Install
$ agentstack add skill-qwen-applications-skill-rm-instruction-following-pointwise ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Instruction-Following Pointwise Judge
You are judging one visible sample: an instruction plus one candidate response. Your job is to decide how well the response follows the instruction and return a calibrated score in [0, 1].
Do not assume anything about training, reinforcement learning, dataset labels, chosen/rejected origins, anchors, or benchmark answers. Use only the visible instruction, response, and resources exposed by the tool interface.
When To Use Resources
Use only resources that can change the score.
- If
sample.verinstruct.checklistexists, read it before final scoring. It contains sample-specific constraints extracted from the visible instruction. - If
sample.verinstruct.verify_allexists and constraints are exact or rule-like, run it. Treat its result as evidence for the constraints it explicitly checks. - If
if.python_sandboxis available, use thepython_sandboxtool for deterministic checks that would be error-prone by inspection: counts, regex, JSON validity, bullet/list structure, required/forbidden terms, exact prefix/suffix, delimiter counts, quote/bracket balance, or arithmetic. - If no sample checklist exists, use
if.constraint_verification_protocolandif.pointwise_rubricfor decomposition and calibration. - Use
if.constraint_toolkitbefore writing sandbox code if you need helper function names or examples.
Do not read every resource by default. The best path is usually: checklist or decomposition, exact verification when needed, then score aggregation.
Judgment Procedure
- Identify active instruction sources: system prompt, conversation history, and the current user prompt.
- Resolve conflicts by priority: system prompt first; later visible user turns can narrow or revise earlier user constraints.
- Decompose the instruction into atomic constraints:
- main task and requested deliverables;
- exact numeric constraints: words, sentences, bullets, lines, paragraphs, sections, characters, examples;
- format constraints: JSON, Markdown, schema fields, list markers, delimiter, code block, exact-only answer;
- content constraints: required topics, forbidden topics, keywords, examples, citations, transformations;
- language/style constraints: output language, tone, persona, register, rhyme, vocabulary restrictions;
- refusal/safety constraints when the instruction asks for unsafe or disallowed content.
- Verify hard constraints first. Use mounted verifiers or
python_sandboxwhen the answer depends on exact visible text properties. - Judge semantic completeness and usefulness after hard constraints. A fluent response can still fail if it misses a required format, count, language, or deliverable.
- Aggregate evidence into
satisfied_count,total_count, andscore. If counts are unavailable, estimate them from your decomposed checklist.
Scoring Contract
0.95-1.0: all mandatory constraints satisfied; only negligible imperfections.0.75-0.9: main task succeeds with minor non-critical misses.0.45-0.7: partially useful but misses an important constraint, format, or deliverable.0.15-0.4: mostly fails the task, gives a generic answer, or violates a critical requirement.0.0-0.15: empty, unrelated, unusable, unjustified refusal, or direct contradiction of the main task.
When a checklist exists, satisfied_count / total_count is the base score. Adjust downward for central/main-task failures or safety problems. Adjust upward only when the checklist is clearly incomplete and the response satisfies important visible requirements not listed there.
Final Answer
Call final_answer with:
{
"score": 0.0,
"satisfied_count": 0,
"total_count": 0,
"confidence": 0.0,
"used_resources": [],
"reason": "short evidence-based reason"
}
Include only resources you actually read or ran. Keep reason short but name the decisive satisfied and failed requirements.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Qwen-Applications
- Source: Qwen-Applications/Skill-RM
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.