Scan a repository to surface actionable findings about agent performance. Analyzes source code, git history, GitHub data, agent logs, and agent context, then synthesizes cross-referenced findings with targeted actions informed by Tessl product awareness. Supports incremental multi-developer contributions and produces a self-contained HTML report.
70
88%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
You receive a set of deduplicated, prioritized findings from an insight scan. Your job is to generate a targeted, specific action for each finding — the single best recommendation for addressing that finding.
Read the shared reference files:
action object shape and valid action typesResolving reference paths: The links above use relative paths (
../../references/...) that work when this skill is read from its tile directory. If those paths do not resolve (e.g. when activated via a.claude/skills/symlink), find the shared references at.tessl/tiles/*/agent-insight-experiment/references/relative to the repository root.
You receive findings as a JSON array (via a temp file or inline in your prompt). Each finding has:
id, category, subcategory, title, descriptionevidence[] with locations and detailsimpact and effort scoresdata_sources[]The findings do not yet have an action field — that's what you're generating.
Before generating any actions, read through every finding to understand the full picture. This helps you:
For each finding, produce an action object:
{
"title": "<concise imperative — what to do>",
"description": "<why this helps and what it involves>",
"type": "<one of the valid action types>",
"effort_size": "<pebble | stone | rock | boulder>",
"example_fix": "<optional — concrete example>"
}Consult tessl-product-context.md for the full decision framework. Key heuristics:
Prefer create_plugin when:
Prefer update_plugin when:
Prefer remove_plugin when:
Prefer simpler types (add_rule, create_context, add_docs, etc.) when:
Prefer code-level types (update_code, refactor, add_tests) when:
Bad: "Add documentation for the auth library." Good: "Create a Tessl plugin for packages/auth with: (1) a skill for the login/logout flow, (2) rules for token refresh patterns, (3) API docs covering all 12 exported functions. Install via tessl.json so all team members' agents get the context automatically."
Bad: "Fix the inconsistent naming." Good: "Add a rule to .cursor/rules/naming.md specifying that all API route handlers use camelCase and all database column names use snake_case. Reference the existing patterns in apps/backend/src/routes/ as the canonical examples."
Use rock sizing consistently:
| Size | Effort Score | Meaning |
|---|---|---|
| Pebble | 1-2 | A few minutes: flip a config, add a one-line rule |
| Stone | 3-4 | Under an hour: write a doc, create a simple skill |
| Rock | 5-7 | Hours to a day: create a plugin, refactor a module |
| Boulder | 8-10 | Days+: major refactoring, comprehensive plugin with evals |
A create_plugin action is typically a rock (basic plugin) or boulder (plugin with comprehensive evals and multiple skills). A simple add_rule is a pebble or stone.
Return the findings array with each finding now containing an action field. Write the result to the same temp file path you received, or return it inline if working within a subagent context.
Do not modify any other fields on the findings — only add the action object.
Before returning:
action with all required fields (title, description, type, effort_size)type values are from the valid set in findings-schema.mdeffort_size values are valid rock sizescreate_plugin, update_plugin, remove_plugin) are used appropriately per tessl-product-context.md