What makes ia-refine-prompt notable?

1.7K downloads. Trust evidence available

How should ia-refine-prompt be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

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agentCLAWHUBUnverified

ia-refine-prompt

Transforms vague prompts into precise, structured AI instructions. Use when asked to refine, improve, or sharpen a prompt, do prompt engineering, write a system prompt, or make AI instructions more effective. Skill: ia-refine-prompt Owner: iliaal Summary: Transforms vague prompts into precise, structured AI instructions. Use when asked to refine, improve, or sharpen a prompt, do prompt engineering, write a system prompt, or make AI instructions more effective. Tags: latest:4.5.2 Version history: v4.5.2 | 2026-09-08T01:46:57.055Z | user v4.5.2 v4.5.0 | 2026-08-29T22:22:30.777Z | user v4.5.0 v4.3.3 | 2026-08-04T01:17:56.595

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.7k

Updated

Oct 10, 2026

Version

4.5.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.7K downloadsadoption · observed Oct 10, 2026
Latest release: 4.5.2release · observed Sep 8, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17bcar8wq0xhegs0ny6f57ypd8484bw:compound-eng-refine-prompt

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-iliaal-compound-eng-refine-prompt/snapshot"

Documentation

CLAWHUB

85,703 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: ia-refine-prompt
class: meta
description: >-
Transforms vague prompts into precise, structured AI instructions. Use when
asked to refine, improve, or sharpen a prompt, do prompt engineering,
write a system prompt, or make AI instructions more effective.
---

# Refining Prompts

## Process

1. **Assess** -- Identify what the prompt is missing:

| Element | Check |
|---------|-------|
| Task | Is the core action explicit and unambiguous? |
| Constraints | Are length, format, tone, and scope defined? |
| Output format | Does it specify the expected structure? |
| Context | Does the model have enough background to act? Check: audience, input format, success criteria, scope boundaries, technical constraints |
| Examples | Would a demonstration clarify the expected output? |
| Edge cases | Are failure modes and boundary conditions addressed? |
| Reader | Will a model parse this with no human available to disambiguate? If yes, apply Machine-Parsed Text below. |

2. **Rewrite** -- Transform into specification language: precise, imperative, no filler. Treat the prompt as a spec, not conversation.

3. **Validate** -- Check the rewrite against the assessment table. Every gap identified in step 1 must be addressed.

## Rules

- **Length**: 0.75x–1.5x the original. Conciseness is a feature -- add only what's missing, cut what's vague.
- **A line must change behavior.** "Cut what's vague" and "cut what the model already does" are different filters, and the second removes far more -- every line reads as non-vague once it is imperative. If the model would act that way by default, delete the whole sentence rather than trimming words from it. The recurring offender is encouragement it already follows: "be careful", "be thorough", "think it through", "make sure to".
- **Name the concept, don't explain it.** Use terms the model knows (idempotent, invariant, race condition, TOCTOU, YAGNI) instead of spelling them out. Spell out only terms the project invented, once, in one place.
- **State a rule once.** If the same rule appears in two sections, cut one and point to the other.
- **Pair every prohibition with the positive target.** Steering by ban drags the forbidden behavior into context and makes it more available, not less -- the negation is a weak modifier riding on a strongly activated concept. Prompt the target instead ("write one-line comments" rather than "don't write long comments") so the banned behavior is never named. A bare prohibition earns its place only as a hard guardrail whose whole content is the refusal, with no behavior to substitute. Everywhere else the check is mechanical: every `never` and `don't` line states its replacement behavior.
- **Never invent** -- only use information present in the original prompt or conversation context. If critical info is missing, ask instead of assuming.
- **Instruction hierarchy** -- order sections by priority: task → constraints → examples → input data → output format. Place the most important in

_meta.json

{
  "ownerId": "kn715jrbbh71q9zncr0bqdkr8n848q1a",
  "slug": "compound-eng-refine-prompt",
  "version": "4.5.2",
  "publishedAt": 1788832017055
}

skill-card.md

## Description:

Transforms vague prompts into precise, structured AI instructions for prompt refinement, prompt engineering, system prompts, and effective AI instructions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[iliaal](https://clawhub.ai/user/iliaal)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, prompt authors, and agent builders use this skill to turn vague or underspecified prompts into precise Markdown instructions for AI systems. It is especially useful for prompt engineering, system prompts, and machine-parsed skill or agent instructions.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: A refined prompt may include secrets or sensitive material if the original prompt contains them and the user chooses to save the result.

Mitigation: Review the refined prompt before saving and approve writing to .ai/PROMPT.md only when a persistent local record is appropriate.

Risk: Prompt refinements can introduce misleading requirements if the original intent is unclear.

Mitigation: Clarify unclear intent first and validate that the rewrite uses only information from the original prompt or conversation context.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/iliaal/skills/compound-eng-refine-prompt)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown refined prompt text]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May ask before appending the refined prompt to .ai/PROMPT.md; persistent writes require explicit confirmation.]

## Skill Version(s):

4.5.2 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

SPEC.md

# ia-refine-prompt Specification

## Intent

`ia-refine-prompt` is a `meta`-class skill (patterns about prompts, agents, or skills themselves). Transforms vague prompts into precise, structured AI instructions. Use when asked to refine, improve, or sharpen a prompt, do prompt engineering, write a system prompt, or make AI instructions more effective.

## Scope

In scope:
- Behaviors described in `SKILL.md` and routed via the should_trigger phrasings in `distillery/tests/fixtures/triggers/ia-refine-prompt.jsonl`.
- Updates to runtime behavior, structure, trigger precision, references, and validation.

Out of scope:
- Acting as the runtime instructions themselves (those live in `SKILL.md`).
- Trigger phrasings already covered by adjacent `ia-*` skills (`validate-plugin` flags >70% description overlap as DUPLICATE_TRIGGER).
- <!-- to fill in: domain-specific exclusions when the skill drifts -->

## Trigger Context

- Class: `meta`
- Hook regex: `plugins/whetstone/hooks/skill-patterns.sh` -> `SKILL_PATTERNS[ia-refine-prompt]`
- Common requests (from fixture should_trigger):
  - "refine this prompt to get better code generation results"
  - "optimize the prompt for the summarization task"
  - "rewrite this prompt to be precise and structured"
- Should not trigger for (from fixture should_not_trigger):
  - "set up a Kubernetes deployment for the API"
  - "write a migration to add soft deletes"
  - "write the documentation for this API"

## Source And Evidence Model

Authoritative sources:

- `SKILL.md` -- runtime instructions and reference routing.
- `references/*.md` -- bundled supplementary content (0 file(s)).
- `distillery/tests/fixtures/triggers/ia-refine-prompt.jsonl` -- positive and negative trigger phrasings under regression test.
- `plugins/whetstone/hooks/skill-patterns.sh` -- regex pattern that fires this skill.
- `distillery/.eval-data/ia-refine-prompt/` -- harvested session examples (when present).

Data that must not be stored in this skill or its references:

- Secrets, credentials, tokens.
- Machine-specific filesystem paths (`/home/...`, `/Users/...`, `~/ai/...`). The validator (`MACHINE_PATH_LEAK`) flags these as HIGH.
- Private URLs, customer data, or unredacted personal information.

### Coverage matrix

| Dimension | Status | Evidence |
|---|---|---|
| Trigger fixtures | complete | distillery/tests/fixtures/triggers/ia-refine-prompt.jsonl (>=5 should_trigger, >=5 should_not_trigger) |
| Hook regex pattern | complete | plugins/whetstone/hooks/skill-patterns.sh (`SKILL_PATTERNS[ia-refine-prompt]`) |
| Reference architecture | n/a | no references; SKILL.md is self-contained |
| Real-usage signal | <!-- populated by harvest-sessions when sessions exist --> | distillery/.eval-data/ia-refine-prompt/ (created by harvest-sessions) |

## Evaluation

Lightweight (run on every change):

```bash
python3 distillery/scripts/distiller.py validate-plugin --component ia-refine-prompt
python3 distillery/scripts/distiller.py test-triggers --skill ia-

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