Skill Writer

Skill Writer

Use this as the single canonical workflow for skill creation and improvement. Primary success condition: maximize high-value input coverage before authoring so the resulting skill has minimal blind spots.

Load only the path(s) required for the task:

Step 1: Resolve target and path

1. Resolve target skill path and intended operation (create, update, synthesize, iterate).
2. Read references/mode-selection.md and select the required path(s).
3. Classify the skill (workflow-process, integration-documentation, security-review, skill-authoring, generic).
4. Ask one direct question if class or depth requirements are ambiguous; otherwise state explicit assumptions.

Step 2: Run synthesis when needed

Read references/synthesis-path.md.

1. Collect and score relevant sources with provenance.
2. Apply trust and safety rules when ingesting external content.
3. Produce source-backed decisions and coverage/gap status.
4. Load one or more profiles from references/examples/*.md when the skill is hybrid.
5. Enforce baseline source pack for skill-authoring workflows.
6. Enforce depth gates before moving to authoring.

Step 3: Run iteration first when improving from outcomes/examples

Read references/iteration-path.md first when selected path includes iteration (for example operation iterate).

1. Capture and anonymize examples with provenance.
2. Re-evaluate skill behavior against working and holdout slices.
3. Propose improvements from positive/negative/fix evidence.
4. Carry concrete behavior deltas into authoring.

Skip this step when selected path does not include iteration.

Step 4: Author or update skill artifacts

Read references/authoring-path.md.

1. Write or update SKILL.md in imperative voice with trigger-rich description.
2. Create focused reference files and scripts only when justified.
3. Follow references/skill-patterns.md, references/workflow-patterns.md, and references/output-patterns.md for structure and output determinism.
4. For authoring/generator skills, include transformed examples in references:
   • happy-path
   • secure/robust variant
   • anti-pattern + corrected version

Step 5: Optimize description quality

Read references/description-optimization.md.

1. Validate should-trigger and should-not-trigger query sets.
2. Reduce false positives and false negatives with targeted description edits.
3. Keep trigger language generic across Codex and Claude.

Step 6: Evaluate outcomes

Read references/evaluation-path.md.

1. Run a lightweight qualitative check by default (recommended).
2. For integration/documentation and skill-authoring skills, include the concise depth rubric from references/evaluation-path.md.
3. Run deeper eval playbook and quantitative baseline-vs-with-skill only when requested or risk warrants it.
4. Record outcomes and unresolved risks.

Step 7: Register and validate

Read references/registration-validation.md.

1. Apply repository registration steps.
2. Run quick validation with strict depth gates.
3. Reject shallow outputs that fail depth gates or required artifact checks.

Output format

Return:

1. Summary
2. Changes Made
3. Validation Results
4. Open Gaps

When to Use

Use this skill when tackling tasks related to its primary domain or functionality as described above.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Skill Writer"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags skill-writer ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.