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arxiv: 2605.18693 · v1 · pith:GESJVO7Rnew · submitted 2026-05-18 · 💻 cs.AI

SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents

Yifan Zhou , Zhentao Zhang , Ziming Cheng , Shuo Zhang , Qizhen Lan , Zhangquan Chen , Zhi Yang , QianyuXu
show 3 more authors
Ronghao Chen Huacan Wang Sen Hu
This is my paper

Pith reviewed 2026-05-20 09:58 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentsskill generationbenchmarktask-conditioned generationtask-agnostic generationrepository-groundeddocument-groundedexecutable skills
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The pith

SkillGenBench creates a controlled testbed to evaluate how LLM pipelines generate reusable executable skills from raw sources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to treat skill generation itself as a separable research target rather than an implicit byproduct of task solving. Current evaluations either assume skills are already supplied or measure only end-task success from raw context, leaving the generation step unexamined. SkillGenBench fixes this by routing raw corpora through a generator that must emit standardized skill artifacts, which are then run under fixed harnesses and scored with deterministic execution checks. The design splits generation into task-conditioned versus task-agnostic regimes and into repository-grounded versus document-grounded sources. Experiments on multiple methods and backbones reveal large performance spreads, especially in task-agnostic distillation, plus qualitatively different error patterns when skills must be extracted from code versus long text.

Core claim

SkillGenBench evaluates skill generation pipelines under a single protocol in which a generator consumes raw corpora and emits standardized skill artifacts that are executed inside pinned environments and scored by deterministic execution-based checks. The benchmark spans task-conditioned generation, where a skill is produced after a task is revealed, and task-agnostic generation, where a reusable library must be distilled beforehand; it also spans repository-grounded instances whose procedures lie across code and scripts and document-grounded instances whose procedures must be recovered from long-form text. Tests across generators show substantial performance differences, particular trouble

What carries the argument

SkillGenBench protocol, which converts raw repositories or documents into executable skill artifacts and subjects them to unified, deterministic execution harnesses across task-conditioned versus task-agnostic and repository versus document regimes.

If this is right

  • Skill-generation methods display large differences in success when forced to produce correct, executable artifacts.
  • Creating reusable skills before any downstream task is known remains especially difficult.
  • Repository-grounded and document-grounded sources trigger qualitatively different classes of generation errors.
  • The same evaluation harness can be reused to compare future generators in a reproducible way.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Agent architectures could shift from static skill libraries toward on-demand generation modules that are themselves benchmarked.
  • The same regime-source structure might expose analogous bottlenecks when applied to non-software domains such as robotic manipulation or web navigation.
  • Developers could adopt the benchmark as a direct yardstick when comparing new language-model backbones on skill-creation ability.

Load-bearing premise

The chosen split into task-conditioned versus task-agnostic regimes together with repository versus document sources and deterministic execution checks is enough to isolate and measure the main difficulties of producing correct, reusable, executable skills.

What would settle it

If every tested generator produces nearly identical success rates across all four regime-source combinations and exhibits indistinguishable failure patterns, the benchmark would fail to isolate distinct skill-generation challenges.

Figures

Figures reproduced from arXiv: 2605.18693 by Huacan Wang, QianyuXu, Qizhen Lan, Ronghao Chen, Sen Hu, Shuo Zhang, Yifan Zhou, Zhangquan Chen, Zhentao Zhang, Zhi Yang, Ziming Cheng.

Figure 1
Figure 1. Figure 1: Overview of SkillGenBench. Skill-generation pipelines transform repository- and document-grounded [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SkillGenBench construction pipeline. Repositories and long documents are first abstracted into a [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Source and domain composition of SkillGen [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Repository-grounded task-specific versus [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Grouped static diagnostics over generated [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Full method–backbone pass@3 matrix across skill-generation methods and generation backbones. The [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Sensitivity of benchmark pass rate to the generation token limit. Each panel fixes the generation backbone [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluate the efficacy of given skills or the ability of agents to solve downstream tasks from raw context, but they do not isolate skill generation itself as the object of study. We introduce SkillGenBench, a benchmark for evaluating skill generation pipelines under a unified and controlled protocol. In SkillGenBench, a generator receives raw corpora and produces standardized skill artifacts, which are then executed under fixed harnesses and assessed with unified evaluation procedures. The benchmark covers two generation regimes: task-conditioned generation, where a task-specific skill is synthesized after the task is revealed, and task-agnostic generation, where a reusable skill library must be distilled before downstream tasks are known. It also spans two complementary procedural sources: repository-grounded instances, where procedures are distributed across code, configuration, and scripts, and document-grounded instances, where procedures and constraints must be distilled from long-form text. We provide standardized task specifications, pinned environments, and evaluation protocols centered on deterministic execution-based checks, supplemented by auxiliary signals for diagnosis. Experiments across a range of skill-generation methods and backbones show substantial performance variation, highlight the difficulty of reusable skill distillation, and reveal distinct failure modes in skill generation from software repositories versus long-form documents. SkillGenBench establishes a reproducible testbed for studying skill generation as an independent research problem in agent systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces SkillGenBench, a benchmark for evaluating skill generation pipelines for LLM agents. It covers two regimes (task-conditioned generation after task revelation vs. task-agnostic distillation of reusable libraries before tasks are known) and two sources (repository-grounded procedures from code/config/scripts vs. document-grounded from long-form text). Generators produce standardized skill artifacts evaluated under fixed harnesses with deterministic execution-based checks plus auxiliary diagnostics. Experiments across methods and backbones report performance variation, highlight reusable distillation difficulty, and identify distinct failure modes by source. The central claim is that SkillGenBench provides a reproducible testbed isolating skill generation as an independent research problem.

Significance. If the protocols truly isolate reusable skill generation without corpus-task leakage, the benchmark fills a clear gap left by prior work focused on skill usage or raw-context task solving. The unified, controlled setup with pinned environments and execution checks could serve as a standard for systematic study of generation pipelines, with the reported variation and failure modes offering initial diagnostic value.

major comments (1)
  1. [Section 3] Benchmark construction (Section 3): The manuscript does not explicitly confirm that downstream evaluation tasks and constraints are fully held-out from the raw corpora (repositories or documents) supplied to generators. For the task-agnostic regime to measure genuine reusable skill distillation rather than retrieval or memorization, this disjointness is required; any overlap would mean observed performance variation reflects leakage instead of independent generation, directly weakening the claim that the benchmark isolates skill generation as an independent problem.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'supplemented by auxiliary signals for diagnosis' is mentioned but not illustrated; adding one sentence or a forward reference to the evaluation protocol would aid readability.
  2. [Experiments] Experiments section: While distinct failure modes are highlighted, a summary table or figure breaking them down by regime and source (e.g., syntax errors vs. reusability failures) would make the results more concrete and easier to compare across backbones.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing SkillGenBench's potential to isolate skill generation as a distinct research problem. We address the single major comment below.

read point-by-point responses

  1. Referee: [Section 3] Benchmark construction (Section 3): The manuscript does not explicitly confirm that downstream evaluation tasks and constraints are fully held-out from the raw corpora (repositories or documents) supplied to generators. For the task-agnostic regime to measure genuine reusable skill distillation rather than retrieval or memorization, this disjointness is required; any overlap would mean observed performance variation reflects leakage instead of independent generation, directly weakening the claim that the benchmark isolates skill generation as an independent problem.

    Authors: We agree that explicit confirmation of disjointness is necessary to substantiate that the task-agnostic regime measures genuine distillation rather than retrieval. The benchmark construction in Section 3 derives evaluation tasks from separate specifications and pinned environments that are not supplied as part of the raw corpora to the generators; however, the manuscript does not state this separation explicitly. We will revise Section 3 to add a dedicated paragraph describing the hold-out procedure, including how task specifications and constraints were selected or partitioned to ensure they are absent from the repository and document sources provided to generators. This revision will directly address the concern and reinforce the benchmark's claim to isolate independent skill generation. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark definition is self-contained

full rationale

This paper introduces SkillGenBench as a new testbed for evaluating skill generation pipelines, with no mathematical derivations, predictions, or first-principles results present in the abstract or described structure. The central contribution is the explicit definition of regimes (task-conditioned vs task-agnostic), sources (repository-grounded vs document-grounded), and deterministic execution-based evaluation protocols. No load-bearing step reduces by construction to fitted inputs, self-citations, or prior ansatzes; the work is a benchmark proposal whose validity rests on the clarity of its own specifications rather than any derived equivalence to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark rests on domain assumptions about what constitutes a reusable and executable skill and that execution-based checks are adequate proxies for real-world utility; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Standardized skill artifacts produced by generators can be executed under fixed harnesses to yield reliable assessments of correctness and reusability.
    This premise is required for the unified evaluation procedures to be meaningful.

pith-pipeline@v0.9.0 · 5838 in / 1278 out tokens · 30656 ms · 2026-05-20T09:58:29.785234+00:00 · methodology

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Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages

  1. [1]

    Organizing, orchestrating, and benchmarking agent skills at ecosystem scale

    Swe-bench: Can language models resolve real-world github issues? InThe twelfth international conference on learning representations. Yusuf Karaaslan. 2026. Skill seekers. https://github.com/yusufkaraaslan/Skill_Seekers. Repository for converting documentation websites, GitHub repositories, and PDFs into Claude-compatible skills. Patrick Lewis, Ethan Perez...

    work page arXiv 2026

  2. [2]

    The **question text** describes the function interface using ABSTRACT references to document rules

    work page

  3. [3]

    All **document-specific constants, thresholds, formulas, parameter values** are hardcoded INSIDE the`solve()`function body -- NOT in the question text, NOT in the test case inputs

    work page

  4. [4]

    Test case inputs contain varying scenarios; the function applies document-internal knowledge to compute outputs

    work page

  5. [5]

    ### Question Quality

    The task must be IMPOSSIBLE to implement correctly without reading the document. ### Question Quality

    work page

  6. [6]

    Questions must require **multi-step reasoning** using multiple document rules

    work page

  7. [7]

    Each question integrates information from **multiple parts** of the document

    work page

  8. [8]

    The function should test **consequences and interactions** of document rules

    work page

  9. [9]

    ### Test Case Requirements

    Input schema should be rich enough to support {test_cases_per_task} diverse test scenarios. ### Test Case Requirements

    work page

  10. [10]

    Generate exactly {test_cases_per_task} test cases with diverse scenarios

    work page

  11. [11]

    Cover: normal cases, edge cases, boundary conditions, rule interactions. 19

    work page

  12. [12]

    input": {{...}},

    Each test case:`{{"input": {{...}}, "expected_output": {{...}}}}`. 12.`solution_code`must define a function named`solve`taking one dict argument

    work page

  13. [13]

    14.`solve`must be self-contained -- no external variable references

    All document-specific values hardcoded inside`solve`. 14.`solve`must be self-contained -- no external variable references

    work page

  14. [14]

    ### Input Constant Consistency

    The`solve`function body should include comments citing which document section each constant comes from. ### Input Constant Consistency

    work page

  15. [15]

    Setting Manager

    All string constants in test case inputs (e.g., entity names, type names, category labels) MUST exactly match the strings used in`solve()`comparisons. If`solve()`checks`name == " Setting Manager"`, the test input must use`"Setting Manager"`, NOT`"SettingManager"`or`" setting_manager"`. Double-check: every string in input that will be compared inside`solve...

    work page

  16. [16]

    No deeply nested reports, audits, or multi-section compilations

    The function output MUST be a FLAT or SHALLOW dict (max 2 nesting levels). No deeply nested reports, audits, or multi-section compilations

    work page

  17. [17]

    Count every terminal value in the nested structure

    Each test case expected_output MUST have at most 15 leaf values (strings, numbers, booleans, nulls). Count every terminal value in the nested structure. If your design exceeds 15, simplify the output schema

    work page

  18. [18]

    compile a full report

    The task MUST be a SINGLE DECISION or SINGLE COMPUTATION -- not "compile a full report". Good : "decide which walls to reset", "classify the input", "compute a score". Bad: "generate a comprehensive audit report", "compile a multi-section learning pack"

    work page

  19. [19]

    PASS", "FAIL

    String values in expected_output SHOULD be short enums or codes (e.g., "PASS", "FAIL", "ALERT "), NOT long narrative sentences. If a reason/message field is needed, keep it under 50 characters

    work page

  20. [20]

    task_id":

    The question text MUST be COMPLETE -- do not truncate input schemas or rule descriptions. If the schema is too large, simplify the function interface to fewer input fields. Every field mentioned in the INPUT section must have its type and description fully specified. ## Output Format Output exactly TWO JSON blocks (```json fenced): ### Block 1: Task ```js...

    work page

  21. [21]

    **complexity_score**: How many reasoning steps are required? Simple lookup = 0.2, multi-step computation = 0.8+

    work page

  22. [22]

    **utility_score**: How practical is this task? Would someone actually need to solve it? Exam- style = 0.2, real tool = 0.8+

    work page

  23. [23]

    **contamination_risk**: Based on pretrain_score, how likely is pretrain contamination? >20% pretrain = high risk

    work page

  24. [24]

    **doc_dependency**: How much does the answer depend on document-specific info? Generic knowledge suffices = 0.2, fully doc-dependent = 1.0

    work page

  25. [25]

    **skill_benefit**: How much would a good skill document help? No help = 0.2, critical = 1.0

    work page

  26. [26]

    10-40% = ideal range

    **doc_only_sufficient**: Based on doc_only_score, can the task be solved with the document? <10% = critically too hard (needs simplification). 10-40% = ideal range. >50% = may be too easy. 21

    work page

  27. [27]

    **doc_adds_value**: Does the document provide advantage over pretrain alone? doc_only_score - pretrain_score < 10% = document not helping

    work page

  28. [28]

    pass" or

    **output_testability**: Are the expected outputs structural and objectively verifiable? - Outputs dominated by long freeform strings (messages, recommendations, dialog >30 chars) test string reproduction rather than document knowledge -> score 0.0-0.2 - Outputs that are numbers, booleans, short enums, computed values -> score 0.8+ - Outputs with emoji cha...

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  29. [29]

    Import any necessary libraries at the top level

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  30. [31]

    Hardcode all necessary constants inside the function

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  31. [32]

    No markdown fences, no explanations

    Return the result as specified in the task Return ONLY executable Python code. No markdown fences, no explanations. With-corpus triviality check.The same solver is rerun with the source document attached; doc_only−pretrain quantifies how much value the document actually contributes. Prompt 7: With-Corpus Triviality Check You are given the following refere...

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  32. [33]

    Import any necessary libraries at the top level 22

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  33. [34]

    Define the`solve(input_data)`function

    work page

  34. [35]

    Hardcode all necessary constants (from the document) inside the function

    work page

  35. [36]

    No markdown fences, no explanations

    Return the result as specified in the task Return ONLY executable Python code. No markdown fences, no explanations. Targeted refinement.Rather than discarding rejected tasks, the verifier’s failure reasons are forwarded to a refinement prompt that surgically fixes the identified issue (contamination, over-difficulty, string-matching output, low diversity,...

    work page

  36. [37]

    The`solve`function should require document-specific constants/rules that cannot be guessed

    **If pretrain contaminated**: Make the question more document-specific. The`solve`function should require document-specific constants/rules that cannot be guessed. Reference obscure details, combine multiple rules, or require document-specific parameter values hardcoded in the function body

    work page

  37. [38]

    **If too simple**: Add more computation steps, require cross-referencing multiple sections, or ask about consequences rather than facts

    work page

  38. [39]

    **If low utility**: Reframe as a practical tool or system that someone would actually build

    work page

  39. [40]

    **If solution code broken**: Fix the`solve`function while keeping the question intent

    work page

  40. [41]

    **If too hard (doc_only too low)**: The task is too complex for the LLM to solve even with the full document. Simplify by: reducing the number of rules/steps required, making the expected output format simpler (fewer nested keys), breaking the task into a smaller more focused scope, using more standard output types (single value, simple dict) instead of c...

    work page

  41. [42]

    **If doc_not_helping (doc_only =~ pretrain)**: The document doesn't provide meaningful advantage. Make the task more document-specific by: requiring document-specific constants that cannot be guessed, referencing obscure details or unique terminology from the document, combining rules from multiple non-obvious sections of the document

    work page

  42. [43]

    reply":

    **If string_matching_dominant**: The task's expected outputs rely on exact matching of long freeform strings (>30 chars). This tests string reproduction, NOT document knowledge. Restructure the output to test STRUCTURAL decisions: - Messages/replies -> action codes + rule IDs:`{{"reply": "Hold on!..."}}`->`{{"action": " SAFETY_BLOCK", "rule_triggered": "R...

    work page

  43. [44]

    responses

    **If output_overly_complex**: The task's expected output is too deeply nested (>20 leaf values per test case), causing format-sensitive failures even when the logic is correct. Simplify the output structure: 23 - Flatten nested dicts: instead of`{{"responses": [{{"task_id": "...", "status": "...", " new_totals": {{"total_spent": ..., "by_category": {{...}...

    work page

  44. [45]

    Replace emoji -containing messages with structured action codes or boolean flags

    **If emoji_in_output**: Remove ALL emoji characters from expected output values. Replace emoji -containing messages with structured action codes or boolean flags. Emoji make exact matching impossible for LLMs and do not test document knowledge

    work page

  45. [46]

    **If low_output_diversity**: The task produces nearly identical output for most test cases, meaning it lacks discriminability -- the LLM could score high just by returning a fixed template. Fix by: - Adding more varied input scenarios that trigger DIFFERENT code paths and produce DIFFERENT outputs - Ensuring at least 50% of test cases have structurally di...

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