Add tldr - lead with a three-line TL;DR, fold the detail - #487
Open
SurefireStudios wants to merge 1 commit into
Open
SurefireStudios wants to merge 1 commit into
SurefireStudios wants to merge 1 commit into
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Adds
tldr.It is an output-shaping skill: every response leads with a three-line TL;DR (what's true, what to do, what it costs) and the full detail is folded directly underneath rather than removed. A never-compress list keeps destructive commands, security findings, data loss, cost, verbatim error text and diffs above the fold. For subagent-to-orchestrator output it returns a parseable block instead of narration.
MIT, two markdown files, no runtime and no network calls. Off by default, turned on with
/tldr.Blind-graded against a no-skill baseline on Sonnet and Opus (16 cases x 3 trials, fidelity weighted at 25% so it cannot score well by dropping caveats): better on correctness, fidelity, actionability, safety and concision on both models. Every run is published, including the ones that failed the release gate: https://github.com/SurefireStudios/tldr/blob/main/evals/RESULTS.md