Latitude is an open-source platform for monitoring AI agents by collecting execution traces, grouping failures, dispatching coding agents to make fixes, and replaying failures to verify them. Teams use it to observe agent behavior, investigate errors, and monitor whether fixes prevent regressions. The catalogue entries include skills, instructions, and an MCP server for working with Latitude.
Borrowing it
Nothing to install: this file belongs to latitude-dev/latitude-llm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/latitude-dev/latitude-llm/development/.agents/skills/ci-watchdog/SKILL.mdgit clone --depth 1 https://github.com/latitude-dev/latitude-llmWrote 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/latitude-dev/latitude-llm/ci-watchdog)<a href="https://agentmods.dev/skills/latitude-dev/latitude-llm/ci-watchdog"><img src="https://agentmods.dev/badge/skills/latitude-dev/latitude-llm/ci-watchdog/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/latitude-dev/latitude-llm/ci-watchdog"><img src="https://agentmods.dev/badge/skills/latitude-dev/latitude-llm/ci-watchdog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00155 | $0.01541 |
| Opus 5 | $0.00077 | $0.00771 |
| Sonnet 5 | $0.00031 | $0.00308 |
| Haiku 4.5 | $0.00015 | $0.00154 |
Grade A, and why
ci-watchdog 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 9d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI Watchdog
Monitor a GitHub PR's CI checks continuously, automatically fix failures, and loop until everything passes.
Quick Start
# Watch current PR until all checks pass (auto-fix enabled)
gh pr checks --watch --fail-fast
# Watch with auto-fix loop
ci-watchdog --fix --loop
Core Workflow
The CI watchdog follows this loop:
- Monitor - Watch PR checks until they complete or fail
- Detect - Identify which checks failed
- Diagnose - Fetch logs and understand the failure
- Fix - Apply appropriate fixes to the codebase
- Commit - Stage, commit, and push fixes
- Repeat - Go back to monitoring until all pass
Usage Patterns
Basic Watch Mode
Just monitor without fixing:
gh pr checks --watch
Auto-Fix Loop (Recommended)
Monitor and automatically fix issues:
LOOP:
1. Run: gh pr checks --watch --fail-fast
2. IF checks pass → DONE
3. IF checks fail:
a. Identify failed check names
b. Fetch logs: gh run view <run-id> --log-failed
c. Analyze error patterns
d. Apply fixes to codebase
e. Commit and push
f. GOTO LOOP
Failure Patterns & Fixes
Type Errors / Lint Errors
Detection: typecheck, lint, check, pyright, tsgo failures
Common fixes:
- Read the specific file and line from error output
- Fix the type mismatch or syntax error
- Remove references to deleted/renamed properties
- Add missing imports or type annotations
Example flow:
# Check failed with pyright errors in file X
python -m pyright src/path/to/file.py 2>&1 | head -30
# → Shows specific line/column errors
# → Fix the issues
# → Commit and push
Test Failures
Detection: test check failures
Common fixes:
- Read the failing test file
- Update test assertions to match new behavior
- Remove obsolete test cases
- Fix test data/setup issues
Example flow:
# Tests failed
# Read the test file mentioned in error
# Update assertions or remove obsolete tests
# Commit and push
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.
- 9d ago First seen · 248 lines · 155 tokens per session scan A 457b78b4a6ed
ci-watchdog is a skill published in the GitHub repository latitude-dev/latitude-llm (4,632 stars, last pushed today), licensed MIT. It adds 155 tokens to every session and 1,541 once invoked, about $0.0008 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 2026-08-30.
Other skills, from other repositories
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trulens-instrumentation
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trulens-notebook-execution
Execute and display Jupyter notebooks for TruLens demos and quickstarts.
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
trulens-blocking-guardrails
Configure and use feedback functions as runtime blocking guardrails.
trulens-dataset-curation
Create and curate evaluation datasets with ground truth for TruLens.