Script Agents

name: script-agents description: Build and maintain minimal, role-aware prompt-dispatch scripts for coding agents. Use when: (1) creating one-shot report/review scripts, (2) wiring deterministic context artifacts into an LLM dispatch step, (3) standardizing configurable harness dispatch contracts (AGENT_DISPATCH_DIR + AGENT_DISPATCH_CMD) without scope bloat. category: meta user-invocable: true

Script Agents

Design and maintain small, high-signal scripts where deterministic local orchestration feeds a configurable coding-agent harness for one-shot analysis.

Standard (2026-02)

For every new or updated script-agent script:

1. CLI ergonomics

• Support --no-dispatch for deterministic local execution.
• Support --debug when artifact inspection is useful.
• Keep defaults zero-config for this machine, but all runtime integrations overrideable via env vars.

2. Dispatch contract

• Use exactly these knobs for model dispatch:
  • AGENT_DISPATCH_DIR
  • AGENT_DISPATCH_CMD
• Parse dispatch command safely for shell array execution:
  • typeset -a DISPATCH_CMD_ARR
  • DISPATCH_CMD_ARR=(${(z)AGENT_DISPATCH_CMD})

3. Artifact contract

• Write timestamped artifacts to a predictable project-owned directory.
• Write/update a stable latest-* pointer when repeated runs are expected.
• In --no-dispatch, still emit a useful local report artifact (do not no-op).

4. Validation contract

• Fail fast on missing inputs and empty outputs.
• Validate required headings/fields for machine-readability.
• Include a cheap smoke path that can run without networked model calls.

5. Prompt contract

• Keep static instructions at top and variable context at bottom.
• Favor XML/section-tagged blocks for deterministic parsing.
• Prefer schema-constrained outputs for structured sections where possible.

Core Pattern (Fresh-Eyes + RR Prompt A)

Use this exact sequence:

1. Goal sentence

• State the project goal in one sentence.

2. Input contract

• List exact files/directories consumed.
• Prefer one JSON context artifact.

3. Output contract

• Define exact output files and section requirements.
• Keep outputs self-contained and machine-readable where possible.

4. Scope trim (RR A discipline)

• Keep only what is needed for the current milestone.
• Defer speculative integrations and abstraction layers.

5. Dispatch contract

• Use two knobs only:
  • AGENT_DISPATCH_DIR
  • AGENT_DISPATCH_CMD
• Provide a sensible project default, but keep it overrideable.
• Allow override to any compatible pi harness command.

6. Validation

• Verify script can run with and without dispatch (--no-dispatch style path when useful).
• Verify artifacts are non-empty and linked via a latest-* pointer when applicable.

Hard Rules

• Keep scripts close to the owning project (for example scripts/).
• Keep prompts and context artifacts in predictable artifact paths.
• Avoid role-specific hardcoding in code when a profile file already contains source-of-truth data.
• Prefer deterministic generation steps before LLM dispatch.
• Do not introduce a framework; use shell + small embedded Python/Node only where required.
• Follow the dogfood skill (/Users/amar/agi/skills/dogfood/SKILL.md).
• Do not work around broken script UX with manual path gymnastics or direct replacement commands.
• If a script is awkward or fails in normal usage, fix the script and re-run it.

SOTA Guardrails

Apply these defaults unless the project has a hard reason not to:

1. Schema-first outputs

• Prefer Structured Outputs / strict tool schemas over free-form JSON.
• Avoid plain "JSON mode" when schema adherence is needed.

2. Explicit tool control

• Use tool_choice deliberately (auto, required/any, or forced tool) per step intent.
• Keep tool surface minimal and role-specific.

3. Cache-friendly prompt layout

• Put reusable static content first, dynamic context last, so prompt caching works predictably.

4. Eval + trace loop

• Add lightweight regression checks for output shape/sections.
• Add periodic trace/eval review for agentic steps, not just final output snapshots.

5. Injection resistance

• Treat external text as untrusted.
• Extract validated fields before allowing downstream tool-driving behavior.

Minimal Script Skeleton

#!/usr/bin/env zsh
set -euo pipefail

AGENT_DISPATCH_DIR="${AGENT_DISPATCH_DIR:-/Users/amar/agi/claudette}"
AGENT_DISPATCH_CMD="${AGENT_DISPATCH_CMD:-node --import tsx bin/claudette.ts --profile scope --provider codex --no-session}"

typeset -a DISPATCH_CMD_ARR
DISPATCH_CMD_ARR=(${(z)AGENT_DISPATCH_CMD})

# 1) Build deterministic context JSON
# 2) Assemble prompt
# 3) Optional no-dispatch path
# 4) Dispatch
(
  cd "$AGENT_DISPATCH_DIR" && \
  CLAUDETTE_SKIP_FORCED_EVAL=1 "${DISPATCH_CMD_ARR[@]}" -p "$(cat "$PROMPT_FILE")"
) > "$REPORT_FILE"

Reusable Outputs

1. context-<ts>.json (deterministic input snapshot)
2. prompt-<ts>.md (exact prompt sent to harness)
3. report-<ts>.md (dispatch output)
4. latest-* pointers where continuous refresh is useful

Example Mapping (Clunkers + Claudette)

• clunkers/scripts/* as orchestration owner
• AGENT_DISPATCH_DIR=/Users/amar/agi/claudette
• AGENT_DISPATCH_CMD="node --import tsx bin/claudette.ts --profile scope --provider codex --no-session"

Treat this as an example instantiation, not a required stack.

Sources (SOTA References)

As of 2026-02-17:

• OpenAI Structured Outputs: https://developers.openai.com/api/docs/guides/structured-outputs
• OpenAI Function Calling (tool_choice, strict schemas): https://developers.openai.com/api/docs/guides/function-calling
• OpenAI Safety in building agents: https://developers.openai.com/api/docs/guides/agent-builder-safety
• OpenAI Trace grading: https://developers.openai.com/api/docs/guides/trace-grading
• OpenAI Evaluation best practices: https://developers.openai.com/api/docs/guides/evaluation-best-practices
• Anthropic prompt caching: https://platform.claude.com/docs/en/build-with-claude/prompt-caching
• Anthropic tool use implementation (tool_choice, strict tools): https://platform.claude.com/docs/en/agents-and-tools/tool-use/implement-tool-use
• Anthropic prompt structuring (XML tags): https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/use-xml-tags
• MCP spec (official repo): https://github.com/modelcontextprotocol/modelcontextprotocol
• Google A2A protocol overview: https://a2a.cx/