# Skill Optimizer

> Improve, debug, benchmark, or refactor an existing Agent Skill from conversation evidence, execution traces, user corrections, eval failures, or target skill files. Use this skill whenever the user asks to optimize, harden, generalize, validate, benchmark, package, or turn observed behavior into durable skill changes. Produces evidence-based diagnosis, failure-class extraction, reviewable patches, trigger evals, validation cases, old/new comparison plans, and safe next-run behavior; do not use it to perform the target skill's normal task.

- Skill: `undertone0809/skill-optimizer` (Agent Skill, multi-file: 17 files)
- Install (CLI): `npx skillmds@latest add undertone0809/skill-optimizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/undertone0809/skill-optimizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: undertone0809 (https://skillmd.com/u/undertone0809)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/undertone0809/skill-optimizer

---


# Skill Optimizer

## Mission

Turn real usage evidence into safer, more reliable, easier-to-trigger, and easier-to-evaluate Agent Skills.

This is a meta-skill. It does not merely rewrite prose. It analyzes the fit between a skill's purpose, trigger description, inputs, procedure, tools, outputs, risks, examples, and evaluations, then proposes small reviewable changes.

## Use when

Use this skill when the user asks to improve, optimize, debug, refactor, benchmark, validate, generalize, harden, package, or document another skill. Also use it when the user says the current conversation should be captured into a skill, or that a previous skill run exposed something that should happen differently next time.

Do not use this skill for ordinary task execution. If the user asks to run the release skill, do not optimize the release skill unless they ask to improve it.

## Modes

- Diagnose: identify what should change without writing a patch.
- Patch: produce a reviewable diff, replacement section, or revised `SKILL.md`.
- Validate: create validation cases and failure-mode checks.
- Benchmark: compare the old and new skill using task cases, trigger cases, and deterministic rubrics.
- Package: organize the skill folder, references, scripts, examples, changelog, and README.

## Required inputs

Infer these from the conversation before asking follow-up questions:

- target skill name and purpose
- current `SKILL.md` and supporting files, if available
- execution evidence: user request, tool use, output, corrections, mistakes, delays, or surprises
- observed failure or success pattern, stated as a reusable failure class when possible
- environment: chat, code agent, workspace agent, API, or another harness
- intended optimization mode
- risk level and write-action authority
- sequencing constraints when optimization is requested after another live task
- current eval suite or validation artifacts, if the target skill has them

If the target skill file is unavailable, do not fabricate an exact diff. Produce an inferred improvement plan, draft replacement sections, validation cases, and assumptions.

If the user asks to optimize a skill after completing another concrete task, finish and verify the concrete task first unless they explicitly ask to pause it. Then optimize from the observed evidence. Do not interrupt the user's primary workflow just because this skill is mentioned.

## Universal optimization lens

Analyze every target skill through these lenses:

1. Purpose and scope: what job the skill owns, who it serves, and what it must not do.
2. Triggering and boundaries: description quality, should-trigger cases, near-negative cases, competing skills, and under/over-triggering risks.
3. Inputs and assumptions: required inputs, source of truth, missing-data behavior, units, locale, time horizon, and user preferences.
4. Workflow and decision rules: ordered steps, branch conditions, heuristics, stop conditions, escalation paths, and exception handling.
5. Tools and authority: required tools, permissions, external writes, approvals, dry runs, and exact operations.
6. Outputs and interfaces: templates, file formats, citations, links, machine-readable fields, handoff artifacts, and user-facing summaries.
7. Quality bar and evaluation: success criteria, deterministic verifiers, examples, regression tests, trigger evals, and human review points.
8. Safety, privacy, and policy: sensitive data, regulated advice, consent, audit trail, access control, retention, and harmful misuse.
9. Failure and recovery: blocked states, retries, rollback, partial completion, cleanup, and user-visible status.
10. Maintainability: concise instructions, bundled resources, changelog, version notes, known limitations, and portability across harnesses.

Use `references/universal-optimization-lens.md` when a deeper diagnosis is needed.

## Evidence rules

Treat the current conversation as evidence, not as a script to memorize.

For each important observation, capture:

- Evidence: what happened or what the user corrected.
- Root cause: why the current skill allowed it.
- Durable change: what should be added, removed, or clarified.
- Classification: one-off instruction, reusable workflow rule, user/team preference, conflicting instruction, or open question.

Do not overfit one unusual task into a permanent rule. Convert it into a reusable rule only when it improves future behavior.

Treat strong user corrections as high-signal evidence. Phrases like "no", "not this", "wrong direction", "first principles", or a correction from a component-level answer to a user-scenario answer usually indicate a framing failure, not just a missing detail. Capture:

- the wrong abstraction level the previous run optimized for
- the user's intended source of truth
- what should have been downstream evidence rather than the starting point
- how the target skill should avoid the same drift next time

When the evidence comes from a multi-step execution, include the verification results, not only the final prose. A durable skill change should be grounded in what was requested, what was attempted, what was corrected, and what was ultimately validated.

## Evidence-to-eval loop

Do not treat bug-specific reproduction as the only useful eval. Many skill failures are tied to one run, one repo state, one external outage, or one user correction that will not reproduce later. Convert those observations into reusable behavior checks.

For each meaningful skill improvement, produce this chain:

```text
run evidence or user correction
-> failure class or success pattern
-> target skill decision point
-> small skill patch
-> validation case or eval task
-> old/new comparison plan
```

When a bug can no longer be reproduced, write an eval for the next-run behavior that would have prevented or diagnosed the bug class. Examples:

- Stale config caused the wrong answer -> eval that requires live source-of-truth probing before using remembered state.
- UI work was accepted without visual proof -> eval that requires browser or screenshot evidence before handoff.
- A one-off fix skipped the larger workflow -> eval that checks the skill starts from the user scenario, not the visible component alone.

Every validation case generated from real evidence should name:

- Failure class: the reusable category of mistake, not only the incident.
- Decision point: the step where the target skill must do something different.
- Required source of truth: logs, API readback, screenshots, current docs, repo files, user artifact, or other live evidence.
- Must-not behavior: the tempting shortcut the improved skill must avoid.
- Observable proof: the evidence a reviewer can use to score whether the skill followed the rule.

For project-based skills, keep the domain logic inside the target skill. Skill Optimizer owns the optimization loop, the rubric, and the old/new comparison. The target skill owns its source-of-truth order, approval gates, must-not behaviors, and domain-specific validation cases.

## Framing and abstraction checks

Before proposing a patch, ask whether the target skill optimized the right object:

- User outcome vs. UI surface: a feature or component may be only a view over a deeper workflow.
- Scenario spine vs. fixture rows: realistic data should come from causal user activity, not isolated screen states.
- Source of truth vs. derivative signal: logs, runs, issues, costs, or decisions may be primary; dashboards, calendars, and summaries may be downstream.
- Product intent vs. local convenience: read available product, requirement, or reference docs before encoding a domain rule from one conversation.

If the observed failure is "the answer satisfied the visible surface but missed the user's real scenario", make that explicit in the diagnosis and add a workflow guard to the target skill rather than only adding more examples.

## Patch rules

Prefer small, auditable patches over broad rewrites. Preserve the target skill's identity, useful examples, and safety constraints.

A patch may change:

- frontmatter description and trigger boundaries
- required inputs and assumptions
- workflow steps and decision rules
- output templates
- safety and approval requirements
- failure handling
- examples and references
- validation cases and benchmark tasks

Never silently weaken safety requirements. For write actions, publishing, financial actions, medical or legal consequences, hiring decisions, external communication, deletion, deployment, migration, or permissions changes, require explicit authority unless the target skill already has a clear safe policy.

## Trigger optimization

The frontmatter description is the primary discovery signal. After improving a skill, evaluate whether the description should change.

Create trigger evals with:

- realistic should-trigger queries
- realistic should-not-trigger near misses
- ambiguous cases where another skill might be more appropriate
- casual phrasing, typos, file paths, role context, and domain language

Optimize for accurate triggering, not maximum triggering.

## Domain adaptation

This skill is domain-general. Do not bake one domain's checklist into the core instructions.

When the domain matters, attach or consult a short domain adapter. A good adapter names:

- source of truth
- required inputs
- review owner
- consequential actions and approval gates
- privacy, confidentiality, or consent constraints
- output template
- validation cases and deterministic checks
- must-not behaviors

Use the transcript to extract observed domain markers, but do not encode hidden rubric terms or unrelated best practices as mandatory rules. Keep adapters modular so the optimizer can handle software, healthcare operations, law, finance, education, research, HR, customer support, operations, creative work, personal productivity, and other workflows.

Use `references/domain-adapter-patterns.md` when building or selecting an adapter. If a matching file exists under `references/adapters/`, consult it as a compact checklist rather than copying it wholesale into the target skill.

## Validation format

Every meaningful behavior change needs at least one validation case.

Use this format:

```md
### Case: <name>

Input:
...

Expected behavior:
...

Must not:
...
```

Include at least one normal case, one edge case, and one regression case when the change could break prior behavior.

When optimizing from a transient or already-fixed bug, the regression case should validate the behavior habit rather than the exact old environment. Prefer:

- "When active runtime is ambiguous, verify it live before diagnosing" over "Reproduce the specific May 19 runtime bug."
- "When release state may be partial, classify npm, tag, asset, and workflow state before republishing" over "Replay the exact failed release run."
- "When user feedback says the abstraction level is wrong, identify the intended source of truth before patching" over "Add this exact sentence to the skill."

Use a compact five-point rubric when no stronger deterministic verifier exists:

1. Correct skill trigger or routing.
2. Correct source of truth checked first.
3. Known bad shortcut avoided.
4. Target behavior or patch completed.
5. Reviewable evidence included.

## Benchmark reporting

When running evals, separate three scores:

1. Trigger accuracy: whether Skill Optimizer should activate.
2. Patch-quality coverage: whether the proposed change includes evidence, scope classification, patch, safety, outputs, and validation.
3. Downstream transfer: whether the optimized target skill actually improves on its own task suite.

For ongoing project-based skill maintenance, also report outcome metrics from real runs when available:

- user correction count for the same failure class
- first useful source-of-truth action latency
- rework or reviewer-blocked count
- handoff evidence completeness
- recurrence rate of the failure class

Keep synthetic verifier scores, human rubric scores, and live outcome metrics separate. A higher keyword score is useful regression evidence, not proof that the target skill improved in production.

Label synthetic verifier scores as synthetic. Do not report them as official benchmark or leaderboard results.

## Packaging expectations

When packaging a skill or skill optimizer project, include:

- `SKILL.md` and supporting references
- README with purpose, installation, usage, eval, limitations, and license
- changelog and version note
- examples of target skills and optimization outputs
- eval cases or a lightweight benchmark harness when available
- a distributable zip that contains exactly one skill folder for installation

## Final response contract

Return these sections unless the user asks for a narrower result:

1. Target skill and optimization mode
2. Diagnosis summary
3. Evidence ledger
4. Improvement categories
5. Proposed patch or revised skill draft
6. Failure-class eval case or old/new comparison plan
7. Trigger eval suggestions when discovery may change
8. Validation cases
9. Benchmark or eval result, if run
10. Assumptions, conflicts, and unresolved questions

When direct file editing is available and the user explicitly requested edits, apply the patch. Otherwise present a reviewable patch.

When optimization is part of a larger completed workflow, keep the final response proportional: report the primary task result first, then the skill changes and validation. Do not force the full nine-section contract if it would obscure the work the user was actually trying to finish.

## Quality bar

A successful optimization makes the next run of the target skill more predictable, easier to trigger correctly, safer around irreversible actions, clearer in output, and easier to verify.