agentleFS
yogsoth-aiyogsoth-ai/de-anthropocentric-research-engine/skills/define-objective/SKILL.md

define-objective

Skill505 starsChanged 48 days agoApache-2.0View on GitHub

Formalize objectives, constraints, and tradeoff preferences for optimization.

2 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    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
  1. Distinctive:

    Do not encode a preferred solution as an objective.

    line 29

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

define-objective

Purpose

Formalize objectives, constraints, and tradeoff preferences before an optimization or selection decision.

Input contract

required: [decision_context, candidate_set, stakeholder_preferences]
optional: [hard_constraints, utility_scale, priority_weights]
constraints: [objectives are measurable or operationalized; tradeoffs and constraint direction are explicit]

Procedure

  1. Elicit the desired outcome, affected scope, stakeholders, and time horizon.
  2. Separate objectives from constraints and preferences, then define each objective's direction and measurement.
  3. Record conflicts, permissible tradeoffs, and any weights or lexicographic priorities with their rationale.
  4. Emit an objective schema suitable for comparison, optimization, and later audit.

Output contract

produces: [objective_schema, constraint_schema, tradeoff_policy, unresolved_preferences]
delta_fields: [findings, assumption_updates, uncertainties, decisions, open_questions]

Quality gates

  • A-class gate: declared universe = all stakeholder objectives and hard constraints in scope; numerator = items operationalized with direction, measurement, and source; batch increment = one objective or constraint formalized; stopping reason = all items formalized or unresolved conflicts are explicit; source references = stakeholder, requirement, and decision IDs; direction/threshold reason = an objective enters optimization only when its direction and acceptable tradeoff boundary are explicit.
  • At least two distinct objectives and one constraint are required unless the decision record justifies a narrower scope.

Failure and counterexamples

Do not encode a preferred solution as an objective. If two stakeholders use the same word for different outcomes, preserve separate definitions.

Provenance map

  • resolved: objective-definition

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.