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Scorers

Part of the TeamAI CLI Usage Guide.

A scorer is a rubric a judge applies to a workflow step's work or to a finished agent session. It is a Markdown file at scorers/<name>.md in the team repo.

scorers/review-quality.md:

---
labels:
  - value: good
    score: 1
    description: The change does what the step asked, with tests.
  - value: partial
    score: 0.5
    description: The change works but misses part of the request.
  - value: bad
    score: 0
    description: The change does not do what the step asked.
passingScore: 1
samplingRate: 25
---
Judge whether the change does what the step asked. Read the diff and the session.
Field Meaning
labels 2 to 20 { value, score, description }. At least one scores at or above passingScore, and one below it.
passingScore A label passes when its score is at or above this.
samplingRate, model, isolation Optional. Percent of passes judged, 0 to 100, default 25. The judge's model: a team model alias, resolved per judge tool, or a model name, which goes unchanged to the first judge tool; default the tool's. verified or best-effort.
sessions Optional. true: the scorer also judges finished sessions.

The text after the frontmatter is the judge's prompt. teamai pull caches valid scorers, which teamai status lists, and warns about the rest. teamai review --branch fails on a malformed scorer, on a model alias the branch's models/aliases.yaml maps for no tool, on any file in scorers/ other than <name>.md, <namespace>/<name>.md, scoring.yaml and README.md, on a scoring.yaml with a key or value it does not know, and on a credential or internal address in any file there, as it does for rules.

scorers/ops/review-quality.md belongs to the ops namespace. Members get it when their role or project lists it:

# manifest/roles.yaml
roles:
  - id: ops
    resources: { knowledge: [], skills: [], scorers: [ops] }

It replaces a root scorer of the same name. One name in two active namespaces is an error. A scorers/<dir>/ that no role or project lists is shared: every member gets its scorers, and one named like a root scorer is an error. A team without roles or projects gets every namespace.

Choose the judge's model

# scorers/review-quality.md, in the frontmatter
model: fast
# models/aliases.yaml
aliases:
  fast:
    claude: haiku

Judge tools are tried in order, and the first whose entry gives a model judges. When none does, score --next lists the work under skipped with the reason and leaves it open for a driver whose tools can, and teamai doctor warns about the scorer. A member's ~ entry for the tool sends it to the tool's default model. So does an active teamai models switch, except that Claude keeps opus, sonnet or haiku and routes it through the switch; a switch also drops the alias's effort. CodeBuddy judges run without the alias's effort.

Each label records what judged:

"judge":{"kind":"model","tool":"claude","model":{"requested":"haiku","reported":"claude-haiku-4-5","alias":"fast"},"isolation":"verified","access":"read"}

requested is the model the judge was launched with, or the one the lease named for a driver's judge. reported is the one the tool reports, or the submission's judge.model. alias stays when the tool default judges. An alias effort adds "effort":{"requested":"low"}.

Score a step

# workflows/review-and-fix.yaml
steps:
  - id: review
    prompt: Review the change.
    scorers: [review-quality]

A name with no valid scorers/<name>.md fails teamai review --branch; teamai pull warns and the step runs without it. An older teamai does not know scorers, so update every member and runner first. Workflows shows the long form.

A run and its judge

$ teamai workflow start review-and-fix --driver local     # the run settles; judging never holds it
$ teamai score --next --json '{"driver":{"kind":"local","machine":"m1","workspace":"/work","can":{"judge":{"isolation":"verified","tools":["claude"]}}}}'
{"data":{"lease":{"ref":"s5d1e07c2a9b4a/a91c0d7e0","subject":{"runId":"r3f9…","step":"review","pass":1},
  "evidence":"…/scores/evidence/a91c0d7e0/","prompt":"…/prompt.md","answer":{"schema":{…}},
  "submit":"teamai score --lease s5d1e07c2a9b4a/a91c0d7e0 --submit --stdin"}}}
$ teamai score --lease s5d1e07c2a9b4a/a91c0d7e0 --json '{"driver":{"kind":"local","machine":"m1","workspace":"/work"}}'
{"data":{"label":{"scorer":"review-quality","label":"good","score":1,"passed":true,…},"published":true}}

samplingRate picks the succeeded passes to judge, the same way on every machine. workflow next never hands out judge work. --lease <ref> judges here with the tool the lease names, in a confined profile, and settles the lease like any driver's answer. A failed judge leaves the lease open.

The label records used.skills, the team skills the pass or its subagents loaded, found as for a session from what the checkout held as teamai delivered it when the pass started. A workflow: step's label lists every skill its child passes recorded, and has no used when none of them recorded one.

The evidence directory holds evidence.json, prompt.md, diff.patch when a range was recorded, repo/ (tracked files at the judged commit, symlink targets stored as text), transcripts/ (with readable subagents) and outputs.json. Every file is redacted, including the values of the secrets the team declares in env/secrets.yaml. A tracked file that is not text is left out and listed under omitted. Missing evidence is listed as gap codes. It is removed when the lease settles or lapses.

Any driver

A driver runs its own judge on prompt.md, with the evidence directory as its only readable root, and submits the answer:

$ teamai score --next --json '{"driver":{"kind":"t3","machine":"m1","workspace":"/work","can":{"judge":{"isolation":"verified","access":"read","tools":["claude"]}}},"running":[]}'
$ teamai score --lease s5d1e07c2a9b4a/a91c0d7e0 --submit --stdin <<'JSON'
{"driver":{"kind":"t3","machine":"m1","workspace":"/work"},"outcome":"success",
 "answer":{"label":"good","reasoning":"…"},"judge":{"tool":"claude"}}
JSON
{"data":{"label":{"scorer":"review-quality","label":"good","score":1,"passed":true,…},"published":true}}

With run, score --next finds the run's scope by its id, so a driver can ask from the run's worktree or from outside the checkout the run started in, and judge and submit its lease from there too. To judge one session by id, lease it with score --session.

can.judge Meaning
isolation verified or best-effort. Only work whose required level it meets is leased. A session you lease by id that asks for more fails isolation_unmet.
access What the judge can do: answer (no tools), read (reads the evidence) or full (any tool), the default. Labels record it.
tools In your order of preference. The first that resolves the scorer's model wins.

A label the scorer does not list is refused, and the lease stays open. running lists the refs you still judge, which renews them; a lease lapses 30 minutes after it was taken or renewed. Never list a judge ref in workflow next's running: it answers judge_lease and changes nothing. run, a run id, takes only that run's workflow passes and never a session. canTakeLease: false renews running and takes nothing. teamai score --help has every field.

Your driver names the team skills a pass loaded when it reports the pass, and the pass's labels carry them:

$ teamai workflow report r3f9… --json '{"attempt":"…","revision":3,"outcome":"success","driver":{…},"used":{"skills":["review-notes"]}}'

Names only: a value that is not a skill name, such as a path, is dropped. A report without used gives labels without it; "skills":[] says the pass loaded no team skill.

On demand

$ teamai score --run r3f9… --step review --scorer review-quality                     # judge the latest succeeded pass here
$ teamai score --run r3f9… --step review --pass 2 --scorer review-quality --queue     # any driver's --next takes it

Sampling does not apply, and judge work that failed or expired opens again. A scorer the run pinned judges with that rubric; any other scorer teamai pull cached, with its current one. Without --queue, the first judge tool on PATH runs, preferring the tool that ran the pass. An unknown run, step, pass or scorer fails and lists the ones that exist. A scorer whose model no judge tool here runs fails judge_model_unavailable and stays open. The error's labels lists what the other scorers labelled.

Score a session

$ teamai score --scorer review-quality          # inside an agent session: scores that session
ℹ Scoring claude-code session 7c1e… in /work/app
ℹ First prompt: Add retries to the client
ℹ review-quality: bad (score 0, did not pass). Reasoning: ~/.teamai/scores/reasoning/9f2c….md
ℹ To share what went wrong, ask your agent to read ~/.teamai/scores/reasoning/9f2c….md and share it with the team (teamai contribute --session-id 7c1e…).
$ teamai score --session 7c1e… --source codex --scorer review-quality --push

Outside a session, pass --session <id>. Scoring never guesses the session. --source takes the platform names of teamai session migrate --source. Leave it out when an agent session variable such as CLAUDE_CODE_SESSION_ID, or the session's captures, name one platform. The judge reads the redacted session with its subagents' transcripts, and sampling does not apply. Its evidence.json lists skills: available, the team skills the captures show teamai delivered, each with its skills namespace (namespaceUnknown when two identical copies cannot be told apart) and its description (left out when the frontmatter does not parse), and used. A session captured by an older release has the skills_unknown gap instead.

The judge reads at most 200,000 characters of a session. A session scorer that names a cheaper model alias keeps a long session cheap to judge.

The label is kept in the scope the session ran in, from whichever directory you score it. It records agent, what the session ran as, from the captures the session-start hook writes. It records used.skills, the team skills the session or its subagents loaded that its captures show teamai delivered, unchanged, when it started, including by the pull it runs at start. A later pull does not change that. A skill the captures cannot place, such as a member's own skill with a team skill's name, is left out. --push publishes every pending label of that scope. With usage reporting off, it prints Usage reporting is off, so labels stay on this machine. and the labels stay pending. An http team repo has no teamai-reports, so --push fails with unsupported_repo and the labels stay here. teamai doctor warns while a label waits to be published.

Label a session by hand

A judge is a model, and a model can be wrong. Label a few sessions yourself, and the Scorers page shows how often the judge agrees with you.

The agent just said the retry loop is fixed, and it is not. Still inside the session:

$ teamai score --scorer review-quality --label missed-bug --push
ℹ Labelling claude-code session 7c1e… in /work/app
ℹ First prompt: Fix the retry loop in sync.ts
ℹ review-quality: missed-bug (score 0, did not pass), by hand. No judge ran.

A session you finished yesterday, from any directory:

$ teamai score --session 7c1e… --source claude-code --scorer review-quality --label good --push

A value the scorer does not list fails, and nothing is written:

$ teamai score --scorer review-quality --label flaky
{"error":{"code":"label_invalid","message":"\"flaky\" is not a label of scorer review-quality. Use one of: good, partial, missed-bug."}}

--label takes one --scorer, a name teamai pull cached. The label's score and pass come from the scorer, as for a judge's label, and it has no reasoning file. It is stored and published as a judge's label is, with judge: { kind: "human" }. A hand label and a judge's label of the same session are kept side by side: rescoring with the judge never replaces a hand label. When several members label one session by hand, the newest hand label is the one counted.

A session a teammate pushed

Alice pushed her session with teamai session push. In your checkout of the same project:

$ teamai session pull
$ teamai session list

Sessions for github.com/acme/app:

  SESSION                                             AUTHOR      PLATFORM        MSGS  UPDATED
  ─────────────────────────────────────────────────────────────────────────────────────────────
  claude-code_fix-the-retry-loop-in-sync-ts_20261021  alice       claude-code       84  2026-10-21

  1 session(s)

$ teamai score --team-session claude-code_fix-the-retry-loop-in-sync-ts_20261021 --scorer review-quality --label missed-bug --push
ℹ Labelling claude-code session 7c1e… in packages/api
ℹ Pushed by alice
ℹ First prompt: Fix the retry loop in sync.ts
ℹ review-quality: missed-bug (score 0, did not pass), by hand. No judge ran.

The label is about the session Alice ran, so it pairs with the judge's label from her machine. Leave out --label and the judge scores the pushed session:

$ teamai score --team-session claude-code_fix-the-retry-loop-in-sync-ts_20261021 --scorer review-quality
ℹ Scoring claude-code session 7c1e… in packages/api
ℹ Pushed by alice
ℹ First prompt: Fix the retry loop in sync.ts
ℹ review-quality: good (score 1, passed). Reasoning: ~/.teamai/scores/reasoning/2b7d….md

When two members pushed a session with that name, the command fails and lists them. Pick one with --author:

$ teamai score --team-session claude-code_fix-the-retry-loop-in-sync-ts_20261021 --author bob --scorer review-quality --label good

--team-session reads the sessions pushed for the project of the current directory on teamai-reports. On a git team it reads where teamai kept sessions on the default branch before only when teamai-reports has no session of that name, and --author picks among the members who pushed the name where it was found. It cannot be combined with --session, --source or --json. The session is redacted before the judge reads it, also when it was pushed without --scrub. The directory it names is the one the session ran in, relative to its repository. Its label takes agent from the archive (what a scrubbed session keeps), so the Scorers page groups it by tool, model and team revision with the sessions scored where they ran. A session archived without one, such as one pushed by teamai 0.28 or earlier, counts as Not recorded there.

When the scorer changes

You change Hand labels
The prompt, or a label's description Still pair. A hand label is your reading of the session, and the judge's prompt does not change it. Rescore with the judge, and the new rubric shows how often it agrees with the hand labels you already gave.
A label's value, such as bad to missed-bug A hand label of bad pairs with no label of the new rubric. Label those sessions again.
passingScore, or a label's score Label again: a hand label keeps the pass it was stored with.

With your own judge

A driver that judges with its own tool, such as a Cursor session or an orchestrator, leases a session instead of having it judged here. With --json declaring can.judge, as score --next takes it, score --session answers a lease. A subagent or a separate agent judges it from prompt.md and the evidence directory only, and the driver submits the answer:

$ teamai score --session 7c1e… --source cursor --scorer review-quality \
    --json '{"driver":{"kind":"cursor","machine":"m1","workspace":"/work","can":{"judge":{"isolation":"best-effort","access":"read","tools":["cursor"]}}}}'
{"data":{"lease":{"ref":"s0c4a9e2f7b1d3/a6e8f2107","source":"session","scorer":"review-quality","prompt":"…/prompt.md","cwd":"/work/app",
  "submit":"teamai score --lease s0c4a9e2f7b1d3/a6e8f2107 --submit --stdin",…}}}
$ teamai score --lease s0c4a9e2f7b1d3/a6e8f2107 --submit --stdin <<'JSON'
{"driver":{"kind":"cursor","machine":"m1","workspace":"/work"},"outcome":"success",
 "answer":{"label":"good","reasoning":"…"},"judge":{"tool":"cursor","model":"…"}}
JSON

--source names where the session is read, and tools what judges it. The lease is taken whatever sampling, the six idle hours, scoring.sessions, the scorer's sessions, an earlier label or daily_limit say, and counts toward the day's daily_limit. Several scorers share one lease with --batch. Nothing is taken when a scorer asks for more isolation than you offer (isolation_unmet), when a scorer pins a model none of your tools runs (judge_model_unavailable: a pinned model needs a tool teamai knows, such as cursor), or while another live lease holds the work (busy). The label records your tool, access and the model you report.

Try a scorer file

$ teamai score --session 7c1e… --scorer ./scorers/review-quality.md

Before you push a new or edited scorer, try it on a session from your checkout of the team repo. --scorer takes a path, a value with a / or ending in .md, relative to the current directory. The file is checked as teamai pull checks it, and an invalid file fails with pull's message before any judge runs. The judge runs as it does for a cached scorer, also with --batch and inside a session.

The file's label is never stored, so the command takes no --push, and --run takes scorer names only. The output prints the label with its reasoning and "stored": false. Next to it, stored lists the newest label kept on this machine for that session and the scorer of the same name, one per rubric, so you see whether your edit changes the verdict. control is the file judged a second time, on the session's first prompt alone. When it passes, it carries a warning: the rubric passes a session where the agent did nothing, so it may not judge the work. It warns and never fails, since a rubric may judge the prompt by design.

A workflow or a whole team branch is tried with teamai pull --team-ref <branch> (distribution).

Finished sessions

Unattended runs now publish and score their sessions by default. In CI, on a cloud VM or in a hosted job (CI or TEAMAI_MODE=unattended), teamai score --drain archives the sessions this machine captured as unattended, and the step sessions of the workflow runs driven here, scrubbed, on teamai-reports. Every scorer with sessions: true judges a sample of those captured sessions, within daily_limit. To keep the old behavior, set unattended: off in both team files:

# teamai.yaml
sessions: { publish: { unattended: off } }
# scorers/scoring.yaml
sessions: { unattended: off }

sessions in scorers/scoring.yaml takes a policy for each run mode. The boolean form keeps working:

# scorers/scoring.yaml, or scorers/<namespace>/scoring.yaml
sessions: { attended: auto, unattended: auto }   # true = auto in both modes, false = off in both
daily_limit: 10                                  # per data home: on a fresh CI runner it caps one job, not one day
Policy attended unattended
off Nothing. The default. Nothing.
ask When the friction reminder offers teamai-share-learnings, it proposes the scorers that sample the session, and they judge it once the person confirms. Acts as off, since nobody is there to answer.
auto A session is judged once nothing has happened in it for six hours, by the background judge. A session is judged when teamai score --drain runs, before the job ends. The default.

Each mode resolves on its own, from the first of these that sets it: a member's scoring.sessions in config.yaml, the active namespaces' scoring.yaml files, the root scorers/scoring.yaml, the default. When two active namespaces disagree, the most restrictive policy wins (off, then ask, then auto), and so does the lowest daily_limit. A partial mapping such as { attended: ask } leaves the other mode to the next level:

# config.yaml
scoring:
  sessions: { attended: ask }   # attended only; unattended still follows the team

scoring in ~/.teamai/config.yaml applies in every workspace and self scope on the machine, to each mode and limit their own config.yaml leaves unset, and only where it is stricter than the team: a mode the team leaves on can be turned off, and a lower daily_limit applies. A machine never starts judging:

scorers/scoring.yaml ~/.teamai/config.yaml In a workspace
sessions: true scoring: { sessions: { unattended: off } } attended auto, unattended off
sessions: { attended: ask } scoring: { sessions: false } off in both modes
sessions: false scoring: { sessions: true } off in both modes: the team's off stays
daily_limit: 4 scoring: { daily_limit: 2 } 2 sessions a day in each workspace
daily_limit: 4 scoring: { daily_limit: 20 } 4 sessions a day in each workspace

Like the team's, the machine's limit counts each workspace on its own: a laptop with three workspaces under daily_limit: 2 can still judge six sessions a day.

teamai doctor says what decided each mode and the limit, once a setting does:

Session judging: attended auto, set by scorers/scoring.yaml sessions; unattended off, set by ~/.teamai/config.yaml scoring.sessions.unattended; up to 2 sessions a day, set by ~/.teamai/config.yaml scoring.daily_limit.

teamai doctor reports a value that is not off, ask or auto (or a boolean), with its file and key, and teamai ignores it, so that mode falls to the next level. Sessions covers sessions.publish, which decides whether sessions are archived.

What is judged Set by
A workflow step's passes The step's scorers, sampled by each scorer's samplingRate. sessions plays no part.
A captured session: yours, or one a CI or TEAMAI_MODE job ran that teamai did not launch sessions for the session's run mode, for every scorer with sessions: true, sampled by its samplingRate.
A session teamai launched, such as a workflow step or a judge Nothing. It writes no capture, and its step is judged as a pass.
A session of a tool teamai reads no sessions from, such as Pi Nothing. teamai doctor notes each such tool the team enables, in each mode whose publish or scoring policy is not off. With the defaults, that is both modes.

A namespace scorer judges a session only when the roles and projects each of its starts recorded make that namespace active. score --next hands out workflow work first, then sessions. A session lease has "source":"session", and its cwd is where to submit from.

A session is judged once per scorer and rubric. A release, a lapse, or a local judge that fails closes it. An answer to a lapsed lease is still recorded, unless a judge has labelled the session since or your own judge leased it again. scoring.daily_limit in config.yaml sets how many sessions one data home leases per local day, 10 by default, counting those that failed or could not be read. On a fresh CI runner each job starts a new data home, so the limit caps one job, not one day. 0 turns session scoring off. Editing a scorer's rubric makes the captured sessions judge work again for it, and the daily limit spreads that over the following days.

With attended: auto, you do not have to run a driver. After the pull at a session's start, when the session's project or your user scope scores sessions, teamai starts a background judge for both scopes. It judges each finished session with a judge tool installed here, stops taking work after 30 minutes, and leaves the labels pending for the next teamai pull to publish. Each lease is one judge call on your account, at most daily_limit a day. A session scorer with a cheap model alias, such as model: fast, keeps that cheap. To stop it, set scoring.sessions: { attended: off } in config.yaml, or in ~/.teamai/config.yaml for every workspace on this machine.

The background judge uses the tool's home that teamai init recorded, such as a moved CLAUDE_CONFIG_DIR. It does not see a credential or base URL that only your shell sets, notably on Windows. Keep those in the tool's sign-in, your user environment, or the tool's settings.json env.

A teamai older than this one rejects a scorer with sessions: true, so update teamai on every member's machine before the team adds one. An older teamai that takes judge work on your machine, such as a driver's own copy, removes the rubrics of session leases, so update every copy there before you turn on scoring.sessions.

Drain before an unattended job ends

$ teamai score --drain
✔ Judged 1 sampled session here (review-quality)
✔ Published 1 label to teamai-reports
✔ Archived 1 captured session to teamai-reports (scrubbed)
✔ Reported usage to stats/<member>.yaml
ℹ Left open: 1 session whose scorer pins a model no judge tool here runs (judge_model_unavailable). A driver whose tools run it takes it with: teamai score --next --json '{"driver":{…,"can":{"judge":{…}}}}'

Every unattended driver ends with teamai score --drain. The local driver runs it at the end of an unattended run, unless publishing and scoring of unattended sessions are both off. The skill driver, T3, Paseo and a CI script run it as their last step. It works in this order:

  1. In unattended mode, every session this data home captured as unattended counts as finished, the calling session included. Attended sessions keep the six-hour rule.
  2. It judges every open judge entry this machine can judge: sampled step passes, and sampled sessions the policies allow, within daily_limit. While another judge pass runs on this machine, such as the background judge, it judges nothing and says so. Run the drain again once that pass ends.
  3. It publishes every pending label, as score --push does, and the workflow run records a run could not publish, as teamai pull does.
  4. With publish: auto for the current mode, it archives on teamai-reports, scrubbed, the finished captured sessions of that mode and the step sessions of the runs driven here that are not archived yet. A step session it cannot archive from here, because teamai reads no sessions from its tool or this machine cannot read it, is listed once as skipped with the reason, and never tried again. The other step sessions of that run are still archived.
  5. It reports usage to stats/<member>.yaml, as teamai pull does, so the counts of an ephemeral runner reach the team.
  6. It prints what is left: entries whose scorer pins a model no judge tool here runs (judge_model_unavailable), or whose isolation this machine does not meet, and every open entry when no judge tool is installed here. Those wait for another driver's score --next.

A drain finishes and publishes only the sessions of its own run mode, so on a machine that is not in unattended mode, run TEAMAI_MODE=unattended teamai score --drain to drain unattended sessions.

The drain exits 0 when nothing is left that it can do here, entries left for other drivers included. It exits 1 when a judgment, a publish or a usage report failed. A failed publish or usage report stays pending, and the next --drain retries it. A failed judgment is final: the drain prints it and never retries it. To ask for a new one, run teamai score --session <id> --source <platform> --scorer <name>.

Where the drain runs decides what it sees of the calling session:

CI job:  claude -p "…"  →  tool exits  →  teamai score --drain      # the job's last step: the session is complete
skill:   … last turn of an unattended session → teamai score --drain  # the session as it stands now

As the job's last step, after the tool exits, the drain sees the session complete. From inside a session, it judges and publishes that session as it stands, and its later turns are neither judged nor published again.

Runs, judge attempts and leases live in the runner's data home. A lapsed session lease or a failed judge attempt is final, and a runner killed before its drain loses that judging. A long-lived VM runs teamai score --drain at the end of each job.

Batching

steps:
  - id: review
    prompt: Review the change.
    scorers: { names: [review-quality, test-coverage], batch: true }

The scorers of one pass that resolve to the same tool and model share one lease, one evidence directory and one prompt. The lease lists scorers and batched: true, and the answer has one label per scorer:

{"driver":{"kind":"t3","machine":"m1","workspace":"/work"},"outcome":"success",
 "answer":{"review-quality":{"label":"good","reasoning":"…"},"test-coverage":{"label":"thin","reasoning":"…"}}}

A step's batch wins over the workflow's defaults.scorers.batch, which wins over batch in scorers/scoring.yaml. --batch does the same for --run. Each label records judge.batched: true. One label a scorer does not list refuses the whole answer, and the lease stays open.

Isolation

# scorers/scoring.yaml
isolation: verified
batch: true

scorers/scoring.yaml holds the team's scoring settings. scorers/ops/scoring.yaml applies to members with the ops namespace active, as its scorers do, and replaces the root file's key of the same name. When two active namespaces set isolation, the strictest wins. When they set batch differently, those members get no judge work until one file changes.

The team's isolation, a scorer's isolation, and scoring.isolation in this scope's config.yaml or in ~/.teamai/config.yaml may each ask for verified. The strictest wins; the default is best-effort. A value teamai does not know counts as verified, with a warning. teamai doctor prints the level, what sets it, the scorers that ask for more, and the level each judge tool here reaches:

Scoring isolation: verified, set by scorers/scoring.yaml isolation.
Scoring isolation: best-effort, the default. scorers/ops/review-quality.md asks for verified for its own work.
Judge tools here: claude 2.1.308 (Claude Code) is verified, codebuddy 2.3.0 is best-effort.

The local judge runs each tool in its own profile:

Tool Level access Profile
Claude Code 2.1.308 verified read --restricted, Read, Grep and Glob only, no MCP, no saved session.
codex-cli 0.163.0 verified read A temporary home whose permission profile lets its shell read only the evidence, with no network and no writes.
OpenCode v2.1.2 verified outside any git or hg checkout, else best-effort read Standalone, with isolated settings, database and data dirs; command, edit, web and external-directory permissions denied; judge sessions deleted after reading.
CodeBuddy, WorkBuddy best-effort full Read-only tools, no MCP, no saved session. It lists about 70 tools whatever --tools says. Not signed in fails judge_tool_unavailable.
Cursor CLI best-effort full Ask mode in Cursor's sandbox.
Pi Coding Agent best-effort full Only its read, grep, find and ls tools; no context files, extensions or skills; no saved session.
Antigravity CLI best-effort read Plan mode in agy's sandbox: it reads files, runs no shell and writes nothing in the evidence. The judge's conversation stays in agy's history.
Any other version best-effort as above The same profile, unproven.

A best-effort tool cannot judge for a scorer that requires verified, nor can your own judge at a lower level than required. Prompts travel through stdin or a file. Tool login and supported model-switch settings are kept. A scorer's model that no judge tool here runs leaves the work open for another driver, without changing the workflow result. Choose the judge's model says which tool judges.

Where labels go

Path What
<dataHome>/scores/labels.jsonl Every label on this machine.
<dataHome>/scores/reasoning/ The judge's reasoning, one file per label. Never published.
<dataHome>/scores/raw/ A failed local judge's output, until a later attempt replaces it.
<dataHome>/sessions/captures/ What each session started as, written by the session-start hook. Removed a day after the session's file is gone.
scores/<member>/<YYYY-MM>.jsonl on teamai-reports Published labels, the newest per scorer, rubric, subject and judge kind. Labels wait in <dataHome>/scores/pending/ until a push lands, and stay there while usage reporting is off.

Labels never enter the run or its record. Workflow Runs shows each pass's labels, and what judging cost apart from the run's own tokens. For a run on this machine that counts every judge attempt; for a run a member published, only the labelled ones. The dashboard's Scorers page shows each scorer's pass rate over time and by configuration.