agentleFS
Sign inStart free
prestariusprestarius/jet-skills-stack/skills/estimate/SKILL.md

estimate

Skill1 starsChanged 2 months agoMITView on GitHub

Estimate effort for a feature, plan, or backlog with explicit assumptions and confidence ranges — never a single confident number. Use whenever the user asks "how long will this take", "estimate this", "size the backlog", "t-shirt sizes for these", or a plan/issue set needs effort attached before commitment.

28 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use team-audio, playtest-report and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • CoralGameSkill

    team-audio

    Orchestrate audio team: audio-director + sound-designer + technical-artist + gameplay-programmer for full audio pipeline from direction to implementation.

    Also in 13 projects that use this

  • CoralGameSkill

    playtest-report

    Generates a structured playtest report template or analyzes existing playtest notes into a structured format. Use this to standardize playtest feedback collection and analysis.

    Also in 12 projects that use this

  • lilangMaxSkill

    asset-spec

    Generate per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.

    Also in 10 projects that use this

  • lilangMaxSkill

    create-control-manifest

    After architecture is complete, produces a flat actionable rules sheet for programmers — what you must do, what you must never do, per system and per layer. Extracted from all Accepted ADRs, technical preferences, and engine reference docs. More immediately actionable than ADRs (which explain why).

    Also in 10 projects that use this

  • +3 more from CoralGame
  • +5 more from lilangMax

The file

Create a free account to mount
Files
SKILL.md

Produce estimates a stakeholder can trust because the uncertainty is visible, not hidden.

  1. Decompose first. Break the work into independently estimable items (reuse the Epic / US / Task numbering from the working agreement if the input is a backlog; if items are too vague to size, say which and why). Read ./CONTEXT.md for the team's units and velocity conventions.
  2. Estimate per item, as a range. Three-point (optimistic / likely / pessimistic) for anything consequential; T-shirt sizes only when the audience asked for rough order. State the unit explicitly (person-days, sprints) — never bare numbers.
  3. Surface what drives the spread. List the assumptions each estimate rests on and the unknowns that dominate the pessimistic tail. Where one unknown dwarfs the rest, recommend a spike via the prototype skill to shrink it before anyone commits to the number.
  4. Aggregate honestly. Sum the ranges, don't sum the likely values and call it the total. Give the total as a range with a stated confidence, and flag dependency chains where one slip cascades.
  5. State the invalidation conditions. What change (scope, staffing, a discovered constraint) would void the estimate — so the reader knows when to ask again.

An estimate with no assumptions listed is a guess in a suit. If the input is genuinely un-estimable, say so and name the one question that would change that.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.

Check your setup, free