product-plugins

Add an endpoint to the backend.

Find and call Tessl API endpoints. Use this WHENEVER you need a Tessl REST API endpoint — to discover which endpoint does something, to learn an endpoint's path, parameters, request body shape, or response shape, or to actually call the Tessl API. Reach for this skill INSTEAD OF reading the raw `openapi.json` spec.

Use once there is a confirmed composition plan, to create it. Scaffolds the plugin, authors or restructures the skills, and adds the rules, hooks, MCP servers, or commands the plan called for. Uses the CLI to scaffold the plugin, then authors the planned composition.

Start here for Tessl Code Review. Use when someone mentions Tessl Code Review or Tessl's AI review, wants automated review of pull requests in a repository, asks to review a diff, branch, or pull request ("review this change", "check this diff for security issues", "what would Code Review say"), asks what Tessl Code Review does, or asks for something and it is not yet clear which Code Review job it is. Works out whether they want to install it on a repository, run a review, change what reviews catch, change which lenses run on which paths, or answer a review's findings. Installing is the Tessl Review GitHub App, which this skill links the user to; the other jobs hand off to create-code-review-lens, respond-to-code-review, or the tessl code review command. The review-* skills in this plugin are lenses that tessl code review runs as reviewer instructions; do not follow one directly to review code, run the command instead.

Run task evals across multiple Claude models, compare results side-by-side, and optimise. Use when you want to benchmark a skill across models, compare haiku vs sonnet vs opus performance, run multi-model comparison or benchmark reports, identify model-specific gaps versus universal plugin gaps, evaluate whether a skill works for all model tiers, or validate a skill before publishing it to the registry.

Author a review lens for Tessl Code Review, the reviewer skill that decides what a review looks for in a diff. Settles the review question and the bar a finding has to clear, drafts the lens, runs it against changes that should and should not trip it, backtests it against changes that already carry review feedback, and packages it as a reference a review can select, either pinned to the version that was backtested or following the newest published version. Use when someone wants their code review to catch something it misses, to stop flagging something it should not, or to write, fork, tune, or debug a code review lens. Not for `tessl review` rubrics, which score skill quality rather than code.

Use when someone wants to turn a problem, a messy or sprawling skill, or some existing work into well-shaped reusable context for their agent. Triggers include "help me make a skill for X", "my skill is a mess, sort it out", "I want to get more out of Tessl with this skill", or "package this so my team can use it". Runs the arc - understand the problem, gather the artifacts, plan the right composition, build it - then points to eval as a separate next step.

Scaffold and author a custom Tessl reviewer plugin, by forking the default rubric or building one from scratch. Fetches the published default Tessl review rubric (tessl/default-skill-review) so you can read and tweak it, scaffolds the plugin directory, writes rubric files and config.json, and validates with tessl review run. Use when the user wants to create or customise a review plugin, fork or tweak the default reviewer rubric, change scoring weights, add custom judges, or build a domain-specific rubric for tessl review.

Database administration tasks including schema changes, backups, monitoring, and user access management.

Use during build when a skill or a problem is too big for one skill - a sprawling SKILL.md doing several jobs, or a large problem the user wants a single skill for. Splits it into focused, independently-verifiable skills, with a plugin as the superstructure that holds them together.

Derive custom review-plugin judges, rubrics, and scoring criteria from evidence — existing skills, PR review feedback, and accumulated agent logs — by finding where agents needed correction or skills failed to activate, then translating those patterns into scoring dimensions and anchors. Hands the rubric design to create-review-plugin for scaffolding. Use when the user wants a custom skill reviewer or scoring rubric grounded in how their agents actually behave — to grade skills consistently, or build review criteria from past PR feedback and agent logs.

Use at the start of creating context, to understand the problem the user is solving and take stock of what they already have. The user may arrive with an existing skill or plugin, code, a written prompt or transcript, or nothing but a pointer ("go look at my PRs"). Gathers what exists and fills the gaps - by asking or by hunting - until there is enough to plan a composition.

Review and improve your skill with actionable recommendations. Reviews the whole bundle, validates syntax and references, explains rubric, shows before/after scores, and edits the SKILL.md and its reference docs. Use when reviewing skill quality, improving a SKILL.md or its reference files, checking scoring dimensions and quick wins, auditing progressive disclosure and orphaned bundle files, generating improvement recommendations, running a post-edit quality audit, creating approval-gated change proposals, or automating the skill review workflow. For the full optimization cycle (review + evals + improve), use `optimize-skill-performance-and-instructions`.

Run task evals, analyze results, diagnose failures, apply targeted fixes, and re-run to verify improvements. Use when debugging evaluation scores, fixing failing or regressed criteria, analyzing why eval criteria pass or fail, reviewing eval rubric quality and redundant criteria, tracking before/after score improvements, editing plugin content to fix specific failing behaviors, or improving agent performance based on eval evidence.

Run the full optimization cycle for a plugin — review best practices, generate eval scenarios, run BOTH activation evals (does the skill self-activate?) and content evals (does the plugin help solve tasks?), diagnose gaps, fix, and re-run until scores improve. Use when someone says "optimize my skill", "improve my plugin", "run evals", "benchmark my plugin", or wants to measure and improve how well a plugin helps agents solve tasks.

Use once the problem and artifacts are understood, to decide the right shape for the context. Produces a short composition plan - a single skill for simple cases, or a plugin with the right mix of skills, rules, hooks, MCP servers, and commands - and confirms it with the user before anything is built.

Promote the pending facts of one project memory topic into the topic's note: merge restatements, apply newer facts and retractions, drop what can be checked live, and keep each line attributed and the note under 48 KB, then save the note, which clears the batch. Use when Tessl's memory promotion job launches a run for a topic and passes it as the input TOPIC.