Eval Author Audit

Ethos Pre-flight

Before drafting audit items, locate the Ethos file that will serve as the source of truth. If the user provides --ethos <path>, validate and use that path as <ethos_path> before applying repository discovery. Otherwise, prefer ETHOS.md at the repository root when it exists. If it is missing, check for one existing Ethos generated by the NeMo Platform Ethos flow at agents/<name>-ethos/ETHOS.md and use that path as <ethos_path>.

If no Ethos file exists, stop the audit flow and ground the user first: Eval Author needs a source of truth for how the agent is supposed to behave before it can decide what audit.md should cover. Code shows what the agent does today; Ethos records intended behavior, mission, constraints, success and failure criteria, and what may change. Link the user to ETHOS.md for what Ethos is and how to create one.

If the current assistant environment exposes both required Ethos creation skills, nemo-explore and nemo-ethos, share that Eval Author can generate the Ethos with them now. Ask the user whether they want you to:

1. automatically generate the Ethos with nemo-explore followed by nemo-ethos, then resume this audit flow with --ethos agents/<name>-ethos/ETHOS.md; or
2. let them create the Ethos themselves from the documentation, then rerun the audit command with --ethos <path>.

Use this user-facing message shape for that skills-present path:

Missing Ethos

Eval Author needs a source of truth for how the agent is supposed to behave before it can generate an audit coverage report.

I could not find `ETHOS.md` at the repository root or an Ethos generated by the NeMo Platform Ethos flow at `agents/<name>-ethos/ETHOS.md`.

ETHOS.md records intended behavior, mission, constraints, success and failure criteria, and what may change.

Docs: https://docs.nvidia.com/nemo-platform/documentation/agents/optimize-agents/ethos

`nemo-explore` and `nemo-ethos` are available here, so I can generate a real Ethos first and then resume audit coverage, or you can create/provide one yourself.

How would you like to move forward?
1. Generate the Ethos for me with `nemo-explore` and `nemo-ethos`.
2. I'll create or provide an Ethos path myself.

Only offer automatic generation when both required skills are present and usable. If those skills are absent, unavailable, or live outside the current assistant environment, do not offer to generate it. Link the docs and tell the user to create or provide an Ethos path, then rerun the audit command with the path to that file.

Use this user-facing message shape for that skills-unavailable path:

Missing Ethos

Eval Author needs a source of truth for how the agent is supposed to behave before it can generate an audit coverage report.

I could not find `ETHOS.md` at the repository root or an Ethos generated by the NeMo Platform Ethos flow at `agents/<name>-ethos/ETHOS.md`.

ETHOS.md records intended behavior, mission, constraints, success and failure criteria, and what may change.

Docs: https://docs.nvidia.com/nemo-platform/documentation/agents/optimize-agents/ethos

I do not have access to both required Ethos creation skills, `nemo-explore` and `nemo-ethos`, in this environment, so I cannot generate one automatically here.

Create or provide an Ethos path, then rerun the audit flow with `--ethos <path>`.

Do not create a placeholder Ethos inside the audit flow, and do not substitute other repository material for it. Contributor docs, operations docs, README files, code, traces, or draft labels are not valid source-of-truth replacements for a missing Ethos. Do not synthesize an audit denominator from those materials, even if the output is marked as draft. The audit denominator depends on the durable agent contract that nemo-explore and nemo-ethos produce.

Eval Author: audit

Read eval-author for the shared standard, vocabulary, and boundaries. This sub-flow generates and validates a finite coverage denominator from <ethos_path> and reviewed audit items, then can measure one ATIF trace and aggregate coverage reports against it. It does not generate tasks yet.

The audit-spec approach has three item kinds in v1:

Every item uses name as its stable coverage key. Names must be unique across the whole file; do not add sequential numeric IDs. Tool references in required_tools, expected_tools, and evidence_required[].tool must match the name of a declared tool item. prohibited_tools may name any syntactically valid tool name, including tools the agent must never call and therefore should not declare as allowed tools.

Write audit artifacts under .eval-author/. Do not edit the customer's source, existing evals, source-of-truth documents, or ETHOS.md.

Scripts

Audit-spec mechanics live under scripts/audit_spec/:

Read scripts/audit_spec/README.md for the current measurement assumptions: ATIF input, Harbor trajectory parsing, v1 tool_calls and capabilities coverage, and coverage aggregation from coverage.json files.

Shared helpers, measurement method contracts, schemas, and examples are documented in scripts/audit_spec/README.md. Runtime dependencies are listed in requirements.txt.

Step 1: Draft Or Update Audit Items

Before drafting or updating .eval-author/audit-items.yaml, read templates/audit.md and schemas/audit.schema.json. Use the template as the worked example and the JSON Schema descriptions as the field definitions. Do not use validation as the primary way to discover the format; validation is the enforcement and repair step after drafting.

Read <ethos_path> and draft audit items at the level between Ethos and runnable tasks: canonical tools, high-level capabilities, and material failure cases. Keep the list finite. Do not create separate items for prompt paraphrases, fixture variants, or ordinary happy-path permutations.

For tool items, use the names that appear in the actual runtime traces or tool registry, including eval-specific tools that may be more precise than product tools named in Ethos prose. If Ethos describes a generic tool such as sqlite but measurement traces expose execute_sql and submit_sql, declare the runtime tool names and connect capabilities or failure cases to those names. Do not invent tool names that will not appear in the measurement surface.

Save the reviewed item proposals as .eval-author/audit-items.yaml. The items file may be either a mapping with an items key or the item list itself. It should use the same item shape shown in templates/audit.md and enforced by schemas/audit.schema.json.

For an initial audit, this file should contain the full proposed denominator. For an update, it may contain only the proposed additions or edits. Existing reviewed audit.md items remain the source of truth in reconcile mode.

Capabilities that do not need tools, such as policy refusals or out-of-scope handling, should use required_tools: []. Failure cases attach to capability names through applies_to; tool-level failure expectations stay on the tool item as expected_failure_behavior.

Step 2: Generate Or Reconcile Audit.md

Create or update .eval-author/audit.md from <ethos_path> and the reviewed item proposals:

uv run --with pyyaml --with jsonschema \
  <skill_dir>/scripts/audit_spec/generate.py \
  --ethos <ethos_path> \
  --items .eval-author/audit-items.yaml \
  --out .eval-author/audit.md

The default mode is reconcile. If .eval-author/audit.md does not exist, it creates the file. If it already exists, the generator parses the existing marked block, updates source metadata such as the Ethos digest, preserves existing item bodies by stable name, appends new proposed items, and reports proposed edits without silently rewriting them. Hand-authored prose outside the marked block is preserved.

By default, the generator treats .eval-author/audit-items.yaml as a partial update proposal. Missing existing items are not stale in that mode, because the items file may contain only additions or edits. Use --items-mode full only when the items file is intended to be the complete denominator; then existing items omitted from the proposal are reported as possibly_stale_items.

Use the explicit modes when the default is not what the user wants:

uv run --with pyyaml --with jsonschema \
  <skill_dir>/scripts/audit_spec/generate.py \
  --ethos <ethos_path> \
  --items .eval-author/audit-items.yaml \
  --out .eval-author/audit.md \
  --mode suggest

uv run --with pyyaml --with jsonschema \
  <skill_dir>/scripts/audit_spec/generate.py \
  --ethos <ethos_path> \
  --items .eval-author/audit-items.yaml \
  --out .eval-author/audit.md \
  --mode reconcile \
  --items-mode full

uv run --with pyyaml --with jsonschema \
  <skill_dir>/scripts/audit_spec/generate.py \
  --ethos <ethos_path> \
  --items .eval-author/audit-items.yaml \
  --out .eval-author/audit.md \
  --mode replace

suggest performs the same comparison as reconcile but writes nothing. replace rewrites the whole file from the item proposal file, including prose outside the marked block, and should be used only when the user wants to discard the existing generated audit file.

The generator prints a JSON summary containing written, added_items, conflicting_items, conflicting_items_applied, and possibly_stale_items. Treat conflicting_items as items where the proposal differs from the reviewed audit item; reconcile mode preserves the reviewed item and reports conflicting_items_applied: false so the user can accept the change manually or through a future editor. If reconcile adds items, finds conflicts, reports stale items in full mode, or detects an agent-name change, an approved audit is demoted to status: draft unless the user passes --status approved.

The generator adds an optional sources entry for Ethos with name: ethos, a path relative to audit.md, and a real sha256 digest. It uses the frontmatter name from ETHOS.md when --agent is omitted. If that inferred name differs from the existing audit's agent, reconcile preserves the reviewed audit name and reports agent_change unless the user passes --agent explicitly. source_refs are advisory provenance notes in v1; the validator preserves them but does not resolve them against sources until a future generator or grammar defines that reference format.

Step 3: Validate

Run validation after every generated or hand-edited audit file:

uv run --with pyyaml --with jsonschema \
  <skill_dir>/scripts/audit_spec/validate.py --audit .eval-author/audit.md

schemas/audit.schema.json is the canonical structural schema. The Python validator applies that schema first, then checks any source digests that are provided and cross-item references such as required_tools and applies_to.

Validation proves only structure and references, not that the denominator is complete or correct.

Step 4: Measure One ATIF Trace

After validation, measure one completed trial directory or one ATIF trajectory file. Use --measure to select one or more comma-separated measurement methods; the default is tool_calls.

uv run --with-requirements <skill_dir>/requirements.txt \
  <skill_dir>/scripts/audit_spec/measure.py \
  --audit .eval-author/audit.md \
  --trial-dir <harbor-job-dir>/<trial-dir> \
  --measure tool_calls \
  --out-dir .eval-author/audit-measurements

The current --trial-dir reader supports Harbor-style trial directories that normally contain agent/trajectory.json; agents that do not emit ATIF may not have that file. When the trace file is already known, pass it directly and stamp the task explicitly:

uv run --with-requirements <skill_dir>/requirements.txt \
  <skill_dir>/scripts/audit_spec/measure.py \
  --audit .eval-author/audit.md \
  --trace <path-to>/trajectory.json \
  --task-id <task-id> \
  --run-id <run-id> \
  --measure tool_calls \
  --out-dir .eval-author/audit-measurements

--measure may be passed more than once or as CSV, for example --measure tool_calls,capabilities. The default is tool_calls; include capabilities when the user wants the same trace to count against capability items. The script loads the trajectory once, then runs each selected method against the same parsed Harbor trajectory model. Unknown method names fail before the trace is loaded.

When capability evidence contains non-tool kinds such as user_intent, output, outcome, policy_boundary, or verifier, inspect the trace and write a structured judgment file under .eval-author/ using schemas/audit_capability_judgments.schema.json. Each judgment must target the capability name plus the zero-based evidence_required index, copy the evidence kind and description exactly, and judge only non-tool evidence. Do not write judgments for tool_call; the script measures those deterministically. Set the sidecar's required trace_sha256 to sha256: followed by the lowercase SHA-256 digest of the exact ATIF trajectory file you inspected. Measurement rejects the sidecar if those trace bytes have changed or another trace is used. Use the capability description, evidence description, and concrete trace content as the rubric: mark satisfied only when the trace clearly demonstrates the requirement, missing when it clearly does not, and unclear when the trace is ambiguous or insufficient. Include brief rationale and supporting trace references when available. A subjective judgment can satisfy only the non-tool evidence it targets; it cannot override missing required tools or missing tool_call evidence. Pass the sidecar when measuring capabilities:

uv run --with-requirements <skill_dir>/requirements.txt \
  <skill_dir>/scripts/audit_spec/measure.py \
  --audit .eval-author/audit.md \
  --trace <path-to>/trajectory.json \
  --task-id <task-id> \
  --run-id <run-id> \
  --measure capabilities \
  --capability-judgments .eval-author/capability-judgments.json \
  --out-dir .eval-author/audit-measurements

Capability coverage is conjunctive: every deterministic requirement must be satisfied, and every judged evidence requirement must be satisfied. Missing judgments leave the capability uncovered. Stale judgments fail measurement before the script writes a coverage report, including judgments bound to a different trace digest.

The script writes one folder per task, run, and method. Task and run ids are encoded as single path components so ids containing / cannot create nested or escaping paths:

.eval-author/audit-measurements/task=<encoded-task-id>/run=<encoded-run-id>/<method>/coverage.json
.eval-author/audit-measurements/task=<encoded-task-id>/run=<encoded-run-id>/<method>/details.json

coverage.json uses the shared coverage schema and contains only the stable audit item names this trace covered plus provider-neutral subject identity (trace, trace_format, task_id, and run_id). It also records item_kind_count, the denominator for the measured item kind. Coverage aggregation should consume this file and ignore method-specific debug details. details.json is specific to the selected method and carries traceability data for humans.

For current method semantics and details schemas, see scripts/audit_spec/README.md.

The script validates coverage.json against schemas/audit_coverage.schema.json and validates details.json against the selected method's details schema before writing. Use the next step to union coverage across tasks and runs.

Step 5: Aggregate Coverage Reports

After measuring one or more traces, aggregate the per-trace coverage.json files into a coverage report:

uv run --with-requirements <skill_dir>/requirements.txt \
  <skill_dir>/scripts/audit_spec/report.py \
  --audit .eval-author/audit.md \
  --coverage-dir .eval-author/audit-measurements \
  --out .eval-author/audit-coverage-report.json

Use --coverage <path-to-coverage.json> for explicit files, or repeat --coverage-dir and --coverage when the inputs are split across directories. The script scans coverage directories recursively for files named coverage.json, validates every input against schemas/audit_coverage.schema.json, and rejects inputs whose audit metadata no longer matches the current audit.md. A status-only mismatch, such as measurements produced while the audit was draft and aggregated after it became approved, is reported as a warning instead of forcing a re-measure.

The aggregate report uses schemas/audit_coverage_report.schema.json. It contains overall and per-kind count summaries, the union of covered item names, warnings, the measured item kinds, and uncovered_items. Each uncovered item includes the original audit item plus generation-oriented context: a stable reason, a one-sentence focus, likely needed_tools, and the item's evidence_required. Use reason: not_measured_by_any_method to distinguish gaps that no included measurement method could close from reason: not_covered_by_any_input_report, which means the item kind was measured but no input report covered that item. Treat that list as the input for a later task-generation step.

Next Steps

• For audit-generation inputs and reconciliation modes, return to Step 2: Generate Or Reconcile Audit.md.
• When aggregate uncovered_items includes actionable items (reason: not_covered_by_any_input_report), hand off to eval-author-task-create to scaffold and prove one gap at a time. Items with reason: not_measured_by_any_method, such as failure-case items and capability items measured without the capabilities method, stay audit findings only in v1.