An Agent Skill that builds a skill with evals out of the conversation in which the agent finally got it right.
84
98%
Does it follow best practices?
Impact
96%
1.71x1 of 3 eval scenarios. Add 2 more for a full score.
Passed
No findings from the security scan
An Agent Skill that turns the conversation you are in into a new skill with evals. Use it the moment the agent has produced what you wanted after one or more corrections: it finds where the story started, captures every round, writes the skill from those corrections only, scaffolds a repository with eval suites, replays your first prompt with the skill installed, and proves the skill with runs with and without it.
It works in Claude Code and Codex and is written to be agent-independent: the folder follows the Agent Skills specification, and the only agent-specific parts (where transcripts live, how to install) are listed in transcripts.md and replay-and-prove.md. Another agent can ask you for what it cannot find.
From the Tessl registry, into the agents configured in your project:
tessl install martinfrancois/build-a-skillOr from a clone; a symlink keeps the installed skill in step with this repository.
git clone https://github.com/martinfrancois/build-a-skill.git
cd build-a-skill
# Claude Code
mkdir -p ~/.claude/skills && ln -s "$PWD/skills/build-a-skill" ~/.claude/skills/build-a-skill
# Codex
mkdir -p ~/.agents/skills && ln -s "$PWD/skills/build-a-skill" ~/.agents/skills/build-a-skillIn the conversation where the agent finally got it right:
/build-a-skill # Claude Code
$build-a-skill # Codexor in plain words: "turn these corrections into a skill". The skill then:
SKILL.md,
its examples as "not like this, like this, why" pairs, and one eval scenario per round;The template's README holds the same steps for doing them by hand.
Hosted evals use Tessl credits: as of October 2026, Free includes 1,000 per calendar month and a
paired scenario costs 20; the quality review is free. The skill checks the balance with
tessl org usage --json before the first eval run, spends credits in order (targeted scenarios,
then suites), and stops at the first stage that is not clean. When the month's credits are used
up it runs the scenarios locally against the same model for a cent or two each instead.
MIT, copyright 2026 François Martin.