Generate, validate, and measure an audit-spec coverage denominator for Eval Author. Use when the user wants a hand-editable audit.md file derived from Ethos, needs schema enforcement for declared tools, capabilities, failure cases, evidence, and references, or wants to measure which audit items one ATIF trace covers. Changes none of the user's source, and saves audit artifacts under `.eval-author/`.
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Quellanweisungen · Schreibgeschützte Vorschau
name
eval-author-audit
description
Generate, validate, and measure an audit-spec coverage denominator for Eval Author. Use when the user wants a hand-editable audit.md file derived from Ethos, needs schema enforcement for declared tools, capabilities, failure cases, evidence, and references, or wants to measure which audit items one ATIF trace covers. Changes none of the user's source, and saves audit artifacts under `.eval-author/`.
triggers
["generate audit.md from ETHOS.md","validate audit.md coverage schema","measure audit.md coverage against a harbor trace","check audit.md coverage denominator","what should my evals cover from the agent ethos","review the audit coverage denominator"]
not-for
["eval-author (use for the standard, the boundaries, and to pick a sub-flow)","eval-author-discover (use to prove whether a Harbor suite is runnable)","eval-author-inspect-trace (use after eval-author selects an Intake trace)","nemo-experimentalist (use to optimize an agent from Insights or explicit datasets)"]
compatibility
Python 3.11 or later for generation and validation; Python 3.12 or later for ATIF measurement via Harbor's trajectory model. Dependencies are listed in requirements.txt. Generation, validation, and measurement read local files only; they do not start Harbor jobs or call platform services.
maturity
alpha
license
Apache-2.0
user-invocable
true
allowed-tools
["Bash","Read","Write","Grep","Glob"]
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.md
and reviewed audit items, then can measure one ATIF trace against it. It
does not generate tasks or aggregate coverage reports yet.
The audit-spec approach has three item kinds in v1:
Kind
Meaning
tool
A canonical tool name the agent may call
capability
A high-level behavior the agent should exercise
failure_case
Expected safe behavior when a capability cannot proceed normally
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, and v1 tool_calls coverage only.
Script
Use it to
scripts/audit_spec/generate.py
Create, reconcile, replace, or preview .eval-author/audit.md from ETHOS.md and reviewed item proposals
scripts/audit_spec/measure.py
Measure one ATIF trace or Harbor trial directory against audit.md and write coverage/details files for each selected method
scripts/audit_spec/validate.py
Validate the marked audit-spec block in
audit.md
Shared helpers are private modules in the same tree:
scripts/audit_spec/_schema.py and scripts/audit_spec/_markdown.py.
Measurement uses Harbor's harbor.models.trajectories.Trajectory to read ATIF
files. Measurement methods live under scripts/audit_spec/measurements/; v1 ships
scripts/audit_spec/measurements/tool_calls.py.
Shared coverage output is defined by schemas/audit_coverage.schema.json.
Tool-call debug output is defined by
schemas/audit_tool_calls_details.schema.json.
Concrete instances live under examples/schemas/tool_calls.coverage.json and
examples/schemas/tool_calls.details.json.
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.md 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.md and the reviewed item
proposals:
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:
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 <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.
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:
--measure may be passed more than once or as CSV, for example
--measure tool_calls,boundary. 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.
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:
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.
The only v1 measurement method is tool_calls. It covers only audit items whose
kind is tool by matching each tool item's name against ATIF
steps[].tool_calls[].function_name, including embedded subagent trajectories.
It does not judge capabilities, failure cases, user intent, output quality, or
other evidence kinds.
The script validates coverage.json against schemas/audit_coverage.schema.json
and validates details.json against the selected method's details schema before
writing. It does not union coverage across tasks; treat coverage aggregation as
the next audit PR.
