calibrate
Calibrate skills/role cards for leaks/gaps with recall, precision, and confidence-accuracy checks.
Install
npx skills add https://github.com/Borda/AI-Rig/tree/main/plugins/codex-rig/skills/calibrate
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install borda-ai-rig@llmmart
git clone https://github.com/Borda/AI-Rig.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole borda/ai-rig collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Before asking, read User Questions.
Calibrate
Run calibration for Codex workflow integrity and behavioral scoring.
Input Schema
{
"scope": "skills|agents|routing|all",
"pace": "fast|full",
"mode": "ab-test|apply",
"require_live_routes": false,
"skip_gate": false,
"done_when": "recall and bias scores emitted; proposals written if mode=apply; gate skipped if skip_gate=true"
}
Workflow
Installed plugin runs use --layout plugin --root <consuming-project>. The runner discovers package assets from its own file location under runtime/calibration, skills, roles, shared; --root controls only report output, Git context, read-only classification work. It must not fall back to source checkout or project .codex.
Repository maintainers may use --layout source --root <source-project> to validate source .codex layout. Never mix source agents, sync manifests, or project registration checks into installed-plugin result.
01: Load calibration task set from ../../runtime/calibration/tasks.json
02: Load behavioral cases from ../../runtime/calibration/behavioral-cases.json
03: Load behavioral observations from ../../runtime/calibration/behavioral-observations.jsonl
- Require
source,run_id,observed_at.source=live-*also needs route; campaign/pair IDs; pair/registered role; actual model/effort; recomputable prompt/task-contract SHA-256; task type/scope; input/cached/output tokens; latency; outcome; tool/check failures; normalized cost; pricing reference. Each complete campaign exactly matches case/role/type/scope signatures inlive-ab-tasks.json; substituted task, fixture, gate, prompt input fails.
04: Inspect ../../runtime/calibration/run.py --help, then run plugin layout against the consuming project
Use --require-live-routes only for strict-live gate. Default offline scoring remains fixture-backed and makes no paid model calls.
05: Inspect checks_failed, leaks_found, and behavioral
06: Review behavioral metrics:
recall: expected IDs recovered from known cases.precision: reported IDs matching expected IDs.confidence_accuracy:1 - mean(abs(confidence - per-case F1)).mean_overconfidence: mean positive confidence bias over per-case F1.gate_metrics_raw: unrounded pass/fail values.by_source: recall, precision, confidence calibration by source.observation_freshness: latestobserved_at, missing timestamps, live/fixture counts.live_route_acceptance: matched baseline/candidate classification and isolated tool-use quality, normalized token-efficiency proxy, evidence sufficiency per configured route; not monetary pricing evidence.
07: Classify gaps as blocking or non-blocking
08: Emit measured recommendations for what should be fixed or improved next
- Start with plain-English explanation of whether calibration passed and what any failure means. Then prioritize failed checks/leaks, naming exact check, file or pattern, evidence, next-action owner, and gate that must pass to resume acceptance.
- Behavioral recommendations name metric gap/affected cases when available.
- Separate fixture-only caveats from live-quality claims.
09: Write skill artifacts to .reports/codex/calibrate/<timestamp>/; preserve runner evidence under .reports/codex/calibration/<timestamp>/
10: Write the validated skill-level artifact when this skill wraps the runner
Follow ../../shared/helper-cli-contract.md/authoritative help. Gate intent: ruff lint/format calibration+skills, explicit no-typed-target reason, calibration tests, clean diff. Write CALIBRATE_METADATA, validate calibrate, promote only validated candidate.
Native Contract Checks
Verify configured native surface, not only runner internals.
Skill checks:
- configured skill file exists; frontmatter has unindented
---,name:,description:; required sections exist; artifact path.reports/codex/<skill>/; examples includestatus,checks_run,checks_failed,findings,confidence,artifact_path; no external runner-only metadata/cache. - CLI checks find every local shebang Python/shell entry point in calibration, shared helpers, code-review, offline harness; each executable, fixed-help-roster registered, authoritative
--help. - every skill references
helper-cli-contract.md, not complete local CLI invocations. - source layout compares
../../runtime/calibration/behavioral-cases.jsonversion toHEAD: dirty tree same or exactly one commit-relative version step; installed plugin layout records packaged fixture as immutable.
Role checks:
- installed layout requires every packaged
roles/<role>/ROLE.md; source layout requires each configured source agent. - role-card frontmatter contains role ID, namespaced name, active model, reasoning effort, approval policy, sandbox, and fallback modes; package-manifest skill/role rosters contain every calibrated target.
- normal parent, review model, implementation, runtime, research, data, adversarial, performance, and executable verification use
gpt-6-sol; delegation/docs/CI-CD/web/OSS/static analysis/curation usegpt-6-luna. accepted-route-evidence.jsonbindsactive_assignmentsto every GPT-6 role model/effort pair and direct parent/deep-review routes;active_assignment_basismarks paired quality/cost evidence pending. Preserve archived GPT-5.6 strict failures and reject unsupported GPT-6 quality claims.model_reasoning_effortfollows the agent-effort-policy: normal parent, implementation, verification, performance, static analysis, and web evidence usemedium; deep review, data/research method, challenge, coordination, documentation, CI/CD, OSS, curation, architecture, and security usehigh.xhigh/maxrequire evidenced task escalation.- high-stakes roles use high-capability tier; bounded support may lower-cost tier. No deprecated model string in active config/TOML.
- role has clear trigger/skip/not-for boundaries, evidence ownership, execution constraints, handover, and confidence contracts; sensitive roles retain sandbox, especially read-only security audit; packaged roles require no external runtime path variable.
Usage Notes
- After meaningful agent/skill instruction change, confirm routing/output match stack.
leaks_foundprimary drift;checks_failedmechanical gate.- Behavioral metrics measure supplied observations only.
fixture-selftestvalidates scoring; live Codex quality requires replacing/appending live-prompt observations. - Missing route coverage is
insufficient-evidence, never acceptance;require_live_routes=trueexits nonzero. - Compare thresholds with
gate_metrics_raw, not rounded display. - Paid paired campaigns:
../../runtime/calibration/run_live_ab.py; plans by default, executes only--confirm-paid-run=chatgpt-subscription, verified local ChatGPT subscription login, no API key env, noCI/GITHUB_ACTIONS. An executing campaign applies full networked CLI approval and denial contract in../../shared/native-skill-contract.mdto complete owning command because it spawnscodex exec. The operation-specific brief is:Action and purpose: run confirmed paid paired calibration;External capability: paid ChatGPT subscription execution throughcodex exec;Credential behavior: use verified local ChatGPT subscription login without reading API keys or credentials;Filesystem and worktree effects: write calibration artifacts only to selected run directory;Retry policy and safe denial outcome: stop turn on denial, retain sandboxed planning or offline scoring only. Planning and offline scoring remain sandboxed. - Each live task names canonical role. Plugin layout prepends exact packaged role card to both prompts; source layout preserves project-instruction plus source-agent prompt construction. Tool pairs can accept candidate passing executable gate when successfully invoked baseline fails; infrastructure timeout is never candidate win.
- Archived GPT-5.6 paired evidence and
live-route-policy.jsonkeep their original Sol/Terra quality rule; that legacy campaign must not be used to validate GPT-6 routing. Define a new model-and-effort paired campaign before claiming GPT-6 quality or cost acceptance. - Never claim currency savings from
normalized-token-v1; need dated authoritative model-specific price. - Fixture
versionis committed-history marker: comparegit show HEAD:<path>; dirty tree stays committed or one-next version until commit. - Missing registration/pattern mismatch: inspect named file and expected registration or pattern first; record observed mismatch. Apply smallest evidenced correction only within authorized edit scope, then rerun that failed check before widening. Otherwise ask for exact missing file, scope approval, or owner decision; never offer only "fix configuration and retry".
Fail-Fast Rules
- Missing calibration files => fail.
- Missing configured skill or role file => fail.
- Native skill/role contract mismatch => fail unless result waives.
- Runtime leakage in native skill or role files => fail.
- Behavioral gate below threshold => fail.
- Result artifact missing => fail.
- Behavioral case-set version >1 step from committed version => fail.
require_live_routes=truewith incomplete route pairs => fail.- Live row without strict paired execution schema => fail.
Quality Gates
Required checks:
calibration:../../runtime/calibration/run.py --layout plugin --root <consuming-project>.behavioral-version-policy: compare case-set version toHEAD; avoid meaningless dirty-tree gaps.review: inspect failed patterns, leaks, behavioral gaps, stale fixtures before recommendations.
Conditional checks:
tests: run focused tests when calibration code changes.format: validate JSON and shell syntax when calibration fixtures change.
Calibration Hooks
When calibration expectations change, update together:
../../runtime/calibration/benchmarks.json../../runtime/calibration/behavioral-cases.json../../runtime/calibration/behavioral-observations.jsonl../../runtime/calibration/run.py../../runtime/calibration/live-route-policy.json../../runtime/calibration/live-ab-tasks.json../../runtime/calibration/run_live_ab.py
Behavioral coverage includes networked CLI owning-command approval for paid live execution.
Output Contract
Before writing result candidate, follow ../../shared/final-handoff-contract.md: render and bind final-handoff.json, final.md, and final-handoff.validation.json; after both validators and promotion pass, emit final.md verbatim.
Use ../../shared/quality-gates.md.
Final chat
Final chat follows shared ordered frame. Outcome is pass, fail, or insufficient-evidence. Results has one measured check or metric per row and exactly Check / metric | Result | Evidence | Next action. Apply shared Verification, Remaining, Next steps, Confidence, and supplemental Artifact rules; include runner mode/coverage, every failed/skipped/deferred check, and calibration recovery evidence.
Minimum artifact payload template: result-template.json.
Files (ai-rig)
-
result-template.json 1.9 KB
{ "artifact_path": ".reports/codex/calibrate/<timestamp>/result.json", "artifacts": { "recommendations": ".reports/codex/calibration/<timestamp>/recommendations.md" }, "behavioral": { "overall": { "confidence_accuracy": 0.0, "f1": 0.0, "mean_overconfidence": 0.0, "precision": 0.0, "recall": 0.0 }, "status": "pass|fail" }, "by_source": { "fixture-selftest": { "confidence_accuracy": 0.0, "precision": 0.0, "recall": 0.0 } }, "checks_failed": [], "checks_run": [ "lint", "format", "types", "tests", "review" ], "confidence": 0.0, "findings": { "critical": 0, "high": 0, "low": 0, "medium": 0 }, "follow_up": [ "non-blocking next check" ], "metadata": { "confidence_gap_closures": [], "confidence_gaps": [], "confidence_recovery": { "evidence": [ "objective calibration evidence" ], "final_confidence": 0.0, "initial_confidence": 0.0, "recovery_actions": [ "recovery action" ], "remaining_limits": [ "residual limit" ], "status": "shared-confidence-band-status" }, "final_handoff": { "branch": "standard", "handoff_path": ".reports/codex/calibrate/<timestamp>/final-handoff.json", "handoff_sha256": "sha256", "rendered_path": ".reports/codex/calibrate/<timestamp>/final.md", "rendered_sha256": "sha256", "schema_version": 1, "validation_path": ".reports/codex/calibrate/<timestamp>/final-handoff.validation.json" } }, "observation_freshness": { "fixture_observations": 0, "latest_observed_at": "2026-06-02T00:00:00Z", "live_observations": 0, "missing_observed_at": 0 }, "recommendations": [ "measured fix or improvement recommendation" ], "schema_version": 2, "status": "pass|fail" } -
SKILL.md 11.1 KB
--- name: calibrate description: Calibrate skills/role cards for leaks/gaps with recall, precision, and confidence-accuracy checks. --- > Before asking, read [User Questions](../../shared/codex-user-questions.md). # Calibrate Run calibration for Codex workflow integrity and behavioral scoring. ## Input Schema ```json { "scope": "skills|agents|routing|all", "pace": "fast|full", "mode": "ab-test|apply", "require_live_routes": false, "skip_gate": false, "done_when": "recall and bias scores emitted; proposals written if mode=apply; gate skipped if skip_gate=true" } ``` ## Workflow Installed plugin runs use `--layout plugin --root <consuming-project>`. The runner discovers package assets from its own file location under `runtime/calibration`, `skills`, `roles`, `shared`; `--root` controls only report output, Git context, read-only classification work. It must not fall back to source checkout or project `.codex`. Repository maintainers may use `--layout source --root <source-project>` to validate source `.codex` layout. Never mix source agents, sync manifests, or project registration checks into installed-plugin result. ### 01: Load calibration task set from `../../runtime/calibration/tasks.json` ### 02: Load behavioral cases from `../../runtime/calibration/behavioral-cases.json` ### 03: Load behavioral observations from `../../runtime/calibration/behavioral-observations.jsonl` - Require `source`, `run_id`, `observed_at`. `source=live-*` also needs route; campaign/pair IDs; pair/registered role; actual model/effort; recomputable prompt/task-contract SHA-256; task type/scope; input/cached/output tokens; latency; outcome; tool/check failures; normalized cost; pricing reference. Each complete campaign exactly matches case/role/type/scope signatures in `live-ab-tasks.json`; substituted task, fixture, gate, prompt input fails. ### 04: Inspect `../../runtime/calibration/run.py --help`, then run plugin layout against the consuming project Use `--require-live-routes` only for strict-live gate. Default offline scoring remains fixture-backed and makes no paid model calls. ### 05: Inspect `checks_failed`, `leaks_found`, and `behavioral` ### 06: Review behavioral metrics: - `recall`: expected IDs recovered from known cases. - `precision`: reported IDs matching expected IDs. - `confidence_accuracy`: `1 - mean(abs(confidence - per-case F1))`. - `mean_overconfidence`: mean positive confidence bias over per-case F1. - `gate_metrics_raw`: unrounded pass/fail values. - `by_source`: recall, precision, confidence calibration by source. - `observation_freshness`: latest `observed_at`, missing timestamps, live/fixture counts. - `live_route_acceptance`: matched baseline/candidate classification and isolated tool-use quality, normalized token-efficiency proxy, evidence sufficiency per configured route; not monetary pricing evidence. ### 07: Classify gaps as blocking or non-blocking ### 08: Emit measured recommendations for what should be fixed or improved next - Start with plain-English explanation of whether calibration passed and what any failure means. Then prioritize failed checks/leaks, naming exact check, file or pattern, evidence, next-action owner, and gate that must pass to resume acceptance. - Behavioral recommendations name metric gap/affected cases when available. - Separate fixture-only caveats from live-quality claims. ### 09: Write skill artifacts to `.reports/codex/calibrate/<timestamp>/`; preserve runner evidence under `.reports/codex/calibration/<timestamp>/` ### 10: Write the validated skill-level artifact when this skill wraps the runner Follow `../../shared/helper-cli-contract.md`/authoritative help. Gate intent: ruff lint/format calibration+skills, explicit no-typed-target reason, calibration tests, clean diff. Write `CALIBRATE_METADATA`, validate `calibrate`, promote only validated candidate. ## Native Contract Checks Verify configured native surface, not only runner internals. Skill checks: - configured skill file exists; frontmatter has unindented `---`, `name:`, `description:`; required sections exist; artifact path `.reports/codex/<skill>/`; examples include `status`, `checks_run`, `checks_failed`, `findings`, `confidence`, `artifact_path`; no external runner-only metadata/cache. - CLI checks find every local shebang Python/shell entry point in calibration, shared helpers, code-review, offline harness; each executable, fixed-help-roster registered, authoritative `--help`. - every skill references `helper-cli-contract.md`, not complete local CLI invocations. - source layout compares `../../runtime/calibration/behavioral-cases.json` version to `HEAD`: dirty tree same or exactly one commit-relative version step; installed plugin layout records packaged fixture as immutable. Role checks: - installed layout requires every packaged `roles/<role>/ROLE.md`; source layout requires each configured source agent. - role-card frontmatter contains role ID, namespaced name, active model, reasoning effort, approval policy, sandbox, and fallback modes; package-manifest skill/role rosters contain every calibrated target. - normal parent, review model, implementation, runtime, research, data, adversarial, performance, and executable verification use `gpt-6-sol`; delegation/docs/CI-CD/web/OSS/static analysis/curation use `gpt-6-luna`. - `accepted-route-evidence.json` binds `active_assignments` to every GPT-6 role model/effort pair and direct parent/deep-review routes; `active_assignment_basis` marks paired quality/cost evidence pending. Preserve archived GPT-5.6 strict failures and reject unsupported GPT-6 quality claims. - `model_reasoning_effort` follows the agent-effort-policy: normal parent, implementation, verification, performance, static analysis, and web evidence use `medium`; deep review, data/research method, challenge, coordination, documentation, CI/CD, OSS, curation, architecture, and security use `high`. `xhigh`/`max` require evidenced task escalation. - high-stakes roles use high-capability tier; bounded support may lower-cost tier. No deprecated model string in active config/TOML. - role has clear trigger/skip/not-for boundaries, evidence ownership, execution constraints, handover, and confidence contracts; sensitive roles retain sandbox, especially read-only security audit; packaged roles require no external runtime path variable. ## Usage Notes - After meaningful agent/skill instruction change, confirm routing/output match stack. - `leaks_found` primary drift; `checks_failed` mechanical gate. - Behavioral metrics measure supplied observations only. `fixture-selftest` validates scoring; live Codex quality requires replacing/appending live-prompt observations. - Missing route coverage is `insufficient-evidence`, never acceptance; `require_live_routes=true` exits nonzero. - Compare thresholds with `gate_metrics_raw`, not rounded display. - Paid paired campaigns: `../../runtime/calibration/run_live_ab.py`; plans by default, executes only `--confirm-paid-run=chatgpt-subscription`, verified local ChatGPT subscription login, no API key env, no `CI`/`GITHUB_ACTIONS`. An executing campaign applies full networked CLI approval and denial contract in `../../shared/native-skill-contract.md` to complete owning command because it spawns `codex exec`. The operation-specific brief is: `Action and purpose`: run confirmed paid paired calibration; `External capability`: paid ChatGPT subscription execution through `codex exec`; `Credential behavior`: use verified local ChatGPT subscription login without reading API keys or credentials; `Filesystem and worktree effects`: write calibration artifacts only to selected run directory; `Retry policy and safe denial outcome`: stop turn on denial, retain sandboxed planning or offline scoring only. Planning and offline scoring remain sandboxed. - Each live task names canonical role. Plugin layout prepends exact packaged role card to both prompts; source layout preserves project-instruction plus source-agent prompt construction. Tool pairs can accept candidate passing executable gate when successfully invoked baseline fails; infrastructure timeout is never candidate win. - Archived GPT-5.6 paired evidence and `live-route-policy.json` keep their original Sol/Terra quality rule; that legacy campaign must not be used to validate GPT-6 routing. Define a new model-and-effort paired campaign before claiming GPT-6 quality or cost acceptance. - Never claim currency savings from `normalized-token-v1`; need dated authoritative model-specific price. - Fixture `version` is committed-history marker: compare `git show HEAD:<path>`; dirty tree stays committed or one-next version until commit. - Missing registration/pattern mismatch: inspect named file and expected registration or pattern first; record observed mismatch. Apply smallest evidenced correction only within authorized edit scope, then rerun that failed check before widening. Otherwise ask for exact missing file, scope approval, or owner decision; never offer only "fix configuration and retry". ## Fail-Fast Rules 1. Missing calibration files => fail. 2. Missing configured skill or role file => fail. 3. Native skill/role contract mismatch => fail unless result waives. 4. Runtime leakage in native skill or role files => fail. 5. Behavioral gate below threshold => fail. 6. Result artifact missing => fail. 7. Behavioral case-set version >1 step from committed version => fail. 8. `require_live_routes=true` with incomplete route pairs => fail. 9. Live row without strict paired execution schema => fail. ## Quality Gates Required checks: - `calibration`: `../../runtime/calibration/run.py --layout plugin --root <consuming-project>`. - `behavioral-version-policy`: compare case-set version to `HEAD`; avoid meaningless dirty-tree gaps. - `review`: inspect failed patterns, leaks, behavioral gaps, stale fixtures before recommendations. Conditional checks: - `tests`: run focused tests when calibration code changes. - `format`: validate JSON and shell syntax when calibration fixtures change. ## Calibration Hooks When calibration expectations change, update together: - `../../runtime/calibration/benchmarks.json` - `../../runtime/calibration/behavioral-cases.json` - `../../runtime/calibration/behavioral-observations.jsonl` - `../../runtime/calibration/run.py` - `../../runtime/calibration/live-route-policy.json` - `../../runtime/calibration/live-ab-tasks.json` - `../../runtime/calibration/run_live_ab.py` Behavioral coverage includes networked CLI owning-command approval for paid live execution. ## Output Contract Before writing result candidate, follow `../../shared/final-handoff-contract.md`: render and bind `final-handoff.json`, `final.md`, and `final-handoff.validation.json`; after both validators and promotion pass, emit `final.md` verbatim. Use `../../shared/quality-gates.md`. ### Final chat Final chat follows shared ordered frame. `Outcome` is `pass`, `fail`, or `insufficient-evidence`. `Results` has one measured check or metric per row and exactly `Check / metric | Result | Evidence | Next action`. Apply shared `Verification`, `Remaining`, `Next steps`, `Confidence`, and supplemental `Artifact` rules; include runner mode/coverage, every failed/skipped/deferred check, and calibration recovery evidence. Minimum artifact payload template: `result-template.json`.
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