loom-writing
Skill87 starsChanged 3 days agoApache-2.0View on GitHub
Plain technical English for commit messages, PR and issue text, review comments and replies, code comments and docs, with a checker for filler words and internal details. Use before writing or posting any of them, or when asked to make text read less generated. Owns wording, not sections.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- llms.txtllms.txt
- loom-address-reviewSkill
- loom-benchSkill
- loom-changeSkill
- loom-debugSkill
- loom-deploySkill
- loom-dev-setupSkill
- loom-file-issueSkill
- loom-investigate-ciSkill
- loom-pre-pushSkill
- loom-releaseSkill
- loom-reviewSkill
- loom-ship-prSkill
- loom-codeSkill
- loomSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
Plain words: Write the word a senior engineer says out loud in a design review.
- Distinctive: line 80
Write the text to a file, then:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Writing in Loom
Readers are maintainers who skim dozens of PRs, issues and comments a day. A text earns its place by saying something the diff, the code or the thread does not already say, in the fewest words that are still exact.
Rules
- Lead with the fact. The first sentence is the claim, the finding or the answer. No preamble ("This PR aims to…", "I wanted to raise…"), no summary at the end that repeats it.
- Concrete over general. Name the op, field, file, setting, query or command. "Search drops pinned rows when
limit< 3" beats "fixes an edge case in search". - Every adjective has evidence. "Fast", "robust", "safe", "scalable" need a number with the command that reproduces it, or the mechanism. Otherwise delete the adjective.
- Say why, once. A commit says why; a code comment says why the code is the way it is; neither narrates what changed ("now", "previously", "updated to").
- Present tense, the system as it is. History, dates and the path you took belong in the PR body, not in code, docs or comments.
- Plain words. Write the word a senior engineer says out loud in a design review. The table below lists the words that mark text as generated.
- No performance of effort or emotion. No "Great catch!", "I apologize", "Happy to help", "Let me know if…", "I have carefully reviewed…". A reply states what changed and where.
- Formatting serves skimming. Bullets for parallel items, a table for options, code formatting for identifiers. No headings in a three-line comment, no bold on every other phrase, no emoji.
- Public text names no host, internal path, namespace, person, customer or production count (
tests/unit/test_public_copy.pyenforces the repository side; you enforce the GitHub side).
Words to drop
| Instead of | Write |
| leverage, utilize | use |
| robust, comprehensive, seamless, powerful | the property itself: "retries twice", "covers every read op" |
| ensure (as filler) | the check: "fails closed", "asserts that…" |
| enhance, improve (with no measure) | the change and its number |
| streamline, simplify (as a claim) | what was removed |
| delve, dive into, explore | read, check, measure |
| crucial, critical, essential, vital, key (as emphasis) | delete, or say what breaks without it |
| it's worth noting, note that, importantly | delete; state the point |
| in order to | to |
| a variety of, various, numerous | the count, or the list |
| facilitate, empower, enable (as filler) | the verb for what actually happens |
| going forward, at this point in time | delete |
| addresses, handles (vague) | fixes / rejects / retries / returns |
| holistic, cutting-edge, state-of-the-art, best-in-class | delete |
Examples
Commit message:
Bad: Enhance search robustness by implementing comprehensive handling of edge cases
Good: Search keeps pinned rows when limit is below the pin count
The pin pass ran after the limit, so a limit of 2 with three pins dropped one.Review comment:
Bad: Great work overall! One small thing worth noting: it might be a good idea to
consider potentially handling the case where the cursor could be empty.
Good: P2 server/memory/workers/usage_fold.py:88 — an empty cursor re-reads from the start, so a
worker restart re-emits every event. Treat "" as "no cursor" and test the restart.Reply to a review comment:
Bad: Thanks so much for the thorough review! I've gone ahead and addressed this.
Good: Fixed in 3f2a9c1: the cursor is now stored before the batch is emitted; the new test
restarts the worker halfway through a pass.Code comment:
# Bad: Robustly handle the case where the token might be missing (updated per review)
# Good: Revocation is checked before the cache: a revoked token must not hit a warm entry.Check before posting
Write the text to a file, then:
python3 .claude/skills/loom-writing/scripts/check_prose.py <file>... # prints path:line, the phrase and a replacement; exit 1 on a hitIt flags the rows of the table that are filler in every use (the FILLER list in the script, which also holds rule 7's phrases), plus home and host paths, private addresses, internal host names and secrets, which it checks inside code blocks too, because pasted logs are where they leak. The other rows are yours to judge. Fix every hit or keep it on purpose (a quoted error message, a term of art such as "critical section"). Then read the text once as the reviewer would and cut any sentence that changes nothing for them.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

