idea-pairwise-match
Skill20 starsChanged 22 days agoMITView on GitHub
Judged pairwise comparison between two research ideas in a campaign. Commit the comparison criteria to disk first (sha256 over the canonicalized list), generate symmetric anchored advocacy statements, collect independent votes from a judge panel drawn from the third-party agent roster (agents.json; at least three distinct model families for a cross-family match, with an honest single-family native subagent degradation when the roster cannot field that floor), and write a pairwise_match_v1 artifact whose outcome enters the belief layer as one observation. Arguments without a literature or computation anchor are discarded and counted, never weighed. Use when a campaign needs a relative-merit signal between two idea nodes beyond their individual posteriors, when choosing which of two competing ideas to advance, or when a newly admitted idea needs a calibrated first comparison.
In this repository34 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- citation-triangulationSkill
- claim-groundingSkill
- claude-cli-runnerSkill
- codex-cli-runnerSkill
- deep-literature-reviewSkill
- derivation-verifySkill
- dsh-cli-runnerSkill
- figure-hygieneSkill
- gemini-cli-runnerSkill
- hep-calcSkill
- hep-mcp-usageSkill
- idea-allocationSkill
- idea-generationSkill
- idea-posteriorSkill
- julia-packageSkill
- julia-perfSkill
- kimi-cli-runnerSkill
- literature-graph-builderSkill
- literature-to-packageSkill
- markdown-hygieneSkill
- numerical-reliability-gateSkill
- opencode-cli-runnerSkill
- paper-reviserSkill
- pdg-lookupSkill
- physics-diagramsSkill
- referee-reviewSkill
- research-harnessSkill
- research-integritySkill
- research-teamSkill
- research-writerSkill
- review-swarmSkill
- zotero-importSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 260
Every judge receives the identical self-contained prompt (commitment, both card summaries, both rebuilt statements) and no file access.
- Very specific: line 285
If at least the floor's worth of families is usable but too many seats fail at run time, run_panel.py exits nonzero and the match is …
- Very specific: line 292
If the roster itself cannot field the floor (fewer USABLE families in the WHOLE roster than policy.cross_family_minimum — for example, no …
- Distinctive: line 423
No short-window rematches: the rematch guard in Step 4 refuses a repeat of an already-matched pair unless a new-evidence rationale is given.
- Distinctive: line 545
All three are standard-library-only Python (3.9 or newer):
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Idea Pairwise Match (criteria-committed, cross-family judged)
Compare exactly two idea nodes of a campaign under criteria that were
committed to disk before any advocacy was drafted and before any judge ran,
with a judge panel drawn from the model families of the third-party agent
roster, and record the result as a pairwise_match_v1 artifact. The outcome
is an observation for the belief layer, never an elimination: the loser keeps
living in the campaign with a lowered worth, and can win a later match when
new evidence arrives.
What a match measures, and what it does not
- The belief layer (skill
idea-posterior, backed by the Gaia argument graph) owns each idea's overall worth posterior, built from literature and computation evidence attached to the idea's claims. - A pairwise match measures only the residual relative merit that per-idea evidence does not settle: with both cards on the table and a fixed set of criteria, which idea is stronger right now.
- One match produces at most one observation (see the fixed mapping table below). It never deletes a node, never sets a posterior directly, and a single match is deliberately capped below the evidence weight that accumulated anchors can reach.
Inputs
Two idea nodes from campaign storage, each with a complete idea card. The fields this protocol consumes:
node_id: engine short id — 8 chars of lowercase Crockford base32, pattern^[0123456789abcdefghjkmnpqrstvwxyz]{8}$(the idea_node_v1 convention).title,gist,status: short descriptive strings; the node should not be archived or parked (a contest is effort spent on it).claims: non-empty array; each claim carries itssupport_type(literature or computation) andevidence_uris.
Claims are assumed to have passed content vetting upstream (the
claim-grounding skill). Judges weigh anchored arguments; they do not fetch
or re-verify sources during a match.
Synthetic demonstration cards live in examples/; they are placeholders for
tests and dry runs, not research content.
Paths
Everything for one match lives under the campaign:
artifacts/matches/match-<match_id>.json: the final artifact.artifacts/matches/match-<match_id>.work/: working materials, referred to as WORK below:commitment.json,card_summary_a.md,card_summary_b.md,statement_a.md,statement_b.md, andpanel/(rendered judge prompt, votes, raw runner receipts,panel_run_report.json).
Mint a fresh engine short id for match_id when creating WORK (8 chars
drawn from the exact alphabet 0123456789abcdefghjkmnpqrstvwxyz), and keep
it for the artifact:
python3 -c "import secrets; print(''.join(secrets.choice('0123456789abcdefghjkmnpqrstvwxyz') for _ in range(8)))"(assemble_match.py mints one the same way when --match-id is omitted,
but WORK is usually named before assembly, so mint up front.)
Protocol
Step 1: commit the criteria, on disk, before anything else
The default criteria library holds five entries, aligned with the worth decomposition used by the belief layer:
- tension resolution: does the idea resolve a standing tension, contradiction, or persistent puzzle rather than restate it?
- downstream reach and breadth of applicability: how much becomes possible or affected if the idea works?
- mechanism insight: does it explain why something happens, not only that it happens?
- testability and timing: can the core claim be probed with means available now or soon?
- verification cost: how much work separates the idea from its first decisive check?
The caller may add or remove criteria, but only before committing:
python3 scripts/commit_criteria.py --out WORK/commitment.json
python3 scripts/commit_criteria.py --out WORK/commitment.json \
--criteria "tension resolution" "verification cost"commit_criteria.py canonicalizes the list (Unicode NFC, whitespace
collapse, duplicate and empty rejection, code-point sort), hashes the
canonical JSON with sha256, and writes {committed_at, criteria,
commitment_hash}. The hash covers only the criteria list, so the same set
always yields the same hash. The script refuses to overwrite an existing
commitment; new criteria mean a new match.
The commitment file is the integrity anchor of the whole match: statements
must open by declaring this hash, the judge prompt embeds the commitment
verbatim, every vote record is stamped with the hash and a collection
timestamp, and assemble_match.py re-verifies all of it and refuses on any
mismatch or on votes collected before committed_at. Committing first is
what stops the criteria from drifting toward whichever side reads better
mid-match.
Step 2: prepare symmetric materials
Card summaries are rendered deterministically (no model in the loop), same template both sides:
python3 scripts/run_panel.py --materials-dir WORK \
--render-card-summaries --card-a cardA.json --card-b cardB.jsonAdvocacy statements are written by two separate host subagents, one per
idea, each blind to the opposing card. Render the two symmetric requests
(same template, same word cap), then hand each request file to its own
subagent and save the replies as WORK/statement_a.md and
WORK/statement_b.md:
python3 scripts/run_panel.py --materials-dir WORK \
--render-statement-prompts --card-a cardA.json --card-b cardB.jsonStatement contract (requested by prompts/statement_prompt.md, and enforced
in code before any judge sees the statement): the first two lines declare the
commitment hash and the node id; every argument line ends with an anchor tag,
[anchor: literature -> reference] or [anchor: computation -> reference],
whose reference comes from the card's evidence entries; no facts beyond the
card; one section per committed criterion plus a closing "Honest weaknesses"
section; at most 600 words (the default --word-cap).
The contract is not merely requested of the author: the statement a judge
reads is REBUILT from verified elements, not passed through verbatim. Before a
statement is substituted into the judge prompt, run_panel.load_materials
NFKC-normalizes it and reconstructs it, keeping only
- headings whose normalized text names a committed criterion or the "Honest weaknesses" section (ATX headings only);
- argument lines that end in a valid anchor tag whose reference cross-matches
the side's own card evidence (the reference must be a card evidence entry, a
requirement
statement_prompt.mdasks for and the rebuild now enforces); - weakness admissions, rebuilt as a bounded list.
Everything else -- a heading that names nothing committed (a would-be
## Required output or ## Binding rules), an argument line whose anchor is
missing, malformed, or points at a reference the card never declared, and any
stray prose outside a committed section -- is simply not rebuilt, so it never
reaches a judge. Two conditions still stop the whole match before any judge
runs: a rebuilt statement over the word cap, and one with no card-anchored
argument at all. The same parser counts the unanchored argument lines it drops
per side (it does not take the author's or a judge's word for the count); those
counts, and a per-judge reconciliation against each judge's self-reported
unanchored_arguments_discarded, are recorded in panel_run_report.json, and
a disagreement is printed as a warning.
This is a rebuild for signal quality, not a security sandbox: see "Scope of the rebuild" below.
Step 3: run the judge panel (roster-drawn, independent)
Judges from a single model family correlate: they share training lineage, blind spots, and failure shapes, so their agreement overstates the evidence. Family diversity is therefore the validity requirement of the normal, cross-family panel — not a preference. The one exception is the degraded single-family form described under "Validity and honest degradation" below, which exists so a roster that cannot field the floor still yields a comparison, and which is stamped as degraded everywhere it is recorded.
The panel's family list, each family's runner, and each family's model string come from the third-party agent roster — an agents.json file, schema version 1. Discovery order, first hit wins:
- an explicit
--roster PATHargument; - the project-level roster:
<project>/.nullius/agents.json, where the project root is the first directory containing.gitfound walking up from the materials directory (docs/AGENTS_FILE.md's definition; the walk starts at the materials directory so the roster follows the campaign the materials belong to). A materials directory outside any git project has no project-level roster. If the walk lands on the same physical file as the user-level roster (a$HOMEthat is itself a git repository, e.g. a dotfiles repo), the file is reported assource="user": the user-level roster is machine-local configuration whatever repository happens to contain$HOME; - the user-level roster
~/.nullius/agents.json; - the built-in pure-native roster: one native seat, recorded under the
neutral family label
host, becauserun_panel.pycannot verify which model family the host actually is and will not guess one. A roster file that declares the real family (for example claude) names it in the records.
A missing discovered file is never an error — the next source applies (an explicit --roster path that does not exist is). A roster file
that exists but does not parse or validate stops the run loudly, naming the
file; silently skipping a broken roster would change panel composition
behind the operator's back. run_panel.py reads the roster with its own
self-contained parser by design; there is no shared roster library.
Each roster family declares its runner (one of native / codex / opencode /
kimi / gemini / claude-cli), its model strings (a models object whose
default entry the panel uses; a family declared available: false may
omit it), and optional notes. Seat execution by runner:
- native: the host family. Its vote is a host subagent's raw reply injected
with
--native-vote FILE;run_panel.pynever spawns a host subagent itself, and a native seat with no injected file is recorded absent. A roster declares at most one native family. The vote records the roster's declared model string for the native family (what the host is expected to run the subagent as);--model-labelpins a different record. - codex / claude-cli: through the review-swarm launcher
(
skills/review-swarm/scripts/bin/run_multi_task.py), which routes each spec to the matching CLI. - opencode / gemini / kimi: directly through each runner script
(
skills/opencode-cli-runner/scripts/run_opencode.sh,skills/gemini-cli-runner/scripts/run_gemini.sh,skills/kimi-cli-runner/scripts/run_kimi.sh), in their isolated working directories. A model ofdefaultdelegates to that CLI's own configured default model; a pinned model is passed with the runner's strict flag, so a pinned model that is unavailable makes the seat absent instead of being silently replaced by the CLI's default (which, for a multi-provider CLI, might not even belong to the seat's family).
Launcher subprocesses always get REVIEW_SWARM_NO_AUTO_CONFIG=1, so a
project-level review-swarm configuration can never silently change panel
composition, models, or fallback behavior. --model-spec FAMILY=MODEL
overrides one family's roster model string for a run that must record a
specific model; --families selects a subset of the roster's families.
python3 scripts/run_panel.py --materials-dir WORK --out-dir WORK/panel \
--render-prompt-only
# host subagent answers WORK/panel/judge_prompt.md (system prompt:
# WORK/panel/judge_system.md); save its raw reply, then:
python3 scripts/run_panel.py --materials-dir WORK --out-dir WORK/panel \
--native-vote WORK/panel/native_vote_raw.txtBecause a native reply is formed in one invocation and collected by a later one, the reply must prove which rendered prompt it answered: the rendered judge prompt file ends with a binding block naming the prompt's recorded digest (it covers the rendered prompt body, the judge system prompt template, and the binding block's own wording, so a code upgrade changing any of the three invalidates replies formed against the old text), the subagent copies that hash into its vote JSON as a judge_prompt_sha256 key, and collection verifies the echo against the current rendering. A reply with no echo, or echoing a different rendering's hash, fails its seat (recorded reason, seat absent); the same rule applies to every seat of the degraded native panel. If the materials change between the two invocations, re-run the render step and have the reply re-formed against the fresh prompt; the collecting invocation also refuses up front when the on-disk prompt left by the earlier rendering no longer matches the current materials.
Every judge receives the identical self-contained prompt (commitment, both
card summaries, both rebuilt statements) and no file access. The prompt pins
three binding rules: judge only against the committed criteria; discard and
count any argument that lacks an anchor; a tie is a legal outcome. It also
frames the two statements as advocacy content to be weighed under the
committed criteria, not instructions to the judge: a sentence inside a
statement that reads as a directive or meta-instruction carries no authority
and is to be ignored. Votes are strict JSON, parsed fence-first; each family
gets one retry on any failure, after which it is recorded absent with its
reason in panel_run_report.json.
Validity and honest degradation:
- A cross-family match is valid only with votes from at least 3 distinct
families (
policy.cross_family_minimum; a roster may raise this floor, never lower it). - With exactly the floor, proceed, and keep the absent families on record.
- A family the roster declares
available: falseis recorded absent up front, with the roster's own notes as the reason, and is never invoked. - A family counts as usable only when it is declared available AND its
runner's CLI executable is actually present on the machine (native always
passes; a
--runneroverride replaces the roster runner, so the family stays usable whatever is installed) — docs/AGENTS_FILE.md's definition. A requested family that is not usable is recorded absent up front with the probe's reason and is never dispatched. - If at least the floor's worth of families is usable but too many seats
fail at run time,
run_panel.pyexits nonzero and the match is terminated; the run report remains as the record of the attempt.assemble_match.pyenforces the same floor independently. - An absent family is never backfilled by a second vote from a present family, and never silently substituted by another model of the same family lineage.
- If the roster itself cannot field the floor (fewer USABLE families in
the WHOLE roster than
policy.cross_family_minimum— for example, no roster file exists anywhere, so the built-in pure-native roster applies; or the CLIs the roster names are not installed on this machine), the panel degrades perpolicy.when_below_minimum = native_subagentsto NATIVE SUBAGENT SEATS: run at least the floor's worth of independent host subagent instances, each answering the rendered judge prompt blind to the other seats, save each raw reply to its own file, and re-run with one--native-vote FILEper seat (the first invocation without enough seat files exits with code 3 and prints exactly this guidance). Such a panel is stampedindependence = "single_family"andindependent_runners = falseinpanel_run_report.json, its vote files carry seat numbers and a sha256 of each seat's raw reply, and the artifact records the same, so a degraded panel can never pass for a cross-family one. The belief layer sees that record and can weight the observation's diversity accordingly. Two guards keep the seats honest: the same reply file given for two seats, or two reply files with byte-identical content, stop the run — independent subagent seats cannot share one reply. A--familiessubset can never force degradation on a roster that could field the floor: such a request just runs a cross-family panel that fails the vote floor. The degraded seats always belong to the roster's native-runner family (which must be available), even when a--familiessubset did not name it; the report records the requested list and the native family side by side. - Distinct family labels are not enough on their own:
run_panel.pyalso checks that the family seats resolve to genuinely different underlying commands. If two or more--runnerseats point at the same command (a single model wearing three labels), a real match is refused. TheIDEA_PAIRWISE_ALLOW_STUB_RUNNERS=1escape hatch exists only for tests and single-model dry runs; when it is used, the run report is stampedindependent_runners = false, so a stub-backed panel can never be mistaken for an independent cross-family one. A--runneroverride whose seat's vote is COUNTED likewise stampsindependent_runners = false(an override command is an arbitrary template that cannot be mechanically compared against roster-resolved seats); an override seat that failed and contributed no vote does not taint the tally the remaining roster-resolved seats produced.
Step 4: tally the votes and write the artifact
python3 scripts/assemble_match.py \
--commitment WORK/commitment.json --votes-dir WORK/panel/votes \
--materials-dir WORK --campaign-dir CAMPAIGN \
--campaign-id CAMPAIGN_ID --idea-a NODE_A_ID --idea-b NODE_B_ID \
--match-id MATCH_IDThe winner is the side with more votes; equal counts make the outcome a tie.
vote_margin is the absolute vote difference divided by the number of votes
cast (tie votes included in the denominator). Before writing anything,
assemble_match.py re-verifies the commitment hash on every vote, the vote
timestamps, and the panel composition against the run report: on a
cross-family panel, family uniqueness and the vote floor (the report's own
min_families, never below three); on a degraded single-family panel,
numbered distinct seats of the one native family. Assembly loads exactly the
vote files the report's votes_collected map names — a vote file in the
directory that the report does not name (a stale seat from an earlier run,
or a foreign file) stops assembly — and refuses a report whose panel_valid
is false. It then validates the assembled artifact field by field and writes
it with observation_write.written = false. It also copies the panel
composition record from panel_run_report.json next to the votes into the
artifact — independent_runners, plus a panel_independence block carrying
the mode (cross_family or single_family), the families that voted, and
the absent families with their reasons — refusing if the report is missing
or incomplete, and refusing a single-family report or artifact that claims
independent_runners = true, so a stub-backed, degraded, or thinned panel
is visible in the artifact itself and cannot dress itself up.
--materials-dir is required, not optional. Assembly cross-checks both
statements' declared hash and node ids against the commitment and the pair
being assembled, and binds a statement_binding block into the artifact: for
each side, the node id and a sha256 over the on-disk statement content. That
content is the sole source from which the judges' rebuilt view is
deterministically derived, so hashing it pins the judge input to an auditable
origin. This makes "the votes correspond to the criteria-bound advocacy
statements" an auditable line in the artifact, not just a check that ran once
and left no trace.
Rematch guard: if the campaign already holds a match for the same unordered
pair, assembly refuses unless --rationale states the new evidence that
justifies rerunning; the rationale is recorded in the artifact. A rerun
produces a second, independent artifact for the pair; this skill does not
deduplicate or supersede the earlier one. Reconciling multiple matches for a
pair (which to weigh, how to age the older observation) is the belief layer's
job (skill idea-posterior), not this skill's.
Step 5: feed the outcome to the belief layer
<!-- shared-mapping-table:start -->Fixed vote-outcome to likelihood-tier mapping, shared verbatim between this skill and the belief-layer skill (idea-posterior); never edit one copy alone.
- Unanimous win: at least 3 valid votes were cast and the losing idea received zero votes. Maps to likelihood tier 10. Individual "tie" votes count toward the valid-vote total and are not votes for the losing idea, so a win with some tie votes but zero opposing votes is still unanimous.
- Split win: a majority winner exists and the losing idea received at least one vote. Maps to likelihood tier 3.
- Tie: equal vote counts for the two ideas. No observation is produced.
Direction is symmetric: one match yields one observation; "the winner's worth rises" and "the loser's worth falls" are the same observation stated two ways, absorbed once, never double-counted.
Why the tiers stop at 10: the tiers are Jeffreys-style Bayes-factor grades (3 / 10 / 30). A single pairwise match is capped at the substantial grade (10) and never earns the strong grade (30), so that one panel's votes cannot overwhelm the literature and computation anchors accumulated in the argument graph. The cap is part of the honesty discipline on evidence weights.
<!-- shared-mapping-table:end -->The absorption itself belongs to the idea-posterior skill (its section on
absorbing pairwise match outcomes): that flow turns the artifact's outcome
into an observation package for the Gaia argument graph, then flips
observation_write.written to true and records the package reference in
observation_write.gaia_package_ref. This skill only writes the artifact
with written = false; if a match artifact still shows written = false,
its outcome has not yet influenced any posterior.
Which pairs to run
The campaign's assignment layer (a selection script or the operating agent) decides who plays. Defaults when nothing else is specified:
- Eligible: nodes that are neither archived nor parked (not waiting_activation / admission_blocked), with a complete idea card.
- Most informative first: among nodes with a non-empty posterior, pair posterior neighbors; adjacent worth means the match carries the most information.
- Cold start: a node with no posterior yet plays the node at the posterior median.
- No short-window rematches: the rematch guard in Step 4 refuses a repeat of an already-matched pair unless a new-evidence rationale is given.
pairwise_match_v1 artifact
Top-level fields (unknown keys are rejected by the validator):
match_id,campaign_id, and the two idea node id fields (idea_a_node_id, idea_b_node_id): engine short ids, exactly^[0123456789abcdefghjkmnpqrstvwxyz]{8}$(the same convention as idea_node_v1 ids); the two node ids must differ.criteria_commitment: exactly{committed_at, criteria, commitment_hash}as written in Step 1; the validator recomputes the hash.panel: array with one entry per vote, each{reviewer_family, model, vote, anchored_arguments, unanchored_arguments_discarded}plus, on a single-family panel only, aseatnumber.reviewer_familyis a lowercase family label from the agent roster; on a cross-family panel no family appears twice and at least 3 distinct families vote; on a single-family panel all entries carry the one native family and distinct seats, at least 3 of them.voteis one of"a","b","tie"; each anchored argument exactly{argument, anchor_type, anchor_ref}withanchor_typeliterature or computation.panel_independence: exactly{mode, families_present, families_absent}, copied frompanel_run_report.jsonat assembly.modeiscross_familyorsingle_family;families_presentlists the families that voted;families_absentlists each requested family that did not vote as{family, reason}. The validator cross-checks this block against the panel array.independent_runners: boolean, read frompanel_run_report.jsonat assembly. It isfalsein three cases: the panel ran under the stub/single-model escape hatch (seats sharing one underlying command), it degraded to native subagent seats, or a--runneroverride seat's vote was counted (an override command's independence cannot be mechanically vouched for, even when it is in fact a distinct backend). Sofalsemeans "independence not mechanically established", not necessarily "seats shared one model"; either way a low-diversity or unvouched panel is visible in the artifact itself and the belief layer can weight the observation's diversity from the record.outcome: exactly{winner, vote_margin, decided_at}; the validator recomputes winner and margin from the panel and rejects disagreement.observation_write:{written}plus optionalgaia_package_ref.rationale(optional): the new-evidence reason recorded for a rematch.
Example:
{
"match_id": "4c9a2d10",
"campaign_id": "3b8e1f70",
"idea_a_node_id": "1f6c9d5e",
"idea_b_node_id": "2a7d0e6f",
"criteria_commitment": {
"committed_at": "2026-07-05T08:00:00+00:00",
"criteria": [
"downstream reach and breadth of applicability",
"mechanism insight",
"tension resolution",
"testability and timing",
"verification cost"
],
"commitment_hash": "sha256:a1e10a3bbabf4338396bbbc361aa9accc504f03221f2b12998b707772854fcd4"
},
"panel": [
{
"reviewer_family": "claude",
"model": "claude/host-subagent",
"vote": "a",
"anchored_arguments": [
{
"argument": "the pilot computation bounds the verification effort",
"anchor_type": "computation",
"anchor_ref": "artifact://campaign/toy/computations/pilot.json"
}
],
"unanchored_arguments_discarded": 0
}
],
"panel_independence": {
"mode": "cross_family",
"families_present": ["claude", "glm", "gpt"],
"families_absent": [
{"family": "kimi", "reason": "runner exit code 1; runner exit code 1"}
]
},
"independent_runners": true,
"outcome": {
"winner": "a",
"vote_margin": 0.25,
"decided_at": "2026-07-05T09:00:00+00:00"
},
"observation_write": {
"written": false
}
}(The example shows one panel entry for brevity; a real valid artifact holds
at least three — from three distinct families in cross-family mode, or three
numbered seats of the one native family in single-family mode. An artifact
assembled with the required --materials-dir also carries a top-level
statement_binding block — per-side node id and a sha256 of the statement
content — omitted here for brevity.)
The authoritative check on an assembled match is validate_pairwise_match in
scripts/assemble_match.py: a standalone, field-by-field check that a match
can be verified against without any package installed. It is the binding
judge of panel validity, including the composition rules a JSON Schema cannot
express directly: a cross-family panel holds at least three DISTINCT
families and no seat numbers; a single-family panel holds at least three
numbered, distinct seats of its one native family and never claims
independent_runners = true; and the panel_independence block must agree
with the panel array. A machine-readable pairwise_match_v1 JSON Schema also
lives with the campaign engine's contracts for downstream consumers; because
a schema cannot require distinctness, its panel array must set
minItems: 3 (not 1) and its description must defer to
validate_pairwise_match as the authoritative composition check, so a
schema-only consumer cannot accept a sub-three-vote artifact that the Python
validator would reject.
Scripts
All three are standard-library-only Python (3.9 or newer):
scripts/commit_criteria.py: canonicalize, hash, and write the criteria commitment. Refuses overwrites.scripts/run_panel.py: verify materials and rebuild each statement from verified elements before any judge runs, render the judge prompt from the rebuilt statements, resolve the agent roster, run the roster's family seats (with the rendering modes shown above) or the degraded native subagent seats, collect and validate votes, writepanel_run_report.json.--runner FAMILY=COMMANDreplaces a family's runner with a command template ({prompt}and{system}expand to the rendered prompt paths; stdout is taken as the judge's raw reply); this hook exists for tests and custom runners, and a real match must use the real families. Pointing several seats at the same command is refused unlessIDEA_PAIRWISE_ALLOW_STUB_RUNNERS=1is set, which stamps the run reportindependent_runners = false.scripts/assemble_match.py: re-verify the integrity thread, tally, map the outcome to the observation tier, validate, and write the artifact. Requires--materials-dirand binds a per-side statement digest into the artifact; copies the panel composition record from the run report.
Tests
python3 -m pytest skills/idea-pairwise-match/tests/The tests cover hash stability, stage-order enforcement (sentinel proof that
no runner executes when materials fail verification), tally and mapping
edges, artifact validation field by field, the rematch guard, and a mocked
end-to-end panel. The agent-roster cases (inline fixtures, no template
dependency) pin roster parsing and validation, the discovery order with the
missing-file pure-native default and the loud error on a broken file, the
floor-met regression path, the below-floor degradation to native subagent
seats with its guidance exit and single-family record, and absence recording
for roster-declared unavailable families. The rebuild cases pin what a judge does and does not see: a
forged judge-prompt heading, an injection tucked into the weaknesses section, a
non-ATX/HTML/homoglyph pseudo-heading, and an argument line whose anchor points
at a reference the card never declared are each dropped from the rebuilt
statement (the panel still runs on the genuine anchored content, and the
injection string is asserted absent from the judge prompt); a statement that
rebuilds to zero card-anchored arguments, or over the word cap, stops the match
before any judge runs; an unanchored flood is counted by the parser and flagged
against the judges' self-reports; a shared --runner command is refused
without the escape hatch, and the resulting independent_runners = false flag
is carried into the artifact; a claimless idea card is rejected on both the
summary and statement paths; and a symmetry test checks that content-identical
statements rebuild to a symmetric judge prompt. Mocked judges appear only in
tests; any real match must collect real cross-family votes or honestly record
the absent families.
Scope of the rebuild
The statement rebuild gives each judge a clean input: anchored, organized by the committed criteria, with only verified content. Its purpose is signal quality — keeping an agent from accidentally writing structure or meta-language that would mislead the panel about the two ideas' relative merit. It is not a security sandbox against deliberate injection, and does not need to be:
- The statements are written by agents inside this pipeline, not submitted by a third party with an adversary's incentive. So the rebuild does NOT chase encoding variants of a forged marker (homoglyph, HTML, setext, and the like) with a blacklist. It works the other way round: only elements that pass a positive check are rebuilt, so a heading that names nothing committed or an anchor that matches no card evidence never appears, whatever form it was written in. Adding a blacklist of variants would be an arms race with no end and is deliberately avoided.
- Each pairwise match yields exactly one capped (tier ≤ 10), non-eliminating observation, and the tier cap keeps a single panel from overwhelming the literature and computation anchors in the argument graph. So even a single contaminated statement has a bounded effect on the belief-layer posterior; this bound, not statement sanitization, is the main source of robustness.
- A rebuilt statement's surviving argument lines are still free text (that is the point of advocacy). The judge-prompt framing — the two statements are advocacy content to weigh, not instructions — handles any directive language that remains inside a legitimate line. That framing is the correct place to stop: the rebuild removes structure and unanchored claims; the framing tells the judge how to read the prose that is left.
Honest limits
- The two advocacy statements are generated by host subagents, so statement authorship shares a family with one judge seat. Statements are inputs (advocacy under a fixed template), not votes; the independence requirement applies to the panel. Noted openly rather than hidden.
- A degraded single-family panel has none of the cross-family diversity this
protocol is built around: its seats are independent instances (separate
contexts, blind to each other) of one host model, so shared blind spots
survive. It exists so a thin roster degrades honestly instead of blocking
all comparison, it is stamped as
single_familyeverywhere (independent_runners = falseincluded), and the belief layer can weight it down from the record. - Judges do not fetch anchor references during a match; anchor content is vetted upstream. A match with unvetted claims measures rhetoric, not merit, and should not be run.
- The
unanchored_arguments_discardedcount stored in each panel entry of the artifact is what that judge self-reported. The mechanism does not depend on it:run_panel.pyparses each statement itself, drops and counts the unanchored argument lines independently, and records that authoritative count (plus a per-judge agreement flag) inpanel_run_report.json. The self-reported number is retained as a cross-check, not as the source of truth. - Idea A and Idea B keep fixed presentation positions; position effects are
not randomized away. If a position effect is suspected, rerun with swapped
labels under the rematch guard with that stated rationale. A symmetry test
(
test_symmetry_identical_content_differs_only_by_node_id) pins that two content-identical statements render a structurally symmetric judge prompt, so the fixed positions add no asymmetry of their own.
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