Claude Skill

calibrate

Calibrate skills/role cards for leaks/gaps with recall, precision, and confidence-accuracy checks.

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Download Borda-AI-Rig-plugins_codex-rig_skills_calibrate-39e3a48.zip · 5 KB
borda/ai-rig 27 4 forks Apache-2.0 Updated 2d ago
Part of borda/ai-rig — 82 skills

Install

skills CLI npx skills add https://github.com/Borda/AI-Rig/tree/main/plugins/codex-rig/skills/calibrate
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install borda-ai-rig@llmmart
Git 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 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.

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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