# Skilladam Optimize > Optimize an existing SKILL.md from a usage-intent description with SkillAdam. Use when a user asks to test, improve, patch, review, validate, or iteratively optimize an Agent Skill, with optional selective hunk review. - Skill: `ruc-datalab/skilladam-optimize-3` (Agent Skill) - Install (CLI): `npx skillmds@latest add ruc-datalab/skilladam-optimize-3` - Raw SKILL.md: https://api.skillmd.com/api/skills/ruc-datalab/skilladam-optimize-3/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ruc-datalab (https://skillmd.com/u/ruc-datalab) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/ruc-datalab/skilladam-optimize-3 --- # SkillAdam Optimize Use the `skilladam_*` MCP tools when available. Otherwise invoke the equivalent `skilladam-product` commands and parse their JSON output. ## Build the task manifest For a new run, research and construct the task signal in the current host session before calling SkillAdam. Do not start a nested agent or external task generator solely for this step. 1. Read the complete target `SKILL.md` and the user's usage intent. 2. Call `skilladam_discover_history` exactly once with `skill_path`, `intent`, and the durable `output_dir`. It automatically scans the current host's known local history formats, keeps only real user messages, redacts likely secrets and personal data, deduplicates them, and writes `history_discovery.json`. Review each candidate for actual relevance and evaluation feasibility. Turn a useful real request into a self-contained evaluation case by minimally normalizing it and, when necessary, embedding a small synthetic artifact that preserves the requested work. Record `metadata.history_transform` describing that normalization; never claim the synthetic artifact came from the user. Use source `local_history` and the candidate's exact returned `source_ref`; never invent a history reference. A completed search with zero accepted candidates is valid and must not be relabeled as a history-derived task. 3. Reuse concrete tasks the user supplied. Extract tasks from the currently visible conversation only when they represent actual Skill usage; use source `conversation`, redact secrets and personal data, and keep a precise turn reference. 4. When a recognized public benchmark would materially improve coverage, use the host's normal web/research tools to select relevant cases. Record the direct source URL and benchmark/license details in metadata. Do not invent provenance when no suitable source was found. 5. Fill the remaining coverage from the Skill itself. Produce exactly `task_count` distinct tasks, covering at least two concise lowercase capability slugs and at least one `boundary`, `adversarial`, or `regression` case. Prefer realistic end-to-end requests over shallow paraphrases. 6. Give every task a stable `task_id`, self-contained `prompt`, `capability`, `difficulty`, `source`, `source_ref`, and explicit `evaluation`. Allowed sources are `local_history`, `conversation`, `user_provided`, `public_benchmark`, and `skill_generated`, in that priority order when the source yields a genuinely useful task. Public benchmark references must be direct HTTP(S) URLs. 7. Use deterministic `programmatic` or `reference` evaluation only when the task has one uniquely correct answer, such as a number, fixed string, explicit JSON structure, or exact set of fields. Open-ended natural-language tasks that allow semantically equivalent answers must use a `rubric_judge` rubric with weighted observable dimensions; never score the complete response with `exact` or `normalized_exact`. This includes generated messages, reviews, rewrites, summaries, recommendations, and analyses. Use `hybrid` only for complementary components. Every evaluation, including each hybrid component, must include concise `failure_feedback` with the actionable correction or evidence to use after a failure. Never omit an evaluation or ask SkillAdam to infer one. Correct a rejected manifest instead of weakening the requested count or evaluation goal. Pass `{"schema_version":"1","tasks":[...]}` in `task_manifest`. Task text must not depend on files, tools, credentials, or context unavailable to the rollout model. ## Start or resume 1. Obtain the source `SKILL.md`, a durable workspace-local output directory, and the user's usage intent. Use a path such as `run/skilladam-optimize` under the current workspace; never use a host temporary or session-state directory. 2. For a new run, call `skilladam_prepare` with the source `skill_path`, `intent`, durable `output_dir`, completed `task_manifest`, and `compact: true`; omit provider fields so SkillAdam reuses the platform's authenticated CLI. Pass provider configuration only for an explicit fixture or OpenAI-compatible override. Use `skilladam_prepare_for_review` instead only when the user explicitly requested confirmation or hunk selection before apply. Do not use the asynchronous `skilladam_start*` tools: the Claude adapter intentionally does not expose them because a non-interactive turn may stop polling before a worker reaches a terminal state. 3. In default mode, call `skilladam_prepare` again with the same `output_dir` and `compact: true` while the workflow state is `ready` or `validating`, until the existing stopping policy returns `completed`. Each call resumes the durable checkpoint. If a call fails, report the exact error; do not shrink `task_count` or weaken evaluation settings as a workaround. ## Review a proposal 1. Read `proposal.hunks` from the structured result. Preserve every `hunk_id`. 2. Determine whether the user explicitly requested hunk review or confirmation before changes are applied. A general request to optimize, improve, patch, or modify the Skill does not opt in to review. 3. By default, `skilladam_prepare` auto-accepts every proposed hunk before the call returns. Do not issue a second selection, omit or reject hunks, pause for confirmation, or override the gate based on your own assessment. 4. Only after an explicit review opt-in, use `skilladam_prepare_for_review`, present every hunk's target, header, and operations in original order, and wait at `awaiting_review`. Prefer a native structured selection UI that can map decisions back to exact hunk IDs; otherwise ask the user for accepted IDs, accept all, or reject all. 5. In review mode, never treat silence, timeout, disconnect, resume, or an unrelated positive comment as acceptance. An empty selection rejects all. 6. Submit exactly the selected IDs with a new, stable idempotency key. Do not use review results from a different proposal or infer a partial selection. ## Validate and iterate 1. Trust only `candidate_validation` whose digest matches the selected candidate. Never reuse validation for the full proposal after a partial selection. Each iteration's current Skill rollout and selected candidate validation must keep the exact ordered mini-batch IDs recorded in the session. 2. Report the gate decision and stop reason concisely. The service writes the source Skill only when the gate accepts the selected candidate. After the gate, the service updates EIT/Momentum from ordered trajectory feedback, the patch, candidate feedback, and the decision, then updates the adaptive edit budget. 3. Continue synchronous `skilladam_prepare` calls through `ready` and `validating` until the workflow reaches `completed`; the same tool resumes both states, so do not call `skilladam_continue`. 4. If the source changed during review, preserve the external edit and report the conflict; do not overwrite it. 5. On `completed`, report final quality only from `final_validation` whose scope is `heldout_terminal`. `baseline_validation` and `candidate_validation` are optimization mini-batch evidence and must never be described as final or held-out. If terminal evidence is unavailable, report that exact condition; do not substitute another metric. Never feed final held-out evidence into patch generation, hunk selection, an iteration gate, or Momentum. 6. Use the compact response's `iterations` entries for per-iteration results. Do not request a full status response merely to reconstruct the final report. Never edit, replace, or patch the source Skill with host file tools or shell commands during an optimization session. This remains forbidden when a proposal looks weak, the gate rejects it, or the iteration limit is reached. Only SkillAdam may write a selected candidate after frozen validation passes the gate; otherwise leave the source unchanged and report the session result. The host owns task research and manifest construction only. Do not reimplement manifest validation, evaluation execution, patch application, gate logic, Momentum, edit budgets, checkpoints, or stopping policy in the host adapter. ## Claude Code structured review When the user explicitly requested confirmation or selective hunk review: 1. After `skilladam_prepare_for_review` reaches `awaiting_review`, present every hunk in original order. For each hunk, show its stable `hunk_id`, target path, header, and complete operations inside a fenced `diff` block. Do not summarize or omit the diff before asking for a decision. 2. Use Claude Code's `AskUserQuestion` tool to collect an explicit accept/reject decision for each displayed hunk. Keep the stable `hunk_id` in each question and batch questions only when required by the host tool's current limit. 3. Map the answers back to exact hunk IDs and call `skilladam_submit_selection` once with the final accepted set. Cancellation, silence, timeout, or an incomplete answer leaves the session at `awaiting_review` and must never trigger default acceptance.