holocron/plugins/kyberforge/skills/skill-author/SKILL.md

name, description, allowed-tools, metadata

name	description	allowed-tools	metadata
skill-author	Use when the user wants to create a new skill from scratch ("write a skill for X", "build a skill that does Y", "create a SKILL.md for Z"), or improve an existing one ("improve this skill", "fix based on feedback", "apply these audit findings", "update based on grill output"). Also use when the user provides inline feedback about a skill's behavior and wants it applied, or when a grill session, eval run, or audit has produced findings the user wants acted on — even if they don't say "improve" explicitly. Authors and refines skills following the agentskills.io specification. Do not use for read-only review — use /skill-audit instead. Do not use to author agent definition files.	Bash Read Write Edit	category source_keys factory agentskills-home agentskills-spec agentskills-best-practices agentskills-optimizing-descriptions agentskills-evaluating-skills agentskills-using-scripts agentskills-quickstart

Gotchas

• Patching per symptom is the default failure mode. Three eval failures may all trace to one missing instruction — always identify the root cause before editing.
• Do not create new scripts unless a signal explicitly calls for it. Writing scripts from scratch requires transcript analysis that is out of scope here; flag the opportunity as a suggestion instead.

Route

Determine which flow to follow before touching the filesystem:

• No skill directory at the target path → follow Creating a new skill
• Directory exists + at least one improvement signal present → follow Improving an existing skill
• Directory exists + no signals present → ask: "No improvement signals found. Did you mean to create a new skill, or do you have feedback to apply?"

Signals include: grill session output, /skill-audit findings (PASS/FAIL punch list), inline user feedback, session context describing what went wrong.

Creating a new skill

Prerequisites

Run /grill-me on the skill's design and research the target domain first. Share those outputs in this conversation: grill context, research docs, examples, constraints.

Design for one coherent user intent — skills too narrow force multiple loads per task; too broad are hard to activate precisely. Extract the skill from a real task you've done — a skill refined from real execution outperforms one written speculatively.

Before touching the filesystem, verify you have:

• A clear purpose — what specific task will this skill handle?
• Trigger scenarios — when should an agent activate it, including indirect cases?
• Skill name (kebab-case) and destination path

If any are missing, stop and ask the user before proceeding.

Requires /skill-audit — used in Step 6 for final validation. Both skills ship in the kyberforge plugin and are co-installed. If /skill-audit is unavailable, stop and ask the user to install the kyberforge plugin before continuing.

Step 1 — Scaffold

Run the copy script with the skill name and destination directory:

bash scripts/new-skill.sh <skill-name> <destination-dir>

Examples:

bash scripts/new-skill.sh my-tool ~/.agents/skills/
bash scripts/new-skill.sh data-analyzer plugins/myplugin/skills/

This creates <destination-dir>/<skill-name>/ with annotated templates ready to fill in.

If the destination is inside a plugin directory (path contains a plugin.json), read references/deployment-modes.md before adding any file references to SKILL.md.

Step 2 — Fill in SKILL.md

Open <destination-dir>/<skill-name>/SKILL.md. Replace every FILL IN: placeholder.

Frontmatter

name — already set by the scaffold script. Must exactly match the directory name. Format: 1–64 characters, lowercase letters/numbers/hyphens only, no leading, trailing, or consecutive hyphens (--).

description — carries the entire triggering burden. Rules:

• Imperative: "Use when..." not "This skill..."
• Focus on user intent, not implementation — describe what the user is trying to achieve, not the skill's internal mechanics
• Specific about capabilities ("parses and validates OpenAPI specs", not "helps with APIs")
• Include indirect triggers: "even if the user doesn't mention X explicitly"
• Add "Do not use when..." only if a near-miss skill exists that could steal activations
• Hard limit: 1024 characters — count before finalizing

Optional fields — uncomment and fill in or remove entirely:

• license — include when distributing the skill externally
• compatibility — include if the skill requires specific tools, runtimes, or network access (max 500 characters)
• metadata — key-value map; use author, version, category
• allowed-tools — space-separated pre-approved tools; reduces permission prompts (experimental — support varies by client)

Body — include only what the agent lacks

Rename the placeholder section heading to one that fits the skill's structure — ## Step 1, ## Workflow, ## Instructions, etc.

Ask of every sentence: "Would the agent get this wrong without it?" Cut anything that answers "no."

Include:

• Non-obvious sequences or ordering constraints — the agent may skip or reorder steps without this
• Domain conventions the agent cannot infer from general knowledge — this is the core value a skill adds
• One default per decision point, plus one escape hatch — never a menu; menus cause the agent to pause or pick arbitrarily
• Gotchas — facts that defy reasonable assumptions; the agent will get these wrong every time without them

Exclude:

• Concepts the agent already knows (what JSON is, how HTTP works) — adds tokens without changing behavior
• Exhaustive option lists — pick a default; the agent doesn't benefit from choosing
• Steps the agent handles independently — over-specifying leads agents to follow unproductive paths
• Restatements of the description — it's already in context; repeating it wastes the token budget

Patterns

Gotchas — highest value; place near the top:

## Gotchas
- <Fact that defies a reasonable assumption>
- <Non-obvious naming discrepancy or hidden constraint>

Default with escape hatch (not a menu):

Use <X> for <task>. For <edge case>, use <Y> instead.

Prescriptive sequence (when order is critical or fragile):

Run exactly:
```bash
<command>
```
Do not modify flags.

Checklist (multi-step workflows):

- [ ] Step 1: ...
- [ ] Step 2: ...

Conditional reference (progressive disclosure — load only when needed):

If <condition>, read `references/<file>.md`.

Output format template (when the skill produces structured output):

Output format:
```
<field>: <value>
<field>: <value>
```

For longer templates, place in assets/<name>.md and reference conditionally.

Size budget

Keep SKILL.md under 500 lines; 5,000 tokens is the recommended body budget. When approaching the limit:

• Move reference material to references/<topic>.md and load it conditionally
• Bundle repeated executable logic into scripts/ rather than reinventing each run

Step 3 — Add scripts (if needed)

Place executable scripts in scripts/. Critical rule: no interactive prompts — agents run non-interactive; blocking on TTY input hangs indefinitely. Accept all input via flags, env vars, or stdin.

Read references/scripts.md before writing any script — it covers the full contract: structured output, pinned versions, self-contained deps, idempotency, exit codes, dry-run, error messages, and output size limits.

If no scripts are needed, delete scripts/README.md and the scripts/ directory.

Step 4 — Add references, assets, and tests (if needed)

references/ — additional documentation loaded on demand. One topic per file. Reference conditionally from SKILL.md: If <condition>, read references/<file>.md. Keep reference chains one level deep — a reference file that references another reference file is rarely loaded correctly.

assets/ — static resources: templates, schemas, lookup tables. Reference by relative path from SKILL.md.

tests/ — test files for scripts in scripts/. Use when scripts are complex enough to break silently. Test infrastructure (.bats, *_test.*) belongs here, not in scripts/. See tests/README.md for setup instructions.

If not needed, delete the placeholder READMEs and their directories.

Step 5 — Populate or delete references/sources.md

If a research sources.md is present in the conversation context:

1. Read it and filter to entries with `extracted` status only.
2. For each entry, determine which skill files it contributed to (SKILL.md and any files in references/ that drew from it). Update Contributing files accordingly — list skill files, not research topic files.
3. Write the updated content to references/sources.md.
4. Add source_keys to the frontmatter of SKILL.md (under metadata) listing the slugs of sources that informed it.
5. For each file in references/ that was informed by research sources, add source_keys frontmatter (same format as research topic files) listing the relevant slugs.

If no research sources.md is in context, delete references/sources.md.

Step 6 — Validate and close

Run /skill-audit on <destination-dir>/<skill-name>.

All FAIL findings must be resolved before the skill is considered done.

Improving an existing skill

Step 1 — Verify inputs

Confirm the skill directory path exists and that at least one improvement signal is present in the conversation or a referenced file.

If the skill dir is missing, ask for it. If no signals are present, stop: "This skill applies existing signals to a skill. For a blind review without signals, use /skill-audit instead."

Signals can come from anywhere in the conversation or referenced files:

• Grill session output (most common predecessor in the factory sequence)
• /skill-audit findings (PASS/FAIL/SUGGESTION punch list)
• Human feedback (feedback.json, inline in conversation, PR or issue comments)
• Session context describing what went wrong

Also verify the name field in frontmatter matches the skill's directory name exactly.

Step 2 — Gather and group signals

Read the current skill files (SKILL.md and any files in scripts/, references/, assets/, tests/). Then collect all signals from the conversation and any file paths the user has referenced.

Group signals by root cause, not symptom. Ask: "What single gap in the skill causes this cluster of failures?" One root cause → one fix. Do not make a separate edit for each symptom.

Example:
- Session context: output format is wrong on every run
- Audit finding: no output template defined
- User feedback: "I always have to ask it to format the output"
→ Root cause: SKILL.md has no output format specification → one fix: add an output template

Step 3 — Announce planned changes

Before editing, state:

• Which root causes were identified and what evidence supports each
• Which files will be changed and what will change in each

Then proceed — edits are reversible via git, no approval checkpoint needed.

Step 4 — Apply changes

Edit any file in the skill directory that the signals point to: SKILL.md, scripts/, references/, assets/, tests/, README.md.

Generalize, don't patch. Find the underlying gap, not the specific example that failed. A fix scoped only to the test cases you've seen will overfit and perform worse on new inputs.

Keep it lean. Remove instructions that aren't pulling their weight. For every sentence you add, ask: "Would the agent get this wrong without it?" A shorter, focused skill consistently outperforms an exhaustive one.

Explain the why. Reasoning-based instructions outperform rigid directives. If you find yourself writing a rule in all caps (ALWAYS/NEVER), reframe it: explain why the behavior matters so the agent can apply judgment in edge cases.

If a signal points to a script or reference file, edit that file directly rather than adding a workaround in SKILL.md.

On scripts: Fix and edit existing scripts freely when signals point to them.

Step 5 — Validate and close

Run /skill-audit on the skill directory. Resolve any FAIL findings before considering the improvement complete.