Reconcile R1 interfaces against NetBox","assistant_text":"Live state: 5 interfaces discovered via show ip interface brief. NetBox state: 4 interfaces documented. Drift detected: Gi5 exists on device but missing from NetBox. Gi2 documented in NetBox but admin-down on device. Reconciliation report generated.","artifacts":[{"path":"reconcile-report-r1.json","content":"Sanitized evidence reference; raw evidence retained locally."}]}'

Integration with ALL Other Skills

GAIT session tracking is used by every other NetClaw skill for audit compliance:

  • pyats-health-check -- Record each health check step and overall results
  • pyats-topology -- Record discovered neighbors and topology changes
  • pyats-security -- Record audit findings by severity
  • pyats-config-mgmt -- Record pre-change baseline, change applied, post-change verification
  • pyats-troubleshoot -- Record each investigation step and root cause
  • pyats-routing -- Record routing table snapshots and protocol state
  • netbox-reconcile -- Record drift detection and remediation actions
  • drawio-diagram -- Record diagram generation with source data reference
  • markmap-viz -- Record mind map creation with underlying data
  • rfc-lookup -- Record RFC references used during investigation
  • wikipedia-research -- Record protocol research context
  • servicenow-incidents -- Record ticket creation and updates
  • nvd-cve -- Record vulnerability findings and remediation tracking

When to Use

Always. Every NetClaw session. No exceptions. This is the audit backbone of the system.

Failure Behavior

  • If a tool call fails with an authentication or connection error, check that GAIT_MCP_SCRIPT is set and valid before assuming a data or device problem.
  • On a tool error (timeout, unreachable host, malformed response), report the failure and its error message directly to the user rather than fabricating or guessing at results.
  • Branch, checkout, initialization and turn recording mutate audit state. On timeout, use gait_status/gait_log to establish whether the mutation succeeded before retrying; a blind retry can duplicate a record. Read-only lookups may be retried once after confirming connectivity.
  • A transport-level response is not proof of success: inspect MCP isError and the returned ok value. Report failure without inventing a successful audit record.
  • Tool schemas vary by installed server revision. Discover tools/list and compare with these examples; do not guess alternate argument names. These examples match the inspected gait_mcp lifecycle signatures (spec 124).

Frequently asked questions

What does the Gait Session Tracking AI skill do?

GAIT session lifecycle management - branch creation, turn recording, audit logging for every NetClaw operation. Use when starting a new NetClaw session, recording a health check or config change, pinning a pre-change baseline, or viewing the audit trail for a troubleshooting session.

Why use Gait Session Tracking on TypingMind?

Because you install it once and use it with any model. Gait Session Tracking 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 Gait Session Tracking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/gait-session-tracking. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gait Session Tracking?

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 Gait Session Tracking?

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

Is the Gait Session Tracking AI skill free?

Yes. It is published on GitHub by automateyournetwork under the Apache-2.0 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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