functional-skill-creator

Functional Skill Creator

Maintain Agent Skills with functional programming discipline.

中文版 README

Functional Skill is an engineering methodology, designed for complex Skill maintenance and iteration. Combined with trace logging and unit testing, it makes Skills modular, traceable, and testable:

• Treat each Step as a Function with explicit Input/Output (pure function first)
• SKILL.md only orchestrates the pipeline of Functions, consuming only external inputs and reference dependencies
• Shared rules across Functions go into references/
• Deterministic logic called by Functions becomes scripts/
• Add trace logging for every Function, running locally, recording input/output/token consumption/duration
• Equip every Function with unit tests and E2E tests, ensuring neither individual Functions nor the full pipeline regress

Quick Start

Install

npx skills add AGI-comming/functional-skill-creator --skill fskill-creator -y

Install via skills.sh. Add -a <agent> to target one agent; add -g only if it supports global install.

Usage

Create — new functional skill from a workflow brief:

/fskill-creator create a functional skill for <workflow>

Migrate — convert an existing legacy skill directory into a functional skill:

/fskill-creator migrate <path-to-skill-dir>

The migrate lane reads the whole skill package — SKILL.md, references/, scripts/, and other companion files — not a single markdown file in isolation.

Optional: include_report, include_unittest, include_viewers (on by default; set to false to skip).

Why

Your Skill is getting bloated.

As your Skill's capabilities iterate, your SKILL.md and references/*.md grow longer, rules pile up, edge cases get patched ever more finely — slowly turning into an unmaintainable wall of prose.

A typical example is when everything keeps getting stuffed into a handful of markdown files:

Before: monolithic prose wall

flowchart TB
  subgraph mono["Monolithic Skill — tangled dependencies"]
    SKILL["SKILL.md<br/>Goal · Workflow · Rules · Output · Known issues"]
    W["Workflow + Step 1.5 patch"]
    RL["Rules · edge cases · stop rules"]
    OUT["Output handler · validation"]

    RULES["references/rules.md<br/>Terms · policies · snippets"]
    EX["references/examples.md<br/>Success · failures · workarounds"]

    S1["parse_input.js"]
    S2["parse_input_new.js"]
    S3["fix_edge_case_once.sh"]
    S4["migrate_old_do_not_delete.js"]
  end

  SKILL <--> W
  SKILL <--> RL
  W <--> RL
  RL <--> OUT
  SKILL <--> RULES
  RULES <--> EX
  EX <--> W
  OUT <--> EX
  W --> S1
  RL --> S2
  S1 <--> S2
  S2 --> S3
  S3 <--> EX
  OUT --> S4
  S4 <--> SKILL
  S3 <--> RL
  S1 --> OUT

  style mono fill:#fff5f5,stroke:#c53030
  style SKILL fill:#fde8e8,stroke:#c53030
  style W fill:#fde8e8,stroke:#c53030
  style RL fill:#fde8e8,stroke:#c53030
  style OUT fill:#fde8e8,stroke:#c53030
  style RULES fill:#fde8e8,stroke:#c53030
  style EX fill:#fde8e8,stroke:#c53030
  style S1 fill:#fde8e8,stroke:#c53030
  style S2 fill:#fde8e8,stroke:#c53030
  style S3 fill:#fde8e8,stroke:#c53030
  style S4 fill:#fde8e8,stroke:#c53030

Functional Skill Creator provides an engineering methodology that makes Skills modular, traceable, and testable:

• Break each step into a Function with explicit Input/Output
• SKILL.md only orchestrates the pipeline of Functions, consuming only external inputs and reference dependencies
• Shared rules across Functions go into references/
• Deterministic logic called by Functions becomes scripts/
• Add trace logging for every Function, running locally, recording input/output/token consumption/duration
• Equip every Function with unit tests and E2E tests, ensuring neither individual Functions nor the full pipeline regress

After: observable functional pipeline

flowchart TB
  subgraph exec["execution — compose(f₄ ∘ f₃ ∘ f₂ ∘ f₁)"]
    direction LR
    ORCH["SKILL.md<br/>orchestration only"] --> F1["f₁ load_input<br/>(raw) → loaded"] --> F2["f₂ extract<br/>(loaded) → req"] --> F3["f₃ generate<br/>(req) → plan"] --> F4["f₄ validate<br/>(plan) → out"]
  end

  subgraph sup["supporting layers — read-only dependencies"]
    direction LR
    REF["references/"] --- SCR["scripts/"] --- TC["testcases/"] --- LOG["logs/runs/"]
  end

  exec --- sup

  style exec fill:#f0fff4,stroke:#2f855a,stroke-width:2px
  style sup fill:#ebf8ff,stroke:#2b6cb0,stroke-width:2px
  style ORCH fill:#e6ffed,stroke:#2f855a
  style F1 fill:#e6ffed,stroke:#2f855a
  style F2 fill:#e6ffed,stroke:#2f855a
  style F3 fill:#e6ffed,stroke:#2f855a
  style F4 fill:#e6ffed,stroke:#2f855a
  style REF fill:#ebf8ff,stroke:#2b6cb0
  style SCR fill:#ebf8ff,stroke:#2b6cb0
  style TC fill:#ebf8ff,stroke:#2b6cb0
  style LOG fill:#ebf8ff,stroke:#2b6cb0

Functional Skill does not aim to make Skills complex, but to put complexity where it belongs: judgment goes to Functions, rules go into references, deterministic actions go to scripts, and regression behavior solidifies into testcases.

When to Use

• You are maintaining a long-evolving agent skill and don't want to rely on gut feeling for every regression check.
• Your SKILL.md and references/ have become impossible to maintain manually, leaving you no choice but to blindly let AI iterate down one path.
• You want to extract deterministic work like parsing, formatting, and validation from prompts and execute them reliably through scripts.
• You want your skill's functionality and execution flow to become traceable, so you can pinpoint exactly where each run went wrong.
• You want your skill to capture real failure cases and ideal runs, turning them into repeatable test suites.

In other words, if your Skill is concise, or you've already split it cleanly in a modular way and are confident it's maintainable — you don't need to make it Functional.

Report Log and Unittest Capabilities

Skills generated by fskill-creator include basic report / unittest tooling by default. Use include_report=false and include_unittest=false to disable them separately, or use include_viewers=true|false to control whether local viewers are generated.

• scripts/report.mjs: Writes function-level report logs, supports report_mode=off|local|remote.
• scripts/runtime.mjs: Exports runStep, writeStepReport, and applyReportMode for wrapping each Step in the function workflow.
• scripts/test_report.mjs: Validates that the report runtime can write JSONL and checks sensitive field redaction.
• scripts/test_cases.mjs: Runs testcases/**/*.case.json function input/output assertions, and can also export trace records as testcases.
• logs/runs/: JSONL traces written when report_mode=local.

Iteration Loop

Functional Skill encourages turning real execution traces into regression assets:

Run skill → Check trace → Review function behavior → Export testcase → Fix function → Run tests

If Function1 fails, add a testcase for Function1; if normalize_input is deterministic logic, push it down into scripts/ and write a script test. Problems stay at the layer where they occur — maintenance cost does not spread across the entire skill.

See methodology details in docs/functional-skill.md. Function contract specification in docs/function-contract.md. When to put logic into scripts/ in docs/scripting.md. Testing and trace in docs/testing.md and docs/observability.md.

What Migration Surfaces

Migration exposes structural issues that were already present in the legacy skill — I/O mismatches, blurred function boundaries, ambiguous step definitions, and similar. That is expected, not a migration failure. Review the proposal, fix the contracts, add testcases, and the skill will run more reliably than before.

Migration does not invent new problems — it makes existing ones visible.

Monolithic skills often work despite implicit handoffs: step 2 assumes something step 1 "obviously" produces, boundaries between parsing and judgment are fuzzy, and shared rules are duplicated across sections. When you split into function contracts, those assumptions become explicit — and mismatches show up immediately.

Common findings after migrate:

• I/O mismatch — a downstream function expects fields that upstream does not output
• Blurred boundaries — one legacy section maps to multiple functions, or one function owns too much
• Ambiguous definitions — inputs and outputs described in prose, not stable object fields

Treat these as migration output, not migration failure. Suggested follow-up:

1. Review migration_proposal and function contracts
2. Align pipeline I/O — rename fields, split or merge functions, add entries to references/shared-glossary.md
3. Run the skill once with trace enabled; export failing steps as testcases
4. Re-run tests until the pipeline is consistent

Fixing these issues is the point of migration. A skill with explicit, tested function contracts hallucinates less and fails more predictably than a prose wall that only worked by accident.

Repository Structure

skills/
  fskill-creator/        Create, maintain, or migrate functional skills
    sub-skills/
      create/            Form create_context from requirement brief
      migrate/           Form migration_context from a legacy skill directory
docs/                    Methodology and specifications
templates/               Reusable skill templates
examples/                Runnable functional skill examples

Project Status

Currently v0.1.0 alpha. File formats and script conventions are usable, but may still adjust before 1.0.

This project does not bind to any agent platform, model vendor, or workflow engine. The built-in testcase runner is a runtime-agnostic assertion engine — it only validates output, does not execute agents or call models.

Contributing

Issues and PRs welcome. See CONTRIBUTING.md for development guide. For security-sensitive submissions, please read SECURITY.md first.

License

MIT. See LICENSE.