# Self Attempt 02 > Credit Office Committee JSON Skill - Skill: `prism-shadow/self-attempt-02-6` (Agent Skill) - Install (CLI): `npx skillmds@latest add prism-shadow/self-attempt-02-6` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/self-attempt-02-6/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: prism-shadow (https://skillmd.com/u/prism-shadow) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/prism-shadow/self-attempt-02-6 --- # Credit Office Committee JSON Skill Use this skill for Credit Office / lending committee tasks that ask for a JSON answer over the public credit office API. ## Ground Rules - Use only the task prompt, its `input/payloads/answer_template.json`, and the public API described by `environment_access.md`. - Do not inspect local environment source/data, train outputs, test files, notes, prior runs, reports, other attempts, or judge endpoints. - Prefer the remote public API directly. In this workspace the base URL is ``. - Return exactly one valid JSON object. Do not add prose around it. - Treat `answer_template.json` as the contract: required keys, enum spellings, ordering, and precision rules override intuition. ## API Workflow 1. Read the prompt for target ids, review/as-of date, benchmark family, and requested analysis. 2. Read the answer template and list every required key, enum, sort order, and rounding instruction. 3. Fetch `/api/manifest` and `/api/policies` first to confirm policy and benchmark versions. 4. Fetch only the public records needed: - Branch base data: `/api/branches/{branch_id}` - Metrics: `/api/branches/{branch_id}/metrics` - Loans: `/api/branches/{branch_id}/loans` - Sector exposure: `/api/branches/{branch_id}/sector-exposures` - Applications: `/api/branches/{branch_id}/applications` - FDIC benchmark: `/api/benchmarks/fdic/q4-2024` - NCUA benchmark: `/api/benchmarks/ncua/q1-2025` - Credit-union segment: `/api/credit-union-segments/{segment_id}` 5. Compute with a script or structured JSON tooling, then validate the final object against the template before answering. ## Field Conventions - Loan exposure is `outstanding_balance`. - Application exposure/capacity use is usually `requested_amount`, unless the decision explicitly reduces or participates the amount. - Branch capacity is `lending_capacity_q1`. - Branch sector limits come from `sector-exposures.limit_pct`; fall back to `branches.sector_ceiling_pct` only if no sector-specific row exists. - CRE policy limit is `branches.cre_policy_limit_pct`. - Metrics rows use `quarter`, not an `as_of_date`; select the row matching the review quarter. - In metrics, map `nonperforming_loans` to NPA exposure and `total_loans_outstanding` to total loans. - Ratios are decimals, not percentages. Basis points are `ratio * 10000`. - Currency is rounded to 2 decimals. Ratio fields usually round to 4 decimals. DSCR fields usually round to 2 decimals. Weighted CRE score rounds to 1 decimal. ## Rating Migration Tasks For regrading loans, filter the exact population requested, commonly loans with `current_rating >= target_min`. Apply the policy `risk_rating` dominant-factor rule: final rating is the worst numeric rating from available DSCR, LTV/collateral, and delinquency factors. - DSCR rating: `>=1.50 -> 3`, `>=1.25 -> 4`, `>=1.05 -> 5`, `>=1.00 -> 6`, `<1.00 -> 7`. - LTV rating: `<=0.65 -> 3`, `<=0.75 -> 4`, `<=0.85 -> 5`, `<=1.00 -> 6`, `>1.00 -> 7`. - Delinquency minimum: `30 Days Past Due -> 4`, `60 Days Past Due -> 5`, `90+ Days Past Due -> 7`, `Nonaccrual -> 8`, `Current -> no floor`. - Material downgrade is `final_rating - current_rating >= material_downgrade_notches`. Aggregate exposure by final rating, sort ascending by rating, and sort loan ids ascending where requested. For NPA benchmark variance, use: - `branch_npa_ratio = nonperforming_loans / total_loans_outstanding` - `variance_ratio = branch_ratio - fdic_benchmark_ratio` - `variance_bps = variance_ratio * 10000` Choose problem-credit and watch-list actions from the template enums only. Tie severity to final rating, payment status, projected loss/collateral weakness, and exposure. ## Watch-List Stress Tasks Adverse-rated watch lists usually mean loans with `current_rating >= 6` unless the prompt gives another threshold. For CDFI factor scoring, sum the available policy scores for `fico`, `ltv`, `liquidity_months`, and `debt_to_asset`. - Classes: `Prime` 0-5, `Desirable` 6-9, `Satisfactory` 10-13, `Watch` 14-18, `Doubtful` 19+, and `Projected Loss` when score is 19+ and `ltv > 1.0`. - Use only the risk class enum in the template. - If DSCR is missing, omit that loan from DSCR stress results but keep it in population and workout aggregates. - Watch-list stress formula: `stressed_dscr = dscr / (1 + 0.18)`. - Breach threshold is the policy `coverage_breach_threshold`, normally `1.00`. Workout queues are typically sorted by descending exposure, then ascending loan id. Severe bucket counts group by `current_rating` and `payment_status`, sorted as the template requires. ## Application Allocation Tasks For pending-application packages: - Fetch branch, sector exposures, and all pending applications for the target branch. - Evaluate application-level policy issues: DSCR, LTV, FICO, recent bankruptcy, startup risk, documentation, SBA guaranty, capacity, and sector concentration. - Keep decisions and conditions to template enums exactly. - Capacity math: - `gross_approved_amount` is total approved or conditionally approved face amount. - `committed_capacity_amount` is the bank-retained amount after reductions or participation. - `remaining_capacity = lending_capacity_q1 - committed_capacity_amount`. - Sector post-approval concentration: - `exposure_after_approval = current_exposure + bank_capacity_used for that sector` - `post_approval_pct = exposure_after_approval / total_assets` - compare to the sector-specific `limit_pct`. - Existing over-ceiling exposure can be grandfathered, but a new approval should not worsen it without mitigation. - Sort decisions by `application_id`, concentration flags by sector then `application_id`, post-approval concentrations by sector, and reason-code lists alphabetically. ## Competing CRE Decision Tasks When comparing named CRE applications, filter the application list to the exact ids in the prompt. - Use policy `cre_weighted_score.weights`: capacity `.45`, capital `.03`, character `.05`, collateral/exposure `.36`, conditions `.11`. - Lower weighted score is better. Classify by policy: `approve_quality <= 2.0`, `conditional <= 3.0`, `weak > 3.0`. - If component scores are not explicit fields, derive them transparently from objective application factors: DSCR for capacity, debt-to-assets/net income for capital, relationship/delinquency/bankruptcy for character, LTV/collateral for collateral exposure, and documentation/concentration/benchmark environment for conditions. - CRE stress formula: `stressed_dscr = dscr * 0.85 / (1 + 0.18)`. - Use the policy coverage breach threshold, normally `1.00`. - CRE concentration after selecting an application: - existing CRE exposure comes from existing CRE/CRE-like sector exposure as requested by the prompt. - post-approval concentration adds the selected bank-retained amount and divides by `total_assets`. - policy variance bps is `(post_approval_concentration - cre_policy_limit_pct) * 10000`. - FDIC 30-89 or noncurrent benchmark variance uses `branch_ratio - benchmark_ratio`; keep the exact metric enum from the template. ## Credit-Union Segment Posture Tasks For segment posture pages: - Fetch the segment endpoint, policies, manifest, and NCUA benchmark table. - Use the segment `state_code`, `peer_states`, `quarterly_capacity`, `current_outstanding`, `risk_tolerance`, `minimum_checklist`, notes, and `internal_context`. - State metrics come directly from the NCUA row for the segment state; do not convert bps or integer percentages unless the template says to. - Peer comparison directions are relative to US and to the median of named peer states. Return only `higher`, `lower`, or `equal`. - Required checklist gates should include the segment minimum checklist, restricted to template choices. - Added operating controls should respond to the actual context: insurance/lien gaps, elevated delinquency, capacity exceptions, staffing/second review, or benchmark monitoring. - Escalation triggers must use allowed trigger ids/conditions/owners only and be sorted ascending by `trigger_id`. - Match posture and interpretation enums to the evidence: capacity availability, external risk status, and risk tolerance. ## Final QA Checklist - All required top-level keys are present and no narrative text is outside the JSON. - Every enum value is copied exactly from the template. - Every list follows the requested ordering. - Currency, ratios, DSCRs, scores, and bps use the requested precision. - Benchmark versions and policy versions come from the API, not memory. - Denominators are correct: total assets for concentration, total loans outstanding for NPA ratios, and capacity for allocation. - Nulls, missing DSCR values, and missing FICO values are handled deliberately rather than coerced to zero. - The answer contains no training-output values or test-answer leakage.