task-intake
Skill0 starsChanged 9 days agoMITView on GitHub
Resolve materially unclear requests into a focused internal task brief before dependent execution. Use when a request is vague, under-specified, or missing context, and when the model must load the correct knowledge from its knowledge base. Restructures the request into goal / context / constraints / acceptance criteria so the model picks the right approach and activates the right knowledge.
4 other projects use a version of this.
Adopted by
Most recently taken up by
- infragoodboys/claude-skillstheir own version, first seen Oct 13, 2026
- GodMaking/makecrewtheir own version, first seen Oct 11, 2026
- gootf/software-agent-soptheir own version, first seen Aug 30, 2026
- yeaight7/agent-powerupstheir own version, first seen Jul 27, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- yeaight7/agent-powerups★ 6same name
- gootf/software-agent-sop★ 2same name
- GodMaking/makecrew★ 1same name
- infragoodboys/claude-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- chatgpt/AGENTS.mdAGENTS.md
- opencode/AGENTS.mdAGENTS.md
- zcode/AGENTS.mdAGENTS.md
- claude/CLAUDE.mdCLAUDE.md
- agent-doc-authoringSkill
- agent-experienceSkill
- ai-image-promptsSkill
- api-behavior-flowSkill
- architecture-and-performanceSkill
- behavioral-testingSkill
- behavior-auditSkill
- browser-tabs-uiSkill
- code-qualitySkill
- context-compactSkill
- design-principlesSkill
- design-to-deliverySkill
- design-validationSkill
- e2e-test-authoringSkill
- engineering-reviewSkill
- excalidrawSkill
- external-ai-consultSkill
- google-omni-videoSkill
- llm-agent-tool-disciplineSkill
- parallel-subagentsSkill
- qa-test-scenariosSkill
- reliable-executionSkill
- research-and-reuseSkill
- server-security-hardeningSkill
- session-handoffSkill
- shadcn-librarySkill
- sudo-handoffSkill
- tauri-v2-docsSkill
- test-failure-triageSkill
- user-behavior-flowSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
2
Distinctive
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 38
Never execute from the raw request alone — always from the brief.
- Distinctive: line 41
Keep the brief to a few lines.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Task Intake — Universal
Purpose
Shape the input before execution. A raw request usually lacks the context that tells the model which knowledge to load — so intake converts it into a structured brief: goal, relevant context, constraints, acceptance criteria, verification. The brief dictates what knowledge gets activated and what the output must look like.
When to use
- The request lacks a goal or constraints that materially change the result ("make this better", "fix it", "build the thing"). A one-line request with sufficient session/repository context does not require clarification.
- The task sits in a domain with many valid approaches (styling, architecture, APIs, algorithms).
- The request implies knowledge that was never stated (framework versions, project conventions, existing patterns).
Preconditions
- The raw request is available to re-read — never shape from memory of it.
- The repo is inspectable to fill context that the request did not state.
Workflow
- Extract the goal. What must be true when the work is done? If the request does not say, that is the first gap to close.
- Identify missing context that changes the approach — stack, versions, existing patterns, constraints, intended users.
- Classify each gap: already stated / discoverable in the repo / must ask the user.
- Fill what the repo answers. Inspect code and docs before asking anything.
- Ask only for gaps the repo cannot answer and that change the outcome — one focused question, not a questionnaire.
- Write the brief internally: Goal · Context loaded (the facts that drive the approach) · Constraints (what not to touch) · Acceptance criteria (how we know it is done) · Verification. Show it only when requested or useful for a consequential decision; it is not an approval form.
- Execute the brief through the
reliable-executionworkflow.
Rules
- Never execute from the raw request alone — always from the brief.
- Load facts from the repo before asking the user; ask only for what the repo cannot answer.
- The brief must name acceptance criteria — without them every output is equally "done".
- Keep the brief to a few lines. It is a working contract, not documentation.
Common mistakes
- Executing a vague request as-is and hoping ("improve" without defining what improvement means).
- Asking the user questions the repo already answers.
- Writing a brief that restates the request instead of adding missing context and constraints.
- Skipping acceptance criteria — this is the point where the model starts guessing.
Validation
- Material decisions trace to the request, repository evidence or a user answer. Safe reversible defaults may be inferred and stated when useful; do not prohibit all assumptions or ask about routine implementation choices.
- The brief drove the execution; every statement in it traces to a source.
- Executing from the brief alone (without the raw message) reproduces the same outcome.
Does it work?
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- It worked
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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.

