go-makefile-writer Skill Evaluation Report¶

Evaluation framework: skill-creator Evaluation date: 2026-03-11 Subject: go-makefile-writer

go-makefile-writer is a skill for creating or refactoring Makefiles in Go repositories, suitable for unifying build, test, lint, run, and CI entry points, and for converging existing Makefiles of varying quality with minimal effort. Its three main strengths are: automatically planning target sets and naming rules from repository structure for more stable, readable Makefiles; consistent version pinning and normative constraints for key targets like install-tools, ci, and tidy to reduce drift; and in Refactor mode, emphasis on minimal-diff and backward compatibility—fixing issues without breaking existing usage patterns.

1. Evaluation Overview¶

This evaluation assesses the go-makefile-writer skill along two dimensions: actual task performance and Token cost-effectiveness. It uses 3 Makefile generation/refactoring scenarios of increasing complexity (single-binary creation, multi-binary+Docker creation, defective Makefile refactoring). Each scenario runs with both with-skill and without-skill configurations, for 3 scenarios × 2 configurations = 6 independent subagent runs, scored against 42 assertions.

2. Test Methodology¶

2.1 Scenario Design¶

2.2 Execution¶

• Each scenario uses an independent Git repo with pre-seeded code and go.mod
• With-skill runs first read SKILL.md and its referenced materials (golden template, quality guide)
• Without-skill runs read no skill; Makefile is generated by model default behavior
• All runs execute in independent subagents in parallel

3. Assertion Pass Rate¶

3.1 Summary¶

3.2 Classification of 13 Without-Skill Failed Assertions¶

3.3 Trend: Skill Advantage Decreases with Scenario Complexity¶

This is expected: Eval 3’s user prompt explicitly listed all 6 defects, effectively embedding the skill’s knowledge in the prompt. Eval 1’s prompt was minimal and most dependent on the skill’s conventions.

4. Dimension-by-Dimension Comparison¶

4.1 Naming Convention (cmd/-path semantics)¶

This is the largest single-item delta, contributing 2 assertion failures in Eval 1.

Analysis: Without-skill naturally used per-binary naming in multi-binary scenarios (Eval 2/3), but in the single-binary scenario defaulted to generic names. The skill’s rule "Map target names to cmd/ path semantics: cmd/<name> → build-<name>" ensures consistency.

Practical value: Consistent naming enables: - No target renaming when scaling from single to multi-binary - Unified Makefile style across teams - Predictable target names in CI scripts

4.2 install-tools and Version Pinning¶

Analysis: Without-skill in Eval 2 embedded golangci-lint installation in the lint target (@latest auto-install). This works locally but in CI causes: - Non-deterministic builds (different versions at different times) - Re-installing tools on every CI run (slow)

The skill explicitly requires "Pin tool versions in install-tools for CI reproducibility".

4.3 Output Contract (Structured Report)¶

This is a skill-only differentiated output. With-skill produces a report after each run containing:

Without-skill produced brief task summaries but no structured Output Contract.

Practical value: The Output Contract enables: - Auditable Makefile changes (PR reviewers know what changed and why) - Traceable backward compatibility in Refactor mode - Documented CI validation results

4.4 ci Target Naming¶

The skill specifies "CI target: ci (fmt-check + lint + test + cover-check in one pass)". Without-skill in Eval 2 used check with content fmt-check vet test (missing cover-check), not fully aligned with the standard CI pipeline.

4.5 Impact of Golden Template¶

With-skill Makefiles closely follow the golden template structure (variables → build → run → quality → ci → version → tools → clean → phony → help), while without-skill structures varied.

Key Eval 2 difference: Without-skill used $(eval $(call build-template,...)) dynamic metaprogramming for build targets; with-skill used explicit per-binary targets per the golden template. The skill’s Anti-Patterns section explicitly flags "Overly dynamic Make metaprogramming (eval/call/define) that reduces readability when explicit targets would be clearer".

4.6 Actual Makefile Quality Comparison¶

Using Eval 2 (most complex scenario) as an example:

5. Token Cost-Effectiveness Analysis¶

5.1 Skill Size¶

go-makefile-writer is a multi-file skill (SKILL.md + references + scripts). What is loaded into context depends on which files the subagent reads.

Typical load scenarios:

5.2 Token Cost for Quality Gain¶

5.3 Token Segment Cost-Effectiveness¶

SKILL.md content split by functional module:

5.4 High-Leverage vs Low-Leverage Instructions¶

High leverage (~360 tokens SKILL.md → 12 assertion delta): - Naming convention cmd/<name> → build-<name> (100 tok → 2) - Output Contract definition (300 tok → 3) — template portion contributes most - install-tools version pinning (80 tok → 3) - ci target specification (50 tok → 2) - tidy target (30 tok → 1) - lint tool check (40 tok → 1)

Medium leverage (~310 tokens → indirect contribution): - Anti-Patterns section (250 tok) — avoided eval/call metaprogramming in Eval 2 - docker-build variable spec (60 tok → 1)

Low leverage (~350 tokens → 0 delta): - Go Version Awareness (150 tok) — not tested - Monorepo Support (200 tok) — not tested

References (~2,150–2,660 tokens → indirect contribution): - Golden templates drive overall Makefile structure consistency - Quality guide provides concrete recipe implementations

5.5 Token Efficiency Rating¶

5.6 Comparison with git-commit Skill Cost-Effectiveness¶

go-makefile-writer’s SKILL.md cost-effectiveness is close to git-commit, but references add significant token overhead. Reference value mainly shows in Makefile structure consistency and avoiding anti-patterns—quality dimensions that are hard to quantify with assertions.

6. Boundary Analysis vs Claude Base Model Capabilities¶

6.1 Base Model Capabilities (No Skill Increment)¶

6.2 Base Model Gaps (Skill Fills)¶

7. Overall Score¶

7.1 Dimension Scores¶

7.2 Weighted Total¶

8. Evaluation Artifacts¶