tool-prompt-optimization

---

name: tool-prompt-optimization description: Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool prompt vs what stays in code. Use when auditing, trimming, writing, or reviewing tool prompts, deciding what schema field descriptions already cover, or testing schema-vs-prompt overlap before deleting prompt lines.

Tool Prompt Optimization

A tool's description prompt and its parameter schema overlap. Whatever a model can reconstruct from the schema + tool name + a blank outline is a prune candidate — the schema may already teach it. This skill measures that overlap so you prune with evidence, not vibes. A candidate is never an automatic delete (see caveats — history first).

Core move: give a model only (name, JSON schema, outline) and have it predict the prompt body. Lines it predicts reliably are prune candidates. Lines it never recovers are load-bearing — keep them.

Run the probe

scripts/probe.ts routes through @pk-nerdsaver-ai/pi-ai (completeSimple) so model/auth/provider behavior matches production.

bun .omp/skills/tool-prompt-optimization/scripts/probe.ts \
  --schema <file|json> --template <file|text> --name <tool_name>

• --schema and --template are the only required inputs (file path or inline value).
• No --model → 3-model panel (fireworks/kimi-k2.7-code, anthropic/claude-opus-4-8, openai/gpt-5.5) × --samples (default 3). Needs FIREWORKS_API_KEY / ANTHROPIC_API_KEY / OPENAI_API_KEY.
• --model p/id,p/id overrides the panel; --samples N, --max-tokens, --json tune it.
• Programmatic: import { probe } from "./scripts/probe.ts" → { prompt, results: [{ model, samples: [{ text, stopReason, usage, error }] }] }.

Builtin shortcut (preferred for this repo's tools)

Skip building the two inputs by hand — scripts/probe-builtin.ts instantiates the live tool, pulls the EXACT wire schema (toolWireSchema) and rendered prompt (tool.description), and derives the outline for you:

bun .omp/skills/tool-prompt-optimization/scripts/probe-builtin.ts --tool <name> [--no-summary] [--show]

• --show prints the resolved schema + derived outline + real prompt and exits (no API calls) — use it to eyeball inputs before spending tokens.
• --no-summary runs the ablation (blank the summary line) directly.
• --samples / --model / --max-tokens / --json forward to the panel; output ends with the REAL prompt so you can diff in place.
• It bypasses the settings allowlist via the factory map, so gated tools (irc, github, …) resolve. If a tool refuses to construct (an availability gate like a missing gh CLI), fall back to the manual inputs below.

Build the two inputs

Schema — use the wire schema the model actually sees, not a hand-sketch. For this repo's arktype tool schemas:

import { arkToWireSchema } from "@pk-nerdsaver-ai/pi-ai"; // or toolWireSchema(tool)
JSON.stringify(arkToWireSchema(toolSchema), null, 2);

Include required and additionalProperties: false — omitting them makes the model infer looser usage than the real tool.

Template (outline) — the real .md's structure with bodies blanked: the one-line summary, then each section tag with ... inside.

Structural code search via native ast-grep AST matching.

<instruction>
...
</instruction>

<output>
...
</output>

<critical>
...
</critical>

Interpret results

Bucket every line of the real prompt against the predictions:

• Prune candidate — content that is STABLE across samples AND agrees across models AND restates the schema (param names, types, "required", value examples already in a field description, clamp ranges already stated). The schema teaches it; the prompt repeats it.
• Keep — content no model recovers: defaults and their direction (gitignore default true), cross-tool routing/escalation ("NEVER shell out to find/fd → use this tool", "broad exploration → Task subagent"), exact output format (mtime sort, grouping, artifact:// truncation), worked anti-patterns, and hard constraints invisible to a type (the AST metavariable grammar, C++ trailing ;).

A single sample is noise. Only treat overlap that is stable across samples and models as a prune candidate — and a candidate is not a verdict until its history clears (see caveats). You MUST NOT delete a line on inferability alone.

Caveats — read before deleting anything

• git blame before cutting — MUST, not SHOULD. Many prompt lines were added on purpose after a real failure: a model that hallucinated a flag, shelled out, scanned the repo root, fabricated an anchor. They look redundant precisely because they now prevent the mistake. You MUST git blame (and read the commit/issue) every line you intend to cut; the history tells you whether it restates the schema or is scar tissue from an incident. Keep scar tissue. Inferability is necessary for pruning, NEVER sufficient.
• Memorization ≠ inference. Public repos (this one included) may be in training data, so a model can recite ast-grep.md it never inferred. Tell: predictions naming repo-specific details absent from the schema (exact tool names, internal URI schemes, the Task subagent) are memorized, not derived — discount them.
• The outline leaks. The summary line and section names are themselves hints. To isolate schema-alone inferability, run an ablation: a second pass with no summary line and generic section tags. Content that survives only with the summary present is "summary-inferable", not "schema-inferable".

Verdict pattern

Per tool: predictions reproduce parameter mechanics and generic usage (already in the schema) but miss defaults, output shape, cross-tool routing, anti-patterns, and domain grammar. Prune the first set (after git blame clears each line); keep the second. Self-documenting flag tools (e.g. find) prune heavily; DSL/capability tools (e.g. read, ast_grep) barely at all.

Tool Prompt Authoring

Tool prompts are not API docs. They teach the agent when to reach for the tool, what shape its inputs take, and which failure modes are the agent's responsibility. Everything else — engine internals, recovery heuristics, fallback chains, performance tuning — stays in code.

Describe surface, not machinery

The agent picks tools from prose, not source. Tell it WHEN and WHY; NEVER HOW the tool works internally.

• read.md enumerates every source it covers (file/dir/archive/sqlite/PDF/URL) so the agent stops reaching for cat/curl/tar. It does NOT mention the chunker, the binary sniffer, or the cache layer.
• lsp.md: "You MUST use lsp whenever a language server is available — safer than text-based alternatives." No mention of the LSP wire protocol, server lifecycle, or capability negotiation.
• ast_edit: teaches metavariable syntax + workflow ("Loosest existence check: pat: 'executeBash' with narrow paths"). Does NOT explain the AST engine, query compilation, or tree-sitter grammar selection.
• hashline.md (this repo): teaches the patch grammar (anchors, ops, payloads, ranges) and the edit shapes that succeed. Hides tryRecoverHashlineWithCache, the fuzz factor, the bigram tables, findUniqueSuffixMatch, untilAborted, formatGroupedFiles. The agent never learns those names — it just sees "the tool resolved your typo" or "the anchor was stale, re-read".

If the agent's behavior shouldn't change based on a detail, the detail does NOT belong in the prompt. Each sentence MUST shift a decision the agent makes.

Anatomy of a good tool prompt

1. One-line purpose. What problem it solves, in the agent's vocabulary. Not "wraps libfoo with X" — instead "compact, line-anchored edit format".
2. Input grammar / surface. Operators, parameters, selectors. Concrete syntax the agent will emit verbatim.
3. Worked examples. 3–8 patterns covering the common shapes. Each example IS the explanation — don't narrate it twice.
4. Failure shapes the agent owns. Things the agent can fix by changing its input (stale anchors, missing payload prefix, fabricated hash). Skip failures the engine recovers from silently.
5. Anti-patterns. WRONG/RIGHT pairs for the mistakes that cost retries. Drawn from real failures, not imagined ones.
6. <critical> recap. 3–6 lines of the load-bearing rules, in case the agent skips the body.

What stays out

• Implementation file names, function names, module layout.
• Recovery, retry, normalization, caching, fuzz matching.
• Performance characteristics ("this is O(n)") unless they change the agent's strategy.
• Telemetry, logging, debug flags, env vars the agent cannot set.
• Version history, deprecated parameters, "previously this worked differently".
• Cross-tool plumbing ("this calls read under the hood") unless the agent must coordinate them.