Meta-Harness
Automated search for task-specific harnesses around a fixed base model.
The README clearly attributes the paper, authors, independent community projects, and separate artifact repository, while the LICENSE supplies explicit copyright and MIT terms, so source_attribution earns full marks. It only broadly says the framework controls what is stored, retrieved, and shown and that proposer interactions should be logged, giving data_flow_transparency thin support without destinations, retention, or log-content details. Commands and tests demonstrate evaluation execution and file writes, so external_effects is minimally visible, but no effects inventory or boundary is supplied. The material documents no least-privilege design, sensitive-data controls, dependency-security practice, consequential-action confirmation, or rollback mechanism, so those criteria score zero. Unknown publisher identity is treated as unknown and is not used to infer risk or safety.
The README's account of the framework, two experiments, separate artifact, and Claude Code proposer assumption is internally coherent, and the shown verifier is consistent with task-output evaluation; self_consistency is therefore adequate. Setup shows uv sync and run commands but no displayed dependency locks, supported versions, or fallback strategy, so dependency_availability is thin. The verifier silently converts a missing file or malformed JSON into a zero reward, and the README merely invites generic problem reports; no actionable failure messages are evidenced.
Research use, text classification, Terminal-Bench 2, and onboarding for new domains are explicit, giving adequate audience-and-scenario coverage. The source defines the harness around a fixed base model, distinguishes framework, examples, and artifact, and explains the Claude Code default plus the wrapper needed for alternatives, so capability boundaries are reasonably clear. Triggering is largely an instruction to have a coding assistant read ONBOARDING.md and converse, without precise entry, selection, or stopping rules, making trigger_precision thin. Environment evidence is limited to uv, Python, bash, a Claude Code assumption, and absolute container paths in tests; platform, version, and resource compatibility are not documented.
The README has a strong structure covering contents, quick starts, onboarding, release status, and citation, so information_architecture earns full marks. Basic install commands and pointers to subdirectory guides are present, but the supplied evidence omits those detailed guides, prerequisites, and troubleshooting, preventing full install_notes credit. Names and entry points appear consistent, though no stability or compatibility policy exists. Two command examples and a new-domain flow are useful, but there is no FAQ. Known limitations earn full marks because the README explicitly says this is cleaned paper code, was not tested beyond confirming it runs, and currently assumes Claude Code. The complete MIT text earns full license credit. A single unversioned release note is not a changelog. Authors are identifiable and issue reports are invited, but maintainer roles, a support route, update policy, and maintenance commitment are absent, so maintenance responsibility is thin.
The quick starts offer runnable experiment paths, onboarding is expected to produce domain_spec.md, and the verifier emits a machine-readable reward, supporting ordinary output usability; complete output schemas and explanatory diagnostics are absent. Automated search over task-specific harnesses offers plausible marginal value over manual harness design and is illustrated in two domains, but no comparative results are included in the supplied files. The README says subdirectory documentation covers runtime, yet that evidence is not supplied, and there is no treatment of API expense, compute requirements, search budget, or benefit tradeoffs, so cost_benefit is only thinly addressed.
Core claims are traceable to a named paper, concrete example directories, a separate artifact, citation metadata, and an exact expected output in the verifier, yielding adequate claim_traceability without line-by-line mapping from results to code. Corroboration is limited because the supplied set contains only the README, license, and a very small verifier fragment rather than the paper text, full implementation, dependency files, or broad tests. Fact-inference separation is reasonably careful: the README distinguishes paper code, the cleaned release, independent community work, and prospective listings, and explicitly limits its testing claim. Broader claims about framework value still lack direct substantiation in the provided material.
- This is a static review limited to the supplied files; no code, dependency installation, or experiment was executed.
- Before using real or sensitive data, verify exactly what proposer-interaction logs contain, where they are sent, how long they persist, and how they can be deleted.
- Review the omitted dependency manifests and lockfiles before running, and confirm the permissions, costs, and network behavior of Claude Code and any other external services.
- The shown verifier silently records missing or invalid JSON as a zero reward, which can conceal environment or implementation failures.
- The repository says only that the cleaned code was confirmed to run; this does not establish paper-result reproduction, correctness, stability, or production suitability.
What does this agent do, and when should you use it?
Meta-Harness is a research framework for automatically searching model harnesses: the surrounding code that controls what a fixed base model stores, retrieves, and sees while working. The repository includes a reusable framework, an onboarding flow for new domains, and two reference experiments from the paper. One experiment searches memory systems for text classification, while the other evolves scaffolds for Terminal-Bench 2.0. Users run searches or evaluations locally through Python and Bash entry points in the experiment directories, and the new-domain workflow produces a domain_spec.md file. The optimized Terminal-Bench 2 harness reported in the paper is distributed separately in an artifact repository.
For text classification, the user enters reference_examples/text_classification, installs the environment with uv, and runs meta_harness.py to search memory-system designs. For Terminal-Bench 2.0, scripts/run_eval.sh accepts an AgentHarness class, evaluation scope, run parameters, and a task identifier such as extract-elf to execute a smoke evaluation. For a new domain, a coding assistant is directed to ONBOARDING.md and uses a conversation to turn domain requirements into domain_spec.md. The shipped experiments use Claude Code as the proposer agent and route it through claude_wrapper.py files that log proposer interactions; another proposer requires adapting those wrappers.
- A research team reproducing the paper's memory-system search experiment for text classification.
- A Terminal-Bench 2.0 researcher running a one-task smoke evaluation of the baseline AgentHarness.
- A developer with a fixed base model exploring storage, retrieval, and context-presentation policies without training the model.
- A team onboarding a new task domain and using ONBOARDING.md to develop a concrete domain_spec.md.
- An agent engineer studying scaffold evolution who wants two executable reference experiments as a starting point.
What are this agent's strengths and limitations?
- It targets the storage, retrieval, and information-presentation code around a fixed model, so model training is not a prerequisite.
- It ships two concrete reference experiments covering memory-system search and Terminal-Bench 2.0 scaffold evolution.
- Its ONBOARDING.md workflow turns discussion of a new domain into a concrete domain_spec.md.
- Separate claude_wrapper.py integration points make the required location for proposer replacement explicit.
- The release note says the cleaned-up paper code has only been checked to run and has not received broader testing.
- The shipped examples assume Claude Code as the proposer; using another proposer requires custom wrapper work.
- The supplied documentation does not specify Python versions, supported operating systems, authentication, cost, or full runtime expectations.
- The paper's optimized Terminal-Bench 2 harness is kept in a separate artifact repository rather than this package.
How do you install or deploy this agent?
The documented workflow requires a local shell, Python, uv, and the Claude Code proposer environment assumed by the shipped examples. For text classification:
cd reference_examples/text_classification
uv syncFor Terminal-Bench 2.0:
cd reference_examples/terminal_bench_2
uv syncThe supplied material does not specify a supported Python version, operating systems, Claude Code installation or authentication steps, or any other credentials. Those details must be confirmed from the experiment configuration before adoption.
How do you use this agent?
Run one text-classification search iteration:
cd reference_examples/text_classification
uv sync
uv run python meta_harness.py --iterations 1Run the Terminal-Bench 2.0 extract-elf smoke task:
cd reference_examples/terminal_bench_2
uv sync
uv run bash scripts/run_eval.sh agents.baseline_kira:AgentHarness full 1 1 -i extract-elfFor a new domain, give ONBOARDING.md to a coding assistant and work through the conversation that produces domain_spec.md. To replace Claude Code as the proposer, adapt reference_examples/text_classification/claude_wrapper.py or reference_examples/terminal_bench_2/claude_wrapper.py while preserving clean proposer-interaction logging.
How does this agent compare with similar options?
Unlike an approach that trains or fine-tunes the base model, Meta-Harness keeps that model fixed and searches the surrounding storage, retrieval, and presentation logic. The repository does not provide a direct benchmark comparison with another named framework.