runs skillA
runs is agent-read markdown (skill) from ai-analyst-lab/ai-analyst: Browse, inspect, compare, and clean up past pipeline runs. This skill is your window into analysis history — every `/run-pipeline` execution creates a tracked run with its own working directory, outputs, and state. Use this skill whenever the user wants to see what analyses have been executed, review past work, compare different approaches to the same problem, or clean up old runs. Trigger on phrases like "/runs", "show me my past analyses", "what pipeline runs do I have?", "list my pipeline run.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Skill: Runs ## Purpose Browse, inspect, compare, and clean up past pipeline runs. Each run is a self-contained directory under `working/runs/` with its own working files, outputs, and pipeline state. ## Architecture: Understanding the Two-Source System The AI Analyst tracks analysis work in **two independent locations**: 1. **Active Pipeline Runs** (`working/runs/`) - Contains in-progress, recently completed, or failed pipeline executions - Each run is a directory with `pipeline_state.json` tracking agent progress - Used for resuming interrupted work and debugging issues - Cleaned up periodically (30-day retention by default) 2. **Archived Analyses** (`.knowledge/analyses/`) - Contains completed analyses that have been formally archived - Includes validation grades, key findings, and links to outputs - Permanent knowledge base for organizational learning - Survives even after working/runs/ cleanup **Why this matters:** A user asking "what analyses have I run?" expects to see BOTH in-progress work (working/runs/) AND completed archived work (.knowledge/analyses/). Missing either source gives an incomplete picture. ## When to Use …
Read the whole file at its exact version.
How to install
mdr add ai-analyst-lab/ai-analyst/runs@git:20260827.7ff2e25mdr add ai-analyst-lab/ai-analyst/runs@sha256:344a02d929991412Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_gomduz5b6m73r3to)
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Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (10103 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ai-analyst-lab/ai-analyst · 302 stars · license MIT · pushed 2026-09-22 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_gomduz5b6m73r3to GET https://markdownregistry.com/api/v1/resolve?ref=ai-analyst-lab/ai-analyst/runs GET https://markdownregistry.com/api/v1/blob/344a02d929991412dd86b4f011915a306399a70bcd1f4b1aad19acfc36b2e0e1
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