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kaicianflone

Coding Pronoun Prompt Resolver

by Kai Cianflone · GitHub ↗ · v0.10.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install coding-pronoun-prompt-resolver
Description
Detects ambiguous pronouns, vague referents, and bare imperatives in user messages and flags them for resolution using conversation context. Zero-latency det...
README (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.

Usage Guidance
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.
Capability Tags
financial-authoritycan-make-purchasesrequires-sensitive-credentials
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install coding-pronoun-prompt-resolver
  3. After installation, invoke the skill by name or use /coding-pronoun-prompt-resolver
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Slug coding-pronoun-prompt-resolver
Version 0.10.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Coding Pronoun Prompt Resolver?

Detects ambiguous pronouns, vague referents, and bare imperatives in user messages and flags them for resolution using conversation context. Zero-latency det... It is an AI Agent Skill for Claude Code / OpenClaw, with 62 downloads so far.

How do I install Coding Pronoun Prompt Resolver?

Run "/install coding-pronoun-prompt-resolver" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Coding Pronoun Prompt Resolver free?

Yes, Coding Pronoun Prompt Resolver is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Coding Pronoun Prompt Resolver support?

Coding Pronoun Prompt Resolver is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Coding Pronoun Prompt Resolver?

It is built and maintained by Kai Cianflone (@kaicianflone); the current version is v0.10.0.

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