Improve Ic Skill

Improve IC Skill

A token-efficient workflow for improving an existing skill in the IC Skills repo. The guiding principle: understand the problem first, change precisely, verify only what changed. Evals exist as regression safety nets — run them selectively, not automatically on every pass.

Step 0 — Understand the problem before touching anything

This is a hard stop. Do not read any file, run any command, or make any change until you have a specific problem statement from the user.

You need to know: 1. What is the specific issue? (e.g. "canister ID X is stale", "missing pitfall about Y", "description doesn't trigger on Z", "code example uses deprecated API W") 2. Why does it matter? (e.g. "agents generate broken code", "skill never triggers for this use case", "upstream sync added a new command we don't cover")

If the request is vague ("improve the motoko skill", "the ckbtc skill feels weak") — ask:

"What specific issue should I focus on? For example: a missing pitfall, a stale canister ID, a broken code example, or a description that isn't triggering correctly."

If the user cannot or will not provide a specific issue — stop. Do not proceed. Do not read the skill. Do not run commands. Do not "explore and find improvements yourself." Explain clearly:

"I need a specific problem to solve before I can start. Blind improvements risk changing things that don't need changing. To find a concrete issue, try: running npm run validate, checking evaluations/<skill-name>.json for failing cases, or pointing me to a GitHub issue or upstream diff."

If the user pushes back, repeats the vague request, or expresses frustration — hold the position. The answer is always the same: no specific issue = no work started.

The only exception: if the reason is unambiguous from context already in the conversation (a GitHub issue body, an upstream diff, a specific error message) — proceed directly to Step 1 without asking.

Step 1 — Understand the skill

Read skills/<skill-name>/SKILL.md fully. Check:

• What it covers and what it's missing
• Whether it's upstream-tracked: grep <skill-name> .claude/upstream.md — if it appears, note which sections are owned by icskills (those cannot be overwritten from upstream)
• Recent git history: git log --oneline -5 -- skills/<skill-name>/SKILL.md

Step 2 — Validate baseline

npm run validate

Fix any errors before making content changes. Warnings are acceptable — don't chase them unless they're relevant to your improvement.

Step 3 — Identify improvements

Read with fresh eyes. Common high-value areas:

Pitfalls — highest-value content; every pitfall documented prevents an agent hallucination. Look for: incorrect usage patterns you know about, error messages agents typically misread, wrong defaults, API gotchas.

Canister IDs and version numbers — verify against mainnet and current release tags. Stale IDs silently break agent-generated code.

Code examples — verify syntax compiles, APIs still exist, no deprecated patterns.

Description — must state what it does, when to use it, AND when NOT to use it. Include specific keywords agents match on. A weak description means the skill never triggers.

Required frontmatter — metadata.title and metadata.category are required by CI. Missing these blocks deployment.

For upstream-tracked skills (motoko, migrating-motoko, migrating-motoko-enhanced, mops-cli): read .claude/upstream.md carefully. Only modify icskills-owned sections freely; changes to shared content should also be filed upstream.

Step 4 — Apply improvements

Edit skills/<skill-name>/SKILL.md. Stay focused — don't refactor things you weren't asked to touch.

Step 5 — Validate again

npm run validate

Fix any errors. Warnings from unrelated sections are fine to leave.

Optionally run LLM quality scoring if the changes were substantial:

skill-validator score evaluate --provider claude-cli skills/<skill-name>

Step 6 — Check and update evals

Open evaluations/<skill-name>.json at the repo root (not inside the skill directory).

If the file doesn't exist yet, create it as part of this improvement session. A skill being improved is the right moment to seed its first evals. Start lean — 1-2 output_evals covering the most important pitfalls, and 2-3 trigger_evals for the description. Don't try to be comprehensive; a small, accurate eval file beats an overwhelming one that never gets run.

For upstream sync sessions where no eval file exists: seed cases directly from the upstream diff — new commands, changed defaults, renamed APIs, and new pitfalls in the diff are exactly where agents will hallucinate without updated guidance. The diff is your best source.

# Verify no eval file exists
ls evaluations/<skill-name>.json 2>/dev/null || echo "No evals yet — create them"

If the file exists, review the existing cases alongside your changes.

Always update evals when:

• You added a new pitfall — pitfalls are exactly where agents hallucinate without guidance; every new pitfall deserves a case
• You changed a command, API, or canister ID that an existing case now describes incorrectly — fix the case
• You introduced a new behavior that agents would likely get wrong without explicit guidance

Keep the suite lean. Don't add cases for things that are obvious or already well-covered. Each case is a future regression test that costs tokens to run.

Step 7 — Run existing evals (only when relevant)

Running the existing suite is a regression check — it answers "did I break something that was already tested?" It is separate from adding new cases (Step 6). You can add a new eval case for a new pitfall without running the existing suite at all.

Run evals when you modified existing tested content:

• You changed or removed content that an eval explicitly tests (canister ID, command name, error message)
• You rewrote a section that several evals cover
• The description changed significantly — run trigger evals only
• You're unsure whether a change is safe

Skip running evals when your changes were purely additive:

• New pitfall, new section, new example — nothing you touched is already tested

Check what evals exist before deciding:

node scripts/evaluate-skills.js <skill-name> --list

Run only the relevant subset:

# Single eval by index (cheapest)
node scripts/evaluate-skills.js <skill-name> --eval <N>

# Trigger evals only — for description changes
node scripts/evaluate-skills.js <skill-name> --triggers-only

# Skip baseline to halve token cost when correctness is what matters, not the delta
node scripts/evaluate-skills.js <skill-name> --eval <N> --no-baseline

# Full suite — only when you made broad changes across the whole skill
node scripts/evaluate-skills.js <skill-name>

Eval format reference:

{
  "output_evals": [
    {
      "name": "descriptive name",
      "prompt": "scoped prompt asking for one thing — specify what to exclude to keep it fast",
      "expected_behaviors": [
        "Specific, checkable behavior the model should exhibit",
        "Another specific behavior"
      ]
    }
  ],
  "trigger_evals": [
    {
      "query": "realistic user prompt that should trigger this skill",
      "expected_behavior": "should trigger"
    },
    {
      "query": "realistic prompt that looks similar but should NOT trigger this skill",
      "expected_behavior": "should NOT trigger"
    }
  ]
}

Keep prompts tightly scoped — open-ended prompts generate long responses and risk timeouts. Ask for one thing; explicitly exclude what you don't want ("just the function, no deploy steps").

Step 8 — Final check

npm run validate

The skill is ready for a PR. Include a brief summary of what changed and, if you ran evals in Step 7, paste the relevant results collapsed in a <details> block (see Submit a PR for the exact format).

Upstream-tracked skills

Before editing motoko, migrating-motoko, migrating-motoko-enhanced, or mops-cli, read .claude/upstream.md. It lists which sections are icskills-owned. You can freely improve owned sections. For shared content, improvements should also be filed as issues upstream so they flow back on the next sync.