← 返回 Skills 市场
kaicianflone

Coding Pronoun Prompt Resolver

作者 Kai Cianflone · GitHub ↗ · v0.10.0 · MIT-0
cross-platform ⚠ suspicious
62
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install coding-pronoun-prompt-resolver
功能描述
Detects ambiguous pronouns, vague referents, and bare imperatives in user messages and flags them for resolution using conversation context. Zero-latency det...
使用说明 (SKILL.md)

Pronoun Resolver

Arguments

If invoked with --stats: run python3 ~/.claude/skills/pronoun-resolver/bin/stats.py and display the output. Do not proceed with the rest of this skill.


You are operating with the pronoun resolver active. When the hook detects ambiguous references in a user message, you will see flags injected before the message.

Your Role

YOU are the resolver. You have the conversation context. The hook just detects — you decide.

Resolution Tiering

When you see [AMBIGUOUS:] flags, apply this framework:

GREEN — Resolve silently (90%+ confidence)

The referent is obvious from the last 1-3 messages. Just act. Don't mention the resolution.

  • "Fix it" when you just showed them a bug → fix the bug
  • "Make that work" after discussing a failing test → fix the test

YELLOW — State assumption, proceed (70-90% confidence)

You're fairly sure but there's ambiguity. State what you're assuming in one line, then act.

  • "I'm taking 'the other one' to mean auth.ts since we discussed two files. Acting on that."

RED — Ask before acting (\x3C70% confidence)

Multiple plausible referents, or no recent context to resolve against. Ask concisely.

  • "What should I make good — the UI layout we discussed or the API response format?"

BLACK — Bare imperative, no context at all

First message of a conversation with no object. Always ask.

  • "Make good" with no prior context → "What would you like me to improve?"

Flag Format

The hook outputs a preamble followed by flags:

[PRONOUN-RESOLVER: Resolve these using conversation context. HIGH confidence=act silently. MEDIUM=state assumption then act. LOW/no context=ask user first.]
[AMBIGUOUS: pronouns="it,that" | type=pronoun]
[AMBIGUOUS: vague="other,something" | type=vague_referent]
[AMBIGUOUS: implicit verb="make" | type=bare_imperative | subtype=verb_adjective]

Ledger

Resolution accuracy is tracked at ~/.claude/skills/pronoun-resolver/.claude/pronoun-ledger.json. When you resolve an ambiguous reference, log it. When the user corrects you ("no not that", "I meant X"), mark the previous resolution as corrected.

The ledger schema:

{
  "resolutions": [...],
  "resolution_count": 0,
  "adaptive_threshold": 0.8,
  "context_reliability": {}
}

Each resolution entry:

{
  "timestamp": "ISO8601",
  "pronoun": "it",
  "prompt_hash": "sha256 hex of the full prompt (no raw text stored)",
  "resolved_to": "the auth middleware",
  "tier_used": "green|yellow|red|black",
  "confidence": 0.92,
  "was_corrected": false
}

Never store raw prompt text in the ledger. Use prompt_hash for deduplication only.

Correction Detection

If the user's next message corrects your resolution:

  1. Mark the previous ledger entry as was_corrected: true
  2. Adjust your confidence calibration — if you're frequently wrong at a given tier, escalate more

Adaptation

Every 10 resolutions, check your accuracy:

  • If >90% correct at green tier → you're well calibrated
  • If \x3C75% correct → shift toward yellow/red (ask more often)
  • Track which context signals (last edited file, recent discussion topic, etc.) are most reliable

Disable

If the user creates .claude/pronoun-resolver-disabled in the project root, stop resolving.

What Gets Detected

  1. Personal pronouns (always flagged): it, them, they, its
  2. Demonstratives (flagged only when standalone, not as determiners): this, that, these, those
    • "fix this" → flagged ("this" is standalone pronoun)
    • "fix this bug" → NOT flagged ("this" is a determiner for "bug")
  3. Vague referents: other, something, someone, somewhere, anything, everything, stuff
  4. Bare imperatives: verb alone ("Fix") or verb + adjective with no object ("Make good", "Clean up", "Make better/faster")

Install

  1. Symlink or copy this directory to ~/.claude/skills/pronoun-resolver
  2. Add the hook to ~/.claude/settings.json:
"UserPromptSubmit": [
  {
    "hooks": [
      {
        "type": "command",
        "command": "bash /ABSOLUTE/PATH/TO/.claude/skills/pronoun-resolver/bin/detect-pronouns.sh"
      }
    ]
  }
]

Note: The path must be absolute. Update it if the skill is moved.

安全使用建议
Install only if you are comfortable with a hook that reads every prompt and keeps local metadata under ~/.claude/skills/pronoun-resolver/.claude/. Avoid using it for sensitive work unless you disable it with the documented project sentinel or regularly purge the ledger and analytics files; be especially cautious with ambiguous requests like delete, revert, or deploy.
能力标签
financial-authoritycan-make-purchasesrequires-sensitive-credentials
能力评估
Purpose & Capability
The pronoun-resolution purpose matches the hook and scripts, and there is no evidence of network exfiltration or destructive hidden code; however, the skill can influence follow-up file edits or commands by resolving ambiguous requests silently at high confidence.
Instruction Scope
The UserPromptSubmit hook processes every user message, and the GREEN tier explicitly says to act silently when the referent seems obvious, without a carveout for destructive or high-impact actions.
Install Mechanism
Installation is manual: clone or symlink the skill and add a Claude Code hook pointing to the local shell script. No package install, service persistence, or automatic external setup is shown.
Credentials
Runtime processing is local Bash/Python over stdin and does not use credentials or remote APIs. The marketplace metadata lists financial/credential-related capability tags that do not fit the artifact behavior, which is confusing but not evidence of implemented misuse.
Persistence & Privilege
The skill writes persistent local analytics for every processed prompt and instructs the agent to maintain a ledger that can include semantic referents in resolved_to; retention is unbounded, though the files are disclosed and deletable.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install coding-pronoun-prompt-resolver
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /coding-pronoun-prompt-resolver 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.10.0
Security hardening: replace raw prompt storage with SHA-256 hash, move ledger and analytics to skill-internal directory, add capability declarations and data retention disclosure, update .gitignore
元数据
Slug coding-pronoun-prompt-resolver
版本 0.10.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Coding Pronoun Prompt Resolver 是什么?

Detects ambiguous pronouns, vague referents, and bare imperatives in user messages and flags them for resolution using conversation context. Zero-latency det... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 62 次。

如何安装 Coding Pronoun Prompt Resolver?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install coding-pronoun-prompt-resolver」即可一键安装,无需额外配置。

Coding Pronoun Prompt Resolver 是免费的吗?

是的,Coding Pronoun Prompt Resolver 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Coding Pronoun Prompt Resolver 支持哪些平台?

Coding Pronoun Prompt Resolver 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Coding Pronoun Prompt Resolver?

由 Kai Cianflone(@kaicianflone)开发并维护,当前版本 v0.10.0。

💬 留言讨论