chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# JSON Schemas
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This document defines the JSON schemas used by skill-creator.
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---
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## evals.json
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Defines the evals for a skill. Located at `evals/evals.json` within the skill directory.
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```json
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{
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"skill_name": "example-skill",
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"evals": [
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{
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"id": 1,
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"prompt": "User's example prompt",
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"expected_output": "Description of expected result",
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"files": ["evals/files/sample1.pdf"],
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"expectations": [
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"The output includes X",
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"The skill used script Y"
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]
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}
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]
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}
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```
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**Fields:**
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- `skill_name`: Name matching the skill's frontmatter
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- `evals[].id`: Unique integer identifier
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- `evals[].prompt`: The task to execute
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- `evals[].expected_output`: Human-readable description of success
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- `evals[].files`: Optional list of input file paths (relative to skill root)
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- `evals[].expectations`: List of verifiable statements
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---
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## history.json
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Tracks version progression in Improve mode. Located at workspace root.
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```json
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{
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"started_at": "2026-01-15T10:30:00Z",
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"skill_name": "pdf",
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"current_best": "v2",
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"iterations": [
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{
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"version": "v0",
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"parent": null,
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"expectation_pass_rate": 0.65,
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"grading_result": "baseline",
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"is_current_best": false
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},
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{
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"version": "v1",
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"parent": "v0",
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"expectation_pass_rate": 0.75,
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"grading_result": "won",
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"is_current_best": false
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},
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{
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"version": "v2",
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"parent": "v1",
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"expectation_pass_rate": 0.85,
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"grading_result": "won",
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"is_current_best": true
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}
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]
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}
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```
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**Fields:**
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- `started_at`: ISO timestamp of when improvement started
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- `skill_name`: Name of the skill being improved
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- `current_best`: Version identifier of the best performer
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- `iterations[].version`: Version identifier (v0, v1, ...)
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- `iterations[].parent`: Parent version this was derived from
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- `iterations[].expectation_pass_rate`: Pass rate from grading
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- `iterations[].grading_result`: "baseline", "won", "lost", or "tie"
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- `iterations[].is_current_best`: Whether this is the current best version
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---
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## grading.json
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Output from the grader agent. Located at `<run-dir>/grading.json`.
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```json
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{
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"expectations": [
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{
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"text": "The output includes the name 'John Smith'",
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"passed": true,
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"evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'"
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},
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{
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"text": "The spreadsheet has a SUM formula in cell B10",
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"passed": false,
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"evidence": "No spreadsheet was created. The output was a text file."
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}
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],
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"summary": {
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"passed": 2,
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"failed": 1,
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"total": 3,
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"pass_rate": 0.67
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},
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"execution_metrics": {
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"tool_calls": {
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"Read": 5,
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"Write": 2,
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"Bash": 8
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},
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"total_tool_calls": 15,
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"total_steps": 6,
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"errors_encountered": 0,
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"output_chars": 12450,
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"transcript_chars": 3200
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},
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"timing": {
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"executor_duration_seconds": 165.0,
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"grader_duration_seconds": 26.0,
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"total_duration_seconds": 191.0
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},
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"claims": [
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{
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"claim": "The form has 12 fillable fields",
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"type": "factual",
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"verified": true,
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"evidence": "Counted 12 fields in field_info.json"
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}
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],
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"user_notes_summary": {
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"uncertainties": ["Used 2023 data, may be stale"],
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"needs_review": [],
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"workarounds": ["Fell back to text overlay for non-fillable fields"]
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},
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"eval_feedback": {
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"suggestions": [
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{
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"assertion": "The output includes the name 'John Smith'",
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"reason": "A hallucinated document that mentions the name would also pass"
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}
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],
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"overall": "Assertions check presence but not correctness."
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}
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}
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```
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**Fields:**
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- `expectations[]`: Graded expectations with evidence
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- `summary`: Aggregate pass/fail counts
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- `execution_metrics`: Tool usage and output size (from executor's metrics.json)
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- `timing`: Wall clock timing (from timing.json)
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- `claims`: Extracted and verified claims from the output
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- `user_notes_summary`: Issues flagged by the executor
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- `eval_feedback`: (optional) Improvement suggestions for the evals, only present when the grader identifies issues worth raising
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---
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## metrics.json
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Output from the executor agent. Located at `<run-dir>/outputs/metrics.json`.
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```json
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{
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"tool_calls": {
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"Read": 5,
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"Write": 2,
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"Bash": 8,
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"Edit": 1,
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"Glob": 2,
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"Grep": 0
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},
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"total_tool_calls": 18,
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"total_steps": 6,
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"files_created": ["filled_form.pdf", "field_values.json"],
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"errors_encountered": 0,
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"output_chars": 12450,
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"transcript_chars": 3200
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}
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```
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**Fields:**
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- `tool_calls`: Count per tool type
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- `total_tool_calls`: Sum of all tool calls
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- `total_steps`: Number of major execution steps
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- `files_created`: List of output files created
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- `errors_encountered`: Number of errors during execution
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- `output_chars`: Total character count of output files
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- `transcript_chars`: Character count of transcript
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---
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## timing.json
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Wall clock timing for a run. Located at `<run-dir>/timing.json`.
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**How to capture:** When a subagent task completes, the task notification includes `total_tokens` and `duration_ms`. Save these immediately — they are not persisted anywhere else and cannot be recovered after the fact.
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```json
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{
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"total_tokens": 84852,
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"duration_ms": 23332,
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"total_duration_seconds": 23.3,
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"executor_start": "2026-01-15T10:30:00Z",
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"executor_end": "2026-01-15T10:32:45Z",
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"executor_duration_seconds": 165.0,
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"grader_start": "2026-01-15T10:32:46Z",
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"grader_end": "2026-01-15T10:33:12Z",
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"grader_duration_seconds": 26.0
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}
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```
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---
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## benchmark.json
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Output from Benchmark mode. Located at `benchmarks/<timestamp>/benchmark.json`.
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```json
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{
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"metadata": {
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"skill_name": "pdf",
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"skill_path": "/path/to/pdf",
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"executor_model": "claude-sonnet-4-20250514",
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"analyzer_model": "most-capable-model",
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"timestamp": "2026-01-15T10:30:00Z",
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"evals_run": [1, 2, 3],
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"runs_per_configuration": 3
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},
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"runs": [
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{
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"eval_id": 1,
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"eval_name": "Ocean",
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"configuration": "with_skill",
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"run_number": 1,
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"result": {
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"pass_rate": 0.85,
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"passed": 6,
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"failed": 1,
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"total": 7,
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"time_seconds": 42.5,
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"tokens": 3800,
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"tool_calls": 18,
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"errors": 0
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},
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"expectations": [
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{"text": "...", "passed": true, "evidence": "..."}
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],
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"notes": [
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"Used 2023 data, may be stale",
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"Fell back to text overlay for non-fillable fields"
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]
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}
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],
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"run_summary": {
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"with_skill": {
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"pass_rate": {"mean": 0.85, "stddev": 0.05, "min": 0.80, "max": 0.90},
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"time_seconds": {"mean": 45.0, "stddev": 12.0, "min": 32.0, "max": 58.0},
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"tokens": {"mean": 3800, "stddev": 400, "min": 3200, "max": 4100}
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},
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"without_skill": {
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"pass_rate": {"mean": 0.35, "stddev": 0.08, "min": 0.28, "max": 0.45},
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"time_seconds": {"mean": 32.0, "stddev": 8.0, "min": 24.0, "max": 42.0},
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"tokens": {"mean": 2100, "stddev": 300, "min": 1800, "max": 2500}
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},
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"delta": {
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"pass_rate": "+0.50",
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"time_seconds": "+13.0",
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"tokens": "+1700"
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}
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},
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"notes": [
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"Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value",
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"Eval 3 shows high variance (50% ± 40%) - may be flaky or model-dependent",
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"Without-skill runs consistently fail on table extraction expectations",
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"Skill adds 13s average execution time but improves pass rate by 50%"
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]
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}
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```
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**Fields:**
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- `metadata`: Information about the benchmark run
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- `skill_name`: Name of the skill
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- `timestamp`: When the benchmark was run
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- `evals_run`: List of eval names or IDs
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- `runs_per_configuration`: Number of runs per config (e.g. 3)
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- `runs[]`: Individual run results
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- `eval_id`: Numeric eval identifier
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- `eval_name`: Human-readable eval name (used as section header in the viewer)
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- `configuration`: Must be `"with_skill"` or `"without_skill"` (the viewer uses this exact string for grouping and color coding)
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- `run_number`: Integer run number (1, 2, 3...)
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- `result`: Nested object with `pass_rate`, `passed`, `total`, `time_seconds`, `tokens`, `errors`
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- `run_summary`: Statistical aggregates per configuration
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- `with_skill` / `without_skill`: Each contains `pass_rate`, `time_seconds`, `tokens` objects with `mean` and `stddev` fields
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- `delta`: Difference strings like `"+0.50"`, `"+13.0"`, `"+1700"`
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- `notes`: Freeform observations from the analyzer
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**Important:** The viewer reads these field names exactly. Using `config` instead of `configuration`, or putting `pass_rate` at the top level of a run instead of nested under `result`, will cause the viewer to show empty/zero values. Always reference this schema when generating benchmark.json manually.
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---
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## comparison.json
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Output from blind comparator. Located at `<grading-dir>/comparison-N.json`.
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```json
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{
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"winner": "A",
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"reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.",
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"rubric": {
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"A": {
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"content": {
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"correctness": 5,
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"completeness": 5,
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"accuracy": 4
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},
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"structure": {
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"organization": 4,
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"formatting": 5,
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"usability": 4
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},
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"content_score": 4.7,
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"structure_score": 4.3,
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"overall_score": 9.0
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},
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"B": {
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"content": {
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"correctness": 3,
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"completeness": 2,
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"accuracy": 3
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},
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"structure": {
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"organization": 3,
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"formatting": 2,
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"usability": 3
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},
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"content_score": 2.7,
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"structure_score": 2.7,
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"overall_score": 5.4
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}
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},
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"output_quality": {
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"A": {
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"score": 9,
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"strengths": ["Complete solution", "Well-formatted", "All fields present"],
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"weaknesses": ["Minor style inconsistency in header"]
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},
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"B": {
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"score": 5,
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"strengths": ["Readable output", "Correct basic structure"],
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"weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"]
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}
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},
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"expectation_results": {
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"A": {
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"passed": 4,
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"total": 5,
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"pass_rate": 0.80,
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"details": [
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{"text": "Output includes name", "passed": true}
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]
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},
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"B": {
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"passed": 3,
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"total": 5,
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"pass_rate": 0.60,
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"details": [
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{"text": "Output includes name", "passed": true}
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]
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}
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}
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}
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```
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---
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## analysis.json
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Output from post-hoc analyzer. Located at `<grading-dir>/analysis.json`.
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```json
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{
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"comparison_summary": {
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"winner": "A",
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"winner_skill": "path/to/winner/skill",
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"loser_skill": "path/to/loser/skill",
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"comparator_reasoning": "Brief summary of why comparator chose winner"
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},
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"winner_strengths": [
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"Clear step-by-step instructions for handling multi-page documents",
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"Included validation script that caught formatting errors"
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],
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"loser_weaknesses": [
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"Vague instruction 'process the document appropriately' led to inconsistent behavior",
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"No script for validation, agent had to improvise"
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],
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"instruction_following": {
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"winner": {
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"score": 9,
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"issues": ["Minor: skipped optional logging step"]
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},
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"loser": {
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"score": 6,
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"issues": [
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"Did not use the skill's formatting template",
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"Invented own approach instead of following step 3"
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]
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}
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},
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"improvement_suggestions": [
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{
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"priority": "high",
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"category": "instructions",
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"suggestion": "Replace 'process the document appropriately' with explicit steps",
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"expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
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}
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],
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"transcript_insights": {
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"winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script",
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||||
"loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods"
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||||
}
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||||
}
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||||
```
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+209
@@ -0,0 +1,209 @@
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---
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name: skill-creator
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description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
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||||
license: Complete terms in LICENSE.txt
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||||
---
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||||
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||||
# Skill Creator
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||||
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||||
This skill provides guidance for creating effective skills.
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||||
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||||
## About Skills
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||||
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||||
Skills are modular, self-contained packages that extend Claude's capabilities by providing
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specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
|
||||
domains or tasks—they transform Claude from a general-purpose agent into a specialized agent
|
||||
equipped with procedural knowledge that no model can fully possess.
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||||
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||||
### What Skills Provide
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||||
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||||
1. Specialized workflows - Multi-step procedures for specific domains
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||||
2. Tool integrations - Instructions for working with specific file formats or APIs
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||||
3. Domain expertise - Company-specific knowledge, schemas, business logic
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||||
4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks
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||||
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||||
### Anatomy of a Skill
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||||
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||||
Every skill consists of a required SKILL.md file and optional bundled resources:
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||||
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||||
```
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skill-name/
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├── SKILL.md (required)
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||||
│ ├── YAML frontmatter metadata (required)
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||||
│ │ ├── name: (required)
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||||
│ │ └── description: (required)
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||||
│ └── Markdown instructions (required)
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||||
└── Bundled Resources (optional)
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||||
├── scripts/ - Executable code (Python/Bash/etc.)
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||||
├── references/ - Documentation intended to be loaded into context as needed
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||||
└── assets/ - Files used in output (templates, icons, fonts, etc.)
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||||
```
|
||||
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||||
#### SKILL.md (required)
|
||||
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||||
**Metadata Quality:** The `name` and `description` in YAML frontmatter determine when Claude will use the skill. Be specific about what the skill does and when to use it. Use the third-person (e.g. "This skill should be used when..." instead of "Use this skill when...").
|
||||
|
||||
#### Bundled Resources (optional)
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||||
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||||
##### Scripts (`scripts/`)
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||||
|
||||
Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
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||||
|
||||
- **When to include**: When the same code is being rewritten repeatedly or deterministic reliability is needed
|
||||
- **Example**: `scripts/rotate_pdf.py` for PDF rotation tasks
|
||||
- **Benefits**: Token efficient, deterministic, may be executed without loading into context
|
||||
- **Note**: Scripts may still need to be read by Claude for patching or environment-specific adjustments
|
||||
|
||||
##### References (`references/`)
|
||||
|
||||
Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
|
||||
|
||||
- **When to include**: For documentation that Claude should reference while working
|
||||
- **Examples**: `references/finance.md` for financial schemas, `references/mnda.md` for company NDA template, `references/policies.md` for company policies, `references/api_docs.md` for API specifications
|
||||
- **Use cases**: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
|
||||
- **Benefits**: Keeps SKILL.md lean, loaded only when Claude determines it's needed
|
||||
- **Best practice**: If files are large (>10k words), include grep search patterns in SKILL.md
|
||||
- **Avoid duplication**: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
|
||||
|
||||
##### Assets (`assets/`)
|
||||
|
||||
Files not intended to be loaded into context, but rather used within the output Claude produces.
|
||||
|
||||
- **When to include**: When the skill needs files that will be used in the final output
|
||||
- **Examples**: `assets/logo.png` for brand assets, `assets/slides.pptx` for PowerPoint templates, `assets/frontend-template/` for HTML/React boilerplate, `assets/font.ttf` for typography
|
||||
- **Use cases**: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
|
||||
- **Benefits**: Separates output resources from documentation, enables Claude to use files without loading them into context
|
||||
|
||||
### Progressive Disclosure Design Principle
|
||||
|
||||
Skills use a three-level loading system to manage context efficiently:
|
||||
|
||||
1. **Metadata (name + description)** - Always in context (~100 words)
|
||||
2. **SKILL.md body** - When skill triggers (<5k words)
|
||||
3. **Bundled resources** - As needed by Claude (Unlimited*)
|
||||
|
||||
*Unlimited because scripts can be executed without reading into context window.
|
||||
|
||||
## Skill Creation Process
|
||||
|
||||
To create a skill, follow the "Skill Creation Process" in order, skipping steps only if there is a clear reason why they are not applicable.
|
||||
|
||||
### Step 1: Understanding the Skill with Concrete Examples
|
||||
|
||||
Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
|
||||
|
||||
To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
|
||||
|
||||
For example, when building an image-editor skill, relevant questions include:
|
||||
|
||||
- "What functionality should the image-editor skill support? Editing, rotating, anything else?"
|
||||
- "Can you give some examples of how this skill would be used?"
|
||||
- "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
|
||||
- "What would a user say that should trigger this skill?"
|
||||
|
||||
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
|
||||
|
||||
Conclude this step when there is a clear sense of the functionality the skill should support.
|
||||
|
||||
### Step 2: Planning the Reusable Skill Contents
|
||||
|
||||
To turn concrete examples into an effective skill, analyze each example by:
|
||||
|
||||
1. Considering how to execute on the example from scratch
|
||||
2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly
|
||||
|
||||
Example: When building a `pdf-editor` skill to handle queries like "Help me rotate this PDF," the analysis shows:
|
||||
|
||||
1. Rotating a PDF requires re-writing the same code each time
|
||||
2. A `scripts/rotate_pdf.py` script would be helpful to store in the skill
|
||||
|
||||
Example: When designing a `frontend-webapp-builder` skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
|
||||
|
||||
1. Writing a frontend webapp requires the same boilerplate HTML/React each time
|
||||
2. An `assets/hello-world/` template containing the boilerplate HTML/React project files would be helpful to store in the skill
|
||||
|
||||
Example: When building a `big-query` skill to handle queries like "How many users have logged in today?" the analysis shows:
|
||||
|
||||
1. Querying BigQuery requires re-discovering the table schemas and relationships each time
|
||||
2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill
|
||||
|
||||
To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
|
||||
|
||||
### Step 3: Initializing the Skill
|
||||
|
||||
At this point, it is time to actually create the skill.
|
||||
|
||||
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
|
||||
|
||||
When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
|
||||
|
||||
Usage:
|
||||
|
||||
```bash
|
||||
scripts/init_skill.py <skill-name> --path <output-directory>
|
||||
```
|
||||
|
||||
The script:
|
||||
|
||||
- Creates the skill directory at the specified path
|
||||
- Generates a SKILL.md template with proper frontmatter and TODO placeholders
|
||||
- Creates example resource directories: `scripts/`, `references/`, and `assets/`
|
||||
- Adds example files in each directory that can be customized or deleted
|
||||
|
||||
After initialization, customize or remove the generated SKILL.md and example files as needed.
|
||||
|
||||
### Step 4: Edit the Skill
|
||||
|
||||
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Claude to use. Focus on including information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.
|
||||
|
||||
#### Start with Reusable Skill Contents
|
||||
|
||||
To begin implementation, start with the reusable resources identified above: `scripts/`, `references/`, and `assets/` files. Note that this step may require user input. For example, when implementing a `brand-guidelines` skill, the user may need to provide brand assets or templates to store in `assets/`, or documentation to store in `references/`.
|
||||
|
||||
Also, delete any example files and directories not needed for the skill. The initialization script creates example files in `scripts/`, `references/`, and `assets/` to demonstrate structure, but most skills won't need all of them.
|
||||
|
||||
#### Update SKILL.md
|
||||
|
||||
**Writing Style:** Write the entire skill using **imperative/infinitive form** (verb-first instructions), not second person. Use objective, instructional language (e.g., "To accomplish X, do Y" rather than "You should do X" or "If you need to do X"). This maintains consistency and clarity for AI consumption.
|
||||
|
||||
To complete SKILL.md, answer the following questions:
|
||||
|
||||
1. What is the purpose of the skill, in a few sentences?
|
||||
2. When should the skill be used?
|
||||
3. In practice, how should Claude use the skill? All reusable skill contents developed above should be referenced so that Claude knows how to use them.
|
||||
|
||||
### Step 5: Packaging a Skill
|
||||
|
||||
Once the skill is ready, it should be packaged into a distributable zip file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
|
||||
|
||||
```bash
|
||||
scripts/package_skill.py <path/to/skill-folder>
|
||||
```
|
||||
|
||||
Optional output directory specification:
|
||||
|
||||
```bash
|
||||
scripts/package_skill.py <path/to/skill-folder> ./dist
|
||||
```
|
||||
|
||||
The packaging script will:
|
||||
|
||||
1. **Validate** the skill automatically, checking:
|
||||
- YAML frontmatter format and required fields
|
||||
- Skill naming conventions and directory structure
|
||||
- Description completeness and quality
|
||||
- File organization and resource references
|
||||
|
||||
2. **Package** the skill if validation passes, creating a zip file named after the skill (e.g., `my-skill.zip`) that includes all files and maintains the proper directory structure for distribution.
|
||||
|
||||
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
|
||||
|
||||
### Step 6: Iterate
|
||||
|
||||
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
|
||||
|
||||
**Iteration workflow:**
|
||||
1. Use the skill on real tasks
|
||||
2. Notice struggles or inefficiencies
|
||||
3. Identify how SKILL.md or bundled resources should be updated
|
||||
4. Implement changes and test again
|
||||
Reference in New Issue
Block a user