problem-framing

problem-framing is a Structured reasoning & decision frameworks skill for AI agents, published by UnpaidAttention in the fable5-methodology repository on GitHub. Skillspace rates problem-framing D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Shape a problem before solving it — check the premise (XY problems, false dichotomies), find the canonical name for the problem shape, locate the hard kernel, and factor fuzzy asks into orthogonal decisions. Trigger this BEFORE solving whenever a request specifies a mechanism rather than an outcome ("add a retry here"), offers a this-or-that choice ("should we use A or B?"), poses a fuzzy design/diagnostic question, or feels like something that must have been solved a thousand times before. Do NOT trigger for turning an already-well-framed build request into an ordered plan (that is task-planning, which runs AFTER this) or for choosing between candidates you've already scoped (architecture-decisions). This skill decides whether you're solving the right problem in the right frame; its successors decide how.

Grade
D
Quality
68 / 100
Safety
Passed
GitHub stars
83

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubUnpaidAttention/fable5-methodology

Our review

Architecture
100 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
Not declared. Check the repository before reusing it.
Adoption
Adopted by 0 other repositories, in 0 collections

Not checked yet

  • Category is a classifier prediction; no model has read this skill yet
  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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