跳到正文
cccskills
免费在 GitHub 公开

tool-prompt-optimization

Review and shorten tool instructions for AI agents by testing overlap with parameter schemas, the definitions of accepted inputs. It also guides writing clear usage rules while preserving warnings backed by past failures.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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.

查看安装方法

适用场景

  • Audit tool instructions for repeated parameter explanations
  • Test schema overlap before removing prompt lines
  • Write tool selection rules and practical examples
  • Separate agent instructions from implementation details

包含的文件(3)

  • SKILL.md7.0 KB
  • scripts/probe-builtin.ts5.7 KB
  • scripts/probe.ts7.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

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:

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:

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Rewrite lengthy prompts, tool descriptions, and agent instructions into compact text while preserving requirements, exceptions, and exact values. The process records removed content and compares token counts, the units models use to measure text length.

中文简介正在准备中。当前显示英语简介。

  • Shortening system prompts
  • Compressing tool documentation and skill bodies
  • Reducing text length for model input
can1357/oh-my-pi3.6万2026年10月22日 更新

Write and edit system prompts, tool instructions, and agent definitions with clear structure and explicit rule strength. It guides tag use, placement of critical constraints, concise wording, and practical examples that show how tools should be used.

中文简介正在准备中。当前显示英语简介。

  • Drafting system prompts for agents
  • Clarifying required and optional instructions
  • Writing tool explanations with runnable examples
can1357/oh-my-pi3.6万2026年10月22日 更新

查看 can1357 的全部技能

报告此技能的问题