optimizing-skills

Optimizing Skills

Treat the skill document as the parameter under optimization: change it only when the change demonstrably beats the version you already ship. This is the discipline distilled from SkillOpt (microsoft/SkillOpt, arXiv:2605.23904) — its training apparatus dropped, its reproducibility discipline kept. The point is to stop editing skills on intuition and start editing them on evidence.

Core principle

A skill edit is only worth shipping if it strictly improves measured behavior on a held-out check. Most edits that feel like improvements don't move the needle, and some quietly regress. The gate below is what separates a real improvement from a confident guess.

The gate — run it every revision

1. Assemble a held-out check set. 3–8 representative tasks/prompts the skill should handle well, and it must include the failure(s) that prompted this revision. Keep the set fixed across the revision so before/after scores are comparable.
2. Hold two versions. best = what you currently ship (never let it silently degrade). candidate = best + your proposed edits.
3. Score both on the check set. "Run" here = dispatch each check task to the Agent tool (subagent_type=general-purpose) with the skill version in context, or evaluate by hand for small sets. Score per criterion, not one collapsed pass/fail. When a task carries several criteria, the criterion that decides accept/reject is the failure that prompted this revision; the others are regression guards that must not get worse. Collapsing criteria masks the win: in the down-skilling-v1.2.0 retro, the edit drove architectural hallucination 60%→0% while an unrelated length criterion stayed 0/5 in both arms — a single combined pass/fail scored that as a 0–0 tie and would have rejected a large, real improvement.
4. Accept only if candidate strictly beats best on the triggering-failure criterion, with no regression guard worse. Ties → reject, keep best. An edit that doesn't move the needle does not ship.

When the skill's own output is compiled by an Agent (down-skilling and creating-skill produce a prompt an author writes from the SKILL), score ≥2 author samples per version, or fix one author across both arms. A single author sample per arm lets author capability dominate the edit effect: the same down-skilling edit measured 95%→0% with one author pair and 60%→0% with another — real either way, but n=1 cannot tell a real edit from a lucky author.

This two-tier best/candidate split is the heart of it: a working revision can explore, but the shipped skill only ever ratchets upward.

Bounded edits (the "textual learning rate")

Cap edits per revision — default ~4 distinct add/replace/delete operations, fewer as the skill matures. Large speculative rewrites drift and destroy your ability to attribute a regression to a cause. If a revision wants more edits than the budget, rank and keep the top ones (below) and let the rest wait.

Reflect: failures first, then successes

Separate the evidence before proposing edits:

• Failure reflection. Across the failing cases, find the common, systematic pattern — not a one-off edge case. Propose edits that fix the pattern. Failures take priority in any merge.
• Success reflection. Across cases that already work, find generalizable patterns worth encoding so they survive future edits. Reinforce; don't duplicate.

For both: edits must generalize (never hardcode task-specific values), and must not duplicate content already in the skill — patch genuine gaps only.

Rank when over budget

When candidate edits exceed the budget, keep them in this priority order:

1. Systematic impact — fixes a recurring failure across many cases, not one.
2. Complementarity — fills a real gap rather than restating existing content.
3. Generality — phrased as a durable principle, not tied to one task/entity.
4. Actionability — concrete, followable guidance over vague advice.

Drop the rest. They can return next revision if still warranted.

Protect the hard-won core

If a skill has a battle-tested core that routine edits keep eroding, fence it off and treat it as off-limits to fast edits. Revisit it only on a deliberate longitudinal review: compare the same check tasks across several versions to catch slow drift and regressions that single-edit review misses. (SkillOpt fences this region with HTML-comment markers and only rewrites it at epoch boundaries — the same idea, manual cadence.)

Carry memory across revisions

After a revision, record what you learned about editing this skill — which kinds of edits helped, which were brittle, redundant, or harmful — via remember() tagged with the skill name. Before the next revision, recall() it. This is the compounding part: each revision starts smarter than the last, the way SkillOpt's optimizer-side meta-skill conditions its future edits.

Edit mechanics

Edits are literal string operations (the Edit tool): the target text must match exactly or the edit is a silent no-op. Keep targets unique and verbatim. Prefer append / insert-after-heading / replace-exact / delete-exact, and verify each edit landed before scoring.

When NOT to use this

• Authoring a brand-new skill from scratch → creating-skill.
• Tracking/rolling back versions during development → versioning-skills.
• One-off prose with no reuse → just write it.

Checklist

• Held-out check set assembled (includes the triggering failure), fixed for the revision
• Edits bounded (~4 max), each generalizable and non-duplicative
• Failure patterns addressed before success reinforcement
• Candidate scored per-criterion against best; accept decided by the triggering-failure criterion, others as regression guards; shipped only if strictly better
• For Agent-compiled artifacts (down-skilling, creating-skill): ≥2 author samples per version, or a fixed author across arms
• Hard-won core left untouched unless doing a deliberate longitudinal review
• Lesson about editing this skill recorded via remember()

For the deeper "dispatch reflection/scoring to the Agent tool" recipe and the adapted reflection/ranking prompt templates, see references/skillopt-provenance.md.