skill-scaffold
Skill6 starsChanged 52 days agoMITView on GitHub
Use when creating new skills, commands, or plugins — emits repo-native seed files (SKILL.md skeleton, routing entry, test snippets)
1 other project uses a version of this.
Adopted by
Most recently taken up by
- gerivdb/SKILLStheir own version, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- gerivdb/SKILLS★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository27 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-safety-reviewSkill
- agent-team-executionSkill
- agent-team-reviewSkill
- alert-hygieneSkill
- authorial-judgmentSkill
- batch-scriptingSkill
- capture-knowledgeSkill
- deploy-gateSkill
- design-debateSkill
- implementation-drift-checkSkill
- improvement-minerSkill
- incident-analysisSkill
- incident-trend-analyzerSkill
- openspec-shipSkill
- outcome-reviewSkill
- panelSkill
- product-discoverySkill
- project-verificationSkill
- prototype-labSkill
- runtime-validationSkill
- second-opinionSkill
- security-scannerSkill
- supply-chain-investigationSkill
- synthesizeSkill
- unified-context-stackSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Ask: What are we creating?
- Distinctive: line 159
Does not auto-register skills -- registration is a deliberate step during REVIEW.
- Distinctive: line 161
All output follows Bash 3.2 and repo JSON conventions.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill Scaffold
Emit seed files for new additions to auto-claude-skills. Ensures agents follow existing patterns from the first file.
When to Use
During DESIGN phase when creating new skills, commands, plugins, hooks, or modules. Co-selects with writing-skills.
Step 1: Identify Addition Type
Ask: What are we creating?
| Type | Skeleton | Notes |
| Domain skill | SKILL.md + routing entry + routing test + content assertion | Default. Most new additions are domain skills. |
| Workflow skill | SKILL.md + routing entry + routing test + composition test | Include precedes/requires fields. |
| Edge-overlay process skill | SKILL.md + routing entry + routing test + content assertion | Restricted to DISCOVER and LEARN phases only. If the user requests a process skill for a superpowers-owned phase (DESIGN, PLAN, IMPLEMENT, REVIEW, SHIP, DEBUG), emit a warning: "This phase's process driver is owned by superpowers. Consider a domain skill instead." |
| Hook | Script + config entry + syntax test | Bash 3.2 compatible. |
| Command | Command markdown + setup registration | Follow existing commands/ pattern. |
Step 2: Emit SKILL.md Skeleton
Generate based on type. Include frontmatter, tiered detection where applicable, and output contract section.
Description rule: the frontmatter description states what the skill is for and when to use it. Do not summarize the workflow steps — an agent may follow the summary instead of reading the full skill.
Domain skill SKILL.md skeleton:
---
name: <skill-name>
description: <one-line description>
---
# <Skill Name>
<One paragraph purpose statement.>
## When to Use
<Phase and activation context.>
## Step 1: Detect Available Tools
<Tiered detection pattern if the skill depends on external tools.>
## Step 2: <Primary Action>
<Core behavior.>
## Output Contract
<What the skill produces -- artifacts, reports, structured data.>Optional anatomy sections (discipline / readiness-claim skills)
Add these ONLY where they earn their place. A skill that enforces a discipline, is commonly skipped or shortcut under pressure, or makes a completion/readiness claim should carry the matching section. Keep a section only if it earns its place -- delete the rest. Filler anatomy on tool/domain skills is the anti-goal.
| Section | Keep when | Shape |
## Rationalizations | the skill is commonly skipped or shortcut under pressure | table of excuse -> reality |
## Red Flags | the skill enforces a discipline with known failure modes | bulleted anti-patterns / "thought -> STOP" list |
## Verification | the skill makes a completion or readiness claim | "Before claiming <X>, confirm: ..." evidence-before-assertions checklist |
Template (paste, then keep-or-delete per the table above):
## Rationalizations
| Excuse | Reality |
|--------|---------|
| "<excuse the agent makes to skip this>" | "<why that's wrong>" |
## Red Flags
- **<anti-pattern>** -- <why it means stop>
## Verification
Before claiming <the skill's specific completion claim>, confirm -- with evidence, not inference:
- <observable check tied to the claim>The qualifier after confirm -- is the canonical default; adapt it to the claim's failure mode (e.g. do not infer for state checks, from observed output for execution checks).
Step 3: Emit Routing Entry Snippet
Generate a JSON snippet for config/default-triggers.json:
{
"name": "<skill-name>",
"role": "domain",
"phase": "<PHASE>",
"triggers": [
"<regex-pattern>"
],
"keywords": ["<keyword1>", "<keyword2>"],
"trigger_mode": "regex",
"priority": 15,
"precedes": [],
"requires": [],
"description": "<one-line description>",
"invoke": "Skill(auto-claude-skills:<skill-name>)"
}The routing entry MUST be added to both config/default-triggers.json and config/fallback-registry.json.
The routing entry description field follows the same description rule as the frontmatter: purpose and when-to-use, never workflow steps.
Step 4: Emit Test Snippets
Routing fixture (tests/fixtures/routing/<skill-name>.txt) — the deterministic
done-gate. REQUIRED for every owned skill that has a trigger regex:
# <skill-name> routing fixtures
MATCH: <realistic prompt the trigger regex matches>
MATCH: <a second, differently-phrased positive>
NO_MATCH: <a decoy borrowed from another skill's fixture — must NOT match>Borrow the NO_MATCH decoy from an existing tests/fixtures/routing/*.txt so an
over-broad regex fails the gate. tests/test-fixture-coverage.sh fails the build
if this file is missing or lacks a MATCH or NO_MATCH line.
Trigger-accuracy eval (skills/<skill-name>/evals/evals.json) — optional but
recommended; the held-out LLM trigger eval (skill-creator / skill-eval.yml)
reads it. See docs/eval-pack-schema.md.
Content/behavior assertion (for a tests/test-<skill>-content.sh): keep the
SKILL.md non-empty + frontmatter name: check.
test_<skill_name>_content_contract() {
echo "-- test: <skill-name> SKILL.md has required sections --"
local skill_file="${PROJECT_ROOT}/skills/<skill-name>/SKILL.md"
# Bash 3.2: declare `local` and assign on SEPARATE lines. `local x=$(...)`
# swallows the command's exit code (local always returns 0), masking failures.
local content
content="$(cat "${skill_file}" 2>/dev/null || echo "")"
assert_not_empty "<skill-name> SKILL.md exists and is non-empty" "${content}"
assert_contains "<skill-name> has frontmatter name field" "name:" "${content}"
}Constraints
- Produces snippets, not complete files. The user or agent integrates them.
- Does not auto-register skills -- registration is a deliberate step during REVIEW.
- Adapts skeleton to skill type (domain, workflow, edge-overlay process).
- All output follows Bash 3.2 and repo JSON conventions.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

