Skip to main content

tool-prompt-optimization

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.

설치로 이동

소스 정보

저장소
can1357/oh-my-pi
최근 소스 활동
2026년 8월 11일 23:48
감지된 SKILL.md 언어
영어
스타
31,552
포크
3,330

설치 방법

기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.

소스 파일 검토

설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.

파일 탐색기
3 개 파일

SKILL.md 표시 중

SKILL.md
소스 지침 · 읽기 전용 미리보기
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 Prompt/schema overlap: content reconstructible from `(name, JSON schema, blank outline)` is a *prune candidate*, never an automatic delete. Probe this overlap for evidence, not vibes: predict the prompt body from those inputs. Reliably recovered lines: candidates; no-model recovery: load-bearing — keep. ## Run probe `scripts/probe.ts`: `@oh-my-pi/pi-ai` `completeSimple`; production-matching model/auth/provider behavior. ```bash bun .omp/skills/tool-prompt-optimization/scripts/probe.ts \ --schema <file|json> --template <file|text> --name <tool_name> ``` - Required: `--schema`, `--template` — file path or inline value. - No `--model`: panel `fireworks/kimi-k2.7-code`, `anthropic/claude-opus-4-8`, `openai/gpt-5.5` × `--samples` (default 3); requires `FIREWORKS_API_KEY` / `ANTHROPIC_API_KEY` / `OPENAI_API_KEY`. - `--model p/id,p/id`: override panel. Tune: `--samples N`, `--max-tokens`, `--json`. - Programmatic: `import { probe } from "./scripts/probe.ts"` → `{ prompt, results: [{ model, samples: [{ text, stopReason, usage, error }] }] }`. ### Builtin shortcut — preferred for this repo `scripts/probe-builtin.ts` instantiates the live tool; gets exact `toolWireSchema`, `tool.description`, and derived outline: ```bash bun .omp/skills/tool-prompt-optimization/scripts/probe-builtin.ts --tool <name> [--no-summary] [--show] ``` - `--show`: resolved schema, derived outline, real prompt; exits without API calls. Inspect before spending tokens. - `--no-summary`: direct summary-line-blank ablation. - `--samples` / `--model` / `--max-tokens` / `--json`: panel passthrough. Output ends with real prompt for in-place diff. - Factory-map bypasses settings allowlist: gated `irc`, `github`, … resolve. Construction availability gate (e.g. missing `gh` CLI) → manual inputs. ## Inputs **Schema:** wire schema the model sees, never hand-sketch. Arktype: ```ts import { arkToWireSchema } from "@oh-my-pi/pi-ai"; // or toolWireSchema(tool) JSON.stringify(arkToWireSchema(toolSchema), null, 2); ``` Include `required`, `additionalProperties: false`; omission makes usage appear looser than reality. **Template:** actual `.md` structure with bodies blanked — one-line summary, then each section tag containing `...`. ``` Structural code search via native ast-grep AST matching. <instruction> ... </instruction> <output> ... </output> <critical> ... </critical> ``` ## Interpret Bucket each real-prompt line: - **Prune candidate:** stable across samples **and** models; schema restatement — parameter names/types, `required`, field-description value examples, stated clamp ranges. - **Keep:** no model recovers it — defaults/direction (`gitignore` default true); routing/escalation (`NEVER` shell out to `find`/`fd` → use this tool; broad exploration → `Task` subagent); exact output shape (mtime sort, grouping, `artifact://` truncation); worked anti-patterns; type-invisible constraints (AST metavariable grammar, C++ trailing `;`). One sample: noise. Stable cross-sample/model overlap is only a candidate; history must clear it. MUST NOT delete on inferability alone. ## Caveats — before every deletion - **MUST `git blame` each cut line; read its commit/issue.** Many lines are incident scar tissue: hallucinated flag, shell-out, repo-root scan, fabricated anchor. Keep scar tissue. History distinguishes schema restatement from incident prevention. Inferability necessary, NEVER sufficient. - **Memorization ≠ inference:** public repos, including this one, may be training data. Repo-specific prediction absent from schema — exact tool names, internal URI schemes, `Task` subagent — is recitation; discount it. - **Outline leaks:** summary and section names hint. For schema-alone inference, second pass: no summary, generic section tags. Content surviving only the summary is summary-inferable, not schema-inferable. ## Verdict Predictions usually recover schema-covered parameter mechanics/generic usage, not defaults, output shape, routing, anti-patterns, domain grammar. Prune the former only after per-line `git blame`; keep the latter. Self-documenting flag tools (`find`) prune heavily; DSL/capability tools (`read`, `ast_grep`) barely. ## Tool Prompt Authoring Tool prompts are not API docs: teach when to choose a tool, input shape, and agent-owned failures. Engine internals, recovery heuristics, fallback chains, performance tuning: code. ### Surface, not machinery Agents choose from prose, not source: tell WHEN/WHY, NEVER internal HOW. - `read.md`: every covered source — file/dir/archive/sqlite/PDF/URL — prevents `cat`/`curl`/`tar`; omit chunker, binary sniffer, cache layer. - `lsp.md`: "You MUST use `lsp` whenever a language server is available — safer than text-based alternatives." Omit LSP wire protocol, server lifecycle, capability negotiation. - `ast_edit`: metavariable syntax/workflow: "Loosest existence check: `pat: 'executeBash'` with narrow paths"; omit AST engine, query compilation, tree-sitter grammar selection. - `hashline.md` (this repo): patch grammar — anchors, ops, payloads, ranges — and successful edit shapes. Hide `tryRecoverHashlineWithCache`, fuzz factor, bigram tables, `findUniqueSuffixMatch`, `untilAborted`, `formatGroupedFiles`; agent sees only "the tool resolved your typo" or "the anchor was stale, re-read". If a detail cannot change agent behavior, it does NOT belong. Each sentence MUST shift an agent decision. ### Good prompt anatomy 1. **One-line purpose:** agent-vocabulary problem; not "wraps libfoo with X", but "compact, line-anchored edit format". 2. **Input grammar/surface:** operators, parameters, selectors; concrete emitted syntax. 3. **Worked examples:** 3–8 common shapes. Example IS explanation; do not narrate twice. 4. **Agent-owned failure shapes:** input-fixable stale anchors, missing payload prefix, fabricated hash; omit silently recovered failures. 5. **Anti-patterns:** real-failure WRONG/RIGHT pairs for retry-causing mistakes, never imagined ones. 6. **`<critical>` recap:** 3–6 load-bearing lines for agents skipping body. ### Exclude - Implementation file/function names, module layout. - Recovery, retry, normalization, caching, fuzz matching. - Performance characteristics such as "this is O(n)", unless strategy-changing. - Telemetry, logging, debug flags, unsettable env vars. - Version history, deprecated parameters, "previously this worked differently". - Cross-tool plumbing such as "this calls `read` under the hood", unless coordination required.
GitHub에서 보기