Spawn Ori as a subprocess to run a throwaway model eval on a pinned harness and model, then relay the results. Use when the user asks which model they should use, wants to compare models, wants to measure whether their agent or prompt does the right thing, wants to catch regressions in agent behavior, or asks how good their current model is. Applies to any codebase in any language. Do not use for plain unit tests that involve no model, and do not use to re-run an eval that already exists (run `ori eval <file>` directly).
Spawn Ori as a subprocess to run a throwaway model eval on a pinned harness and model, then relay the results. Use when the user asks which model they should use, wants to compare models, wants to measure whether their agent or prompt does the right thing, wants to catch regressions in agent behavior, or asks how good their current model is. Applies to any codebase in any language. Do not use for plain unit tests that involve no model, and do not use to re-run an eval that already exists (run `ori eval <file>` directly).
Spawn Ori Eval
Ori writes and grades the eval on a pinned harness and model, so the bench is identical for every coding agent. You run Ori, keep the user informed, and relay the result. An eval you write yourself is not reproducible, and a score change must come from the user's agent, not from the environment.
Pins
This section gives the two models. The other sections use the names <RUN_MODEL> and <JUDGE_MODEL>. Replace each name with the model that this section gives. To change a model, change only the two lines that follow.
RUN_MODEL is openai/gpt-5.6-terra. The run uses this model.
JUDGE_MODEL is openai/gpt-5.6-terra. The eval judge uses this model.
Steps
Do these in order. One line, one action. Appendix letters point to the detail and run in step order, except the troubleshooting table, which is a lookup and comes last.
Create the run directory and derive its path from the repo root (appendix A).
Tell the user where the run directory is.
Read whatever is already in the directory without changing it, and tell the user what is there and how old it is (appendix A).
Ask the user what to do with it, then reuse the existing steps.txt and jump to the first step after 4 that is not done, or archive the files and write a fresh steps.txt covering step 5 onward, or stop and leave everything untouched. Write the fresh tracker without asking only when the directory holds no run files of its own (appendix A).
Run the lookup or install for the ori binary yourself (appendix B).
If it is still missing, run the ~/.local/bin/ori fallback yourself, and stop if that fails too.
Run ori auth yourself, read its output, and branch on the exit status and message: continue when access resolves, stop with login instructions when no credential resolves, and stop with instructions to use 0.3.0 or newer when the command is unknown because the installed Ori predates the headless run support this skill requires (appendix B).
Check for bun yourself, and stop if it is missing.
Read the eval surface yourself, continuing even if the commands error (appendix B).
Tell the user where the binary landed, if you installed it.
Tell the user what the run will do, from what you read in step 9.
Tell the user it takes 10 to 30 minutes and can spend more than the credit on their key.
Tell the user they get a scored table and that a question can restart the run.
Write the task prompt file (appendix C).
Start one run from the repo root and capture its answer and error files (appendix D).
Wait for the run to exit, then read the answer file (appendix E).
Show the user Ori's narration lines from the answer file in plain language.
If the completed answer contains a tagged question anywhere or its assistant text, above the summary line, ends on an untagged question, continue to step 19; relay only the first question, report a broken one-question contract if another appears, and report an untagged question as a violation while still relaying it (appendix E).
Print Ori's full context for the first question in an ordinary message, including any table. Do nothing else in this step (appendix E).
Update steps.txt here, between the context message and the question, recording what was actually printed: quote the table header row that was printed when the answer carried a table, or record that it carried no table (appendix E).
Ask the user with your own question UI, preserving the three short options and free-text Other. Keep the question to one short sentence and do not restate or summarize the context table (appendix E).
Append the question and the user's own reply to the task prompt file, or, when the reply does not answer the question, append nothing, ask the same question again through the question UI, and do this step again on the next reply (appendix E).
Restart over the whole prompt file with the next attempt number and return to step 16, but only when step 22 appended an answer (appendix E).
Relay the result table, the ship or no-ship decision, and the quoted failures.
Relay Ori's cost and timing table in full (appendix F).
Add each attempt's own duration and cost from the summary line at the end of its answer file (appendix F).
Add one line on the cheaper cost of a re-run.
Tell the user where Ori left the temporary workspace and that it is throwaway.
Say they can move the eval into their repo if the numbers made them want to keep it.
Rules
These hold for the whole run.
Never write the eval yourself and never delegate it to your own subagent. Ori's create-eval skill runs automatically inside the run.
Always pass --model <RUN_MODEL> and never pass --harness. The pin is what makes every coding agent run the same bench. Never substitute another slug and never leave the flag off, because either one hands the run to whatever default the user's install happens to carry.
Always pass --prompt-file. The -p flag works but never use it here, because a one-time string cannot carry state across a restart, and a bare positional prompt is rejected outright.
Run every command in steps 5 to 9 yourself. Installing the binary when it is missing is expected. The credential check is the only setup handoff.
Never resume and never clear a previous run on your own judgement. The skill is loaded and run inside one session, so anything already in the directory came from a different one, and it is the only record of work the user paid for. Every path out of step 4 needs their answer first, apart from the fresh start on a directory that holds no run files of its own.
Update steps.txt as you go: mark a step current before you do it and done before you start the next, and reread the file to decide what comes next instead of trusting memory. A restart replays the prompt file from the top, so this is the only record of how far the last attempt got.
Mark a step whose action is a message to the user done only after that message exists in the conversation. Reading the content in a tool result is not showing it.
Run one Ori process at a time, never one per candidate model. ori eval is what compares models.
Do not tell the user internal pass labels such as eval run pass 3. Use plain progress language instead.
Treat the run directory's task.txt as the only task prompt state. Append every later message to it, resend the whole file on every restart, never use --session, and keep one answer file and one error log per attempt.
Never ask the user what to eval before the run. Ori's interview covers the surface, success criteria, real data, cost limit, and baseline model. Pass a vague or empty request through unchanged.
Never answer Ori's question on the user's behalf. If you cannot reach the user, stop and wait. A guessed target produces an invalid eval that looks correct.
Append only the user's reply to the question currently open. A clarification request or complaint is not an answer, so append nothing, respond to what the user actually said, and ask that same question again through the question UI. A later reply answers the question open at that time and never backfills an earlier gate.
Do not invent an approval gate before starting the run. Steps 12 and 13 disclose the time and cost, and the only user pauses are the run directory choice in step 4 and the questions handled by step 21.
Each turn is silent from start to finish. Say that plainly before starting it. Do not report phase banners as milestones because they arrive only when the turn ends.
Never invent a number. Every attempt reports its own duration and cost on the summary line that ends its answer file, and the eval's own model calls come from Ori's closing table. Name a figure unmeasured only when the attempt wrote no summary line at all.
Never name a winner unless the production model is in the table. "No change" is a valid result.
Never give model ids or prices from memory. Check live prices on OpenRouter. The pins are the one exception, because this skill fixes them.
The setup check must confirm that OpenRouter access resolves through ori auth. Tell the user to run ori login when it does not; never tell the user to export a raw key. An already inherited key may satisfy the check. An unknown ori auth command means the installed Ori predates the headless run support this skill requires, so tell the user they need 0.3.0 or newer and stop.
Never paste step 9's output to the user. You read it, they did not ask for it.
Never print a secret value from credentials.json, a .env file, or a config file. Name the key and its location only, such as OPENAI_API_KEY at .env:4.
Never write the eval into the user's repository. It is a throwaway measuring instrument, not something they asked to keep, and the decision to keep it is theirs to make after they see the numbers.
Never put the eval inside the repo's own test framework. ori eval finds *.eval.ts files only, so a pytest, vitest, or Go test file silently never runs.
Never present raw API calls as an Ori eval. If you measure another way, label it clearly.
Never show the user this skill's vocabulary, including "pre-run", "spawn", "verbatim", and "harness".
Never write "bakeoff" to the user. Ori's eval docs and its narration still use the word, so replace it with "model comparison" every time you relay, quote, or summarise them, and in your own status lines and command labels.
Write each status update in plain language. Do not show attempt numbers, file paths, process IDs, command flags, or exit codes to the user. This rule also applies when a run fails. Appendix E gives examples.
Ori's interview has seven tags in this order: [surface], [workspace-files], [workspace-data], [criteria-priority], [evaluation-constraint], [candidates], and [next-step]. [surface] is conditional when the scan finds more than one call site. [workspace-files] is conditional only when the scan finds no model call site and no material to mine. The two conditional questions are mutually exclusive. The other five are always asked, so there are five questions at minimum and six at most.
Relay one question per turn. Preserve each question's three concrete options one for one and render Other as free text. If Ori emits two questions in one turn, relay only the first and report the one-question contract violation.
Ask every question through the question UI, including after an interruption or an out-of-band exchange. Ordinary prose that asks a question does not satisfy step 21.
Print the full context in an ordinary message, including any table, before asking. This gives the user a readable table in the transcript that remains there after the answer. Update the step tracker between the context message and the question. This applies to every question, including [next-step]. Print the full results and cost tables above the question rather than compressing them into it. Never put a markdown table inside a question body because the body renders plain wrapped text and turns a table into raw pipes. Appendix E gives the single placement.
Never copy CLI details into this skill or into text for the user. Re-read what step 9 printed for run options, reports, baselines, timeouts, and the eval-file API, because the CLI changes and copies go stale.
Appendix A: run directory and step tracker
What you need is one scratch directory outside the user's repository whose name is fixed by the repository being evaluated. Two properties matter. The same repository must always resolve to the same directory, including when the run is started from a subdirectory, so derive the name from the absolute path of the repository root and fall back to the working directory when there is no repository. Two different repositories must never resolve to the same directory, so the name varies with that path, and whatever produces it has to work on Linux and macOS alike, because a step that quietly produces nothing on one of them collapses every repository into a single directory. Work the name out again in each shell that needs it rather than trusting a variable to survive, because shell state usually does not persist between commands.
The name has to be reproducible to the byte, because a later run of this skill finds the earlier one by arriving at the same path rather than by searching. So it is /tmp/spawn-ori-eval-<hash>, where the hash is the first twelve characters of the hexadecimal SHA-256 of exactly the path that identifies the tree, which is the repository root or the working directory when there is no repository, with no trailing newline and nothing else fed in. Only that value is pinned, not how you compute it, but it is worth checking that whatever you reach for hashes those bytes and no others, since a trailing newline produces a different directory and hides an earlier run. /tmp rather than whatever the environment calls the temporary directory, since that varies between machines and would hide a run from the next session on the same one.
Every file the outer run produces lives there and nowhere else: steps.txt, task.txt, and each attempt's answer-<n>.txt and error-<n>.log. The ori/ subdirectory is reserved for Ori's tracker and every file the run writes itself. The scratch workspace is wherever create-eval's own surface puts it, so record the path Ori reports in ori/. The directory is per repository, so two repos evaluated on one machine never read each other's prompt or answer.
steps.txt carries the user's request on its first line and then one line for each step from 5 onward, each marked todo, current, or done. Steps 1 to 4 are not tracked, because they are what produce the file.
request: which model should we use for the support triage agent
5 done look up the ori binary
6 done fallback lookup not needed
...
15 current start one run
16 todo wait for it to exit and read the answer file
A directory with no run files of its own needs no question, because there is nothing to decide about. That means it is empty apart from previous/ and has no steps.txt, task.txt, attempt files, or ori/ subdirectory. This covers a directory that is empty and one that holds only an earlier archive, since archiving leaves previous/ behind for good. Everything else goes to the user, whatever it holds. A tracker started for a different request, or one whose every step is done, is a reason to tell the user what they are looking at rather than a licence to clear it, because the answer files are the run they paid for and they may want to read them before anything moves.
So read the directory and report it. Give the user what they need to decide without opening it themselves: the request the old tracker was started for, the step it stopped at in plain language, how many attempts ran, and how long ago the last one wrote anything. Then ask with the operator's own question UI and wait.
Offer resuming only when the old tracker's first line matches the request you are working on and a step is unfinished. Resuming anything else would resend another request's prompt file to Ori. Starting a new run and stopping are always available, so the question has three answers at most and two at least.
Resuming reuses what is there as it stands: mark up the same steps.txt, append to the same task.txt, keep every earlier attempt in the cost table, and keep the ori/ directory with its recorded scratch workspace path, because the user already paid for that work. Starting a new run archives the tracker, the prompt file, every answer and error log, and the ori/ directory, which drops the answers the user already gave Ori and pays for the repo exploration again. Stopping changes nothing and ends the task there, which is what the user wants when they would rather read the old files before anything moves. Say which one you are recommending and why, and let them decide.
One thing to settle before resuming: step 15 or 16 left marked current means an attempt was started and its outcome is unknown. Establish whether that process is still running before starting another, and wait for it if it is, because two runs against the same prompt file break the one-process rule. Any other step left current was interrupted rather than started, including step 22, which stays current while it waits for a reply that answers the question.
Archiving means the old run's files, including ori/, end up under previous/ inside the run directory, in their own subdirectory named after the time they were moved, and nothing is deleted. Two things go wrong without the timestamp: a later archive overwrites an earlier one, and the archive directory gets moved inside itself. So move the run's own files and leave previous/ where it is.
Appendix B: setup commands
Install the binary with curl -fsSL https://openrouter.ai/labs/ori/install.sh | bash. It lands in ~/.local/bin, which is frequently absent from PATH in a non-login shell, so try ~/.local/bin/ori --version before reporting a failure. Bun is required because Ori executes *.eval.ts with it.
Read the eval surface with ori eval -h and ori eval skill, falling back to ori skills get create-eval if the second errors. This is what Ori itself follows inside the run, so it tells you what the run will do and which questions it will ask. It never blocks the task: if both commands error, carry on.
Run ori auth before starting. It resolves the credential the CLI will use, including an inherited environment key, and exits zero when access is available. Read its output to distinguish an unknown command from a missing credential, but do not show that output to the user or repeat any credential value. If it exits non-zero because no credential resolves, tell the user to run ori login and stop. If the binary reports that auth is an unknown command, tell the user that the installed Ori predates the headless run support this skill requires, that they need 0.3.0 or newer, and stop.
Appendix C: task prompt template
Write this to task.txt in the run directory, filling in every angle-bracket field, taking <JUDGE_MODEL> from the pins.
Pass the run directory into the prompt so the outer run can archive every
file it owns and still start the next session cleanly.
Use the create-eval skill. Follow its five phases in this order: workspace
context, criteria and narrowing, model comparison, routing, close. There are
seven possible question tags in this order: `[surface]`, `[workspace-files]`,
`[workspace-data]`, `[criteria-priority]`, `[evaluation-constraint]`,
`[candidates]`, and `[next-step]`. The first two are mutually exclusive
conditional questions, so each run asks five or six questions.
Ask exactly one question per turn and end the turn after asking it. Give each
question three concrete options plus a free-text `Other` option. Never combine
questions in one turn.
Judge with <JUDGE_MODEL> rather than the SDK's default judge model: pass it
to setupJudge as its own agent.
User request: <verbatim request>
Repo context pointers: <paths>. Read these first.
The Ori directory is <absolute run directory>/ori. Create it if it is absent.
Keep the step tracker and every file you write outside the scratch workspace
under that directory. Record the scratch workspace path you report in the
tracker directory. Do not derive another tracker path, and do not adopt or
resume tracker state outside it. The scratch workspace path you report is the
only other location this run may use for its eval and supporting files. Any
other tracker or state files outside these locations belong to a different
session. Keep the eval and supporting files in that scratch workspace outside
the user's repository. Run the eval with ori eval. Do not create or modify
anything in the user's repository.
Appendix D: starting a run
What you want at the end of this step is one ori code process, started from the repository root on the pinned model with the whole prompt file passed to it, whose assistant text and whose diagnostics have landed in two separate files in the run directory under the same attempt number.
The attempt number is the first one not already used, never a fixed one, because the cost table is read back out of every attempt's files and an overwritten answer takes an attempt's cost with it. Keeping the two streams apart matters for the same reason: only the answer stream carries the assistant text and the summary line the cost table needs, and diagnostics mixed into it would corrupt both. Note which attempt number this run is using, since every later step refers to that attempt's files.
Run it in the foreground. If the operator's shell calls are cut off before a run ends, background it instead and poll it with the operator's own process-management tools.
Appendix E: answer shape
The current run's answer file is answer-<n>.txt in the run directory. The process writes the complete assistant answer when the turn settles, so read the answer file after it exits. Diagnostics go to the error log. There is no live progress source or question detection during the turn.
The last line of the answer file is Ori's own summary line rather than part of the answer, and it looks like this.
summary model=served/model duration=200ms input=100 tok output=25 tok context=90 tok $0.012346
Every attempt writes it, including one that stopped at a question, so it is where that attempt's duration and cost come from. A turn that died before finishing writes none. Read the question from the assistant text above it, since the summary line is what actually ends the file.
The assistant text above the summary line contains Ori's narration: short plain lines that tell what it did, for example "Now scanning where this repo calls models, a few minutes." Show these lines to the user when the turn ends, before the question or the result. They tell the user what happened during the silence. If there are many lines, give a short summary. Keep Ori's own words when they are already plain.
A restart is not a retry. Ori cannot hold a live conversation, so each answer starts a new session that re-reads the full prompt file and continues. Never show the words "restart" or "attempt" to the user, because they suggest a failure that did not happen.
Status updates the user sees must stay plain. Examples:
Do not say
Say
Attempt 2 exited with a tagged question; see answer-2.txt.
Ori read your repo and has a question before it continues.
eval run pass 3 completed.
Ori has finished reading. I am checking what it came back with.
Restarting ori code --prompt-file with n=3.
I sent your answer to Ori. It reads the project and the full story again, then continues from where it stopped. About 15 minutes.
Background command "Restart Ori run as attempt 4 with priority answer" completed (exit code 0)
Ori has finished reading. I am checking what it came back with.
Process 48210 is running; error-1.log is empty.
The run continues. Silence is normal here.
The run wrote no summary line.
The run stopped before it could report its time and cost.
The bakeoff is launched.
The model comparison is running.
A finished turn ends its assistant text either on a question or on the final report. Find question tags anywhere in the completed answer text, and treat any narration after the first question as noise rather than evidence that the turn continued past it. Relay only the first question when a turn contains more than one, report the one-question contract violation, then append and restart under the rule below. An untagged question at the end of the assistant text is also relayed and handled the same way, with the contract violation reported alongside it. Show the first question and its three options to the user, ask with the operator's own question UI, keep Other as free text, and append the question and the user's own answer to task.txt only when the reply answers it. A reply that asks for clarification or otherwise does not answer is not an answer, so append nothing, respond to what the user said, ask the same question again, and wait rather than restarting, because a restart on an unchanged prompt file pays to re-read the repo and arrives back at the same question. Do not answer the question yourself. If Ori answered its own scoping question instead, discard that attempt rather than relaying it as a result, ask the user, append the answer, and restart. Every question still requires the question UI after an interruption or out-of-band exchange; an ordinary prose question does not satisfy the step.
A question is not only its labels. It carries context the labels do not, such as the markdown table of surface and current model, and the user must see that context at answer time. Print the narration and the full context in an ordinary message, including any table, then update the step tracker before asking in the same turn. Mark the context step done only after its message exists in the conversation, and record the table header row that was actually printed or an explicit note that no table was present. The printed table stays readable in the transcript after the answer. This applies to every question, including [next-step]. Print the full results and cost tables above the question rather than compressing them into it.
Ask with a minimal question UI that carries only one short sentence, the three short option labels, and free-text Other. Never put a markdown table in the question body. It renders plain wrapped text, so a table there turns into raw pipes and is unreadable. Keep the three options one for one, keep Other as free text, and translate the wording into simple language.
What you append afterwards is the question's full text in plain language plus the single answer string, including the typed text when the user chose Other.
Appendix F: cost and timing table
Include one row for every attempt, including each attempt that ended at a question, since a restart repeats repo exploration. Each attempt's duration and cost come from the summary line at the end of its answer file, and the eval's own model calls and judging come from Ori's closing table in the final answer. Start times are the operator's observation, because the summary line reports duration only. An attempt whose answer file has no summary line reported nothing, so mark it "unmeasured", which is not zero, and report the total as a floor whenever any row is unmeasured. Label the rows in plain language, because the table goes to the user and the plain-language rule holds here too.
Step
Start
Duration
Cost
Reading the project, stopped to ask you a question
observed 20:29
39s
$0.42
Reading the project again after your answer
observed 20:30
15m 10s
$3.20
Eval model calls
20:46
2m
$0.46
Judging
20:48
1m
$0.05
…
Total
$4.13
Follow it with one line, for example: the run cost $4.13 in total, and a rerun costs only the amount shown in Ori's closing table.
Appendix G: troubleshooting
Symptom
Do this
The run directory holds run files already
Report what is in it and ask the user. Never continue it and never clear it on your own.
ori: command not found after a good installation
Run ~/.local/bin/ori. The installer's PATH change does not apply to the current shell.
The credential is missing
Stop. Tell the user to run ori login.
A long pause on the first run
The first run creates ~/.ori/global and downloads templates. It takes about 30 seconds and is not a stopped run.
Ori does nothing and the prompt looks empty
The path in the start command does not match the file you wrote.
Ori reports that a model id is not available
Tell Ori to find the id again. Do not supply one from memory.
The eval file is inside the user's repository
Move it and its supporting files to a temporary workspace and run ori eval on the new path.
Ori resumed a tracker of its own from an earlier session
Discard that attempt rather than relaying it. Tell the user plainly that Ori resumed state from an earlier session, and ask before anything moves. When they agree, move only the leftover ori/ state under previous/<timestamp>/, leave the current tracker, prompt file, and cost table intact, then restart from the full prompt file with the next attempt number.
Ori picked the target itself
Discard the attempt rather than accepting the guessed target. Ask the user, append the answer, and restart from the full prompt file.
The answer has no tagged question but the run looks stopped
Read the final answer. Relay an untagged question, report the contract violation, and carry on from step 19.
The user says they cannot read the table
Print the context again in an ordinary message. Never put the markdown table inside the question body, because it renders as raw pipes.
ori code refuses a redirected run because it needs a terminal
Stop. The attempt spent nothing, and the run cannot proceed on this binary. Tell the user to use Ori 0.3.0 or newer.
403 Key limit exceeded or a 402 payment error
The key is at its spend limit. See below.
A run that dies within seconds on a key limit or payment error is not a defect in Ori or in the eval. Tell the user plainly that the key has no credit, no eval was written, and the attempt spent nothing. Give them the exact Manage it using <url> link from the error and ask whether to raise the limit or add credits. The dashboard change is enough, since the credential stays valid and a new ori login is not needed. When they confirm, start the same run again and continue the task from the same point, since the error is a recoverable pause rather than a terminal failure.
