From agent-almanac
Grades trading cards using PSA, BGS, or CGC standards with observation-first assessment to prevent grade anchoring. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-almanac:grade-tcg-cardThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Assess and grade a trading card following professional grading standards (PSA, BGS, CGC). Uses an observation-first protocol adapted from the `meditate` skill to prevent grade anchoring — the most common grading bias.
Assess and grade a trading card following professional grading standards (PSA, BGS, CGC). Uses an observation-first protocol adapted from the meditate skill to prevent grade anchoring — the most common grading bias.
Adapted from meditate Step 2-3: observe the card without anchoring to expected grade or market value.
Expected: A neutral starting state where the card is assessed purely on physical condition, not market expectations. Grade anchoring (knowing the value before grading) is the #1 source of grading inconsistency.
On failure: If bias feels sticky (a high-value card makes you want to see a 10), write down the bias explicitly. Externalizing it reduces its influence. Proceed only when you can examine the card as a physical object.
Measure the card's print centering on both faces.
PSA Centering Thresholds:
+-------+-------------------+-------------------+
| Grade | Front (max) | Back (max) |
+-------+-------------------+-------------------+
| 10 | 55/45 or better | 75/25 or better |
| 9 | 60/40 or better | 90/10 or better |
| 8 | 65/35 or better | 90/10 or better |
| 7 | 70/30 or better | 90/10 or better |
+-------+-------------------+-------------------+
BGS Centering Subgrade:
+------+-------------------+-------------------+
| Sub | Front (max) | Back (max) |
+------+-------------------+-------------------+
| 10 | 50/50 perfect | 50/50 perfect |
| 9.5 | 55/45 or better | 60/40 or better |
| 9 | 60/40 or better | 65/35 or better |
| 8.5 | 65/35 or better | 70/30 or better |
+------+-------------------+-------------------+
Expected: Numeric centering ratios for both faces with the corresponding grade/subgrade identified. This is the most objective measurement in the grading process.
On failure: If borders are too narrow to measure accurately (full-art cards, borderless prints), note "centering N/A — borderless" and skip to Step 3. Some grading services apply different standards for borderless cards.
Examine the card's surface for defects.
Expected: A detailed surface inventory with each defect located, described, and severity-rated. Factory vs. handling defects distinguished.
On failure: If images are too low-resolution for surface analysis, note the limitation and provide a grade range rather than a point grade. Recommend physical inspection.
Assess the card's edges and corners for wear.
Expected: Per-edge and per-corner condition assessment. The worst individual corner/edge typically limits the overall grade.
On failure: If the card is in a sleeve or toploader that obscures edges, note which areas couldn't be fully assessed.
Combine sub-assessments into the final grade.
Expected: A final grade with confidence level. For BGS, all four subgrades reported. The grade is supported by evidence from Steps 2-4.
On failure: If the assessment is inconclusive (e.g., can't tell if a surface mark is a scratch or dirt), provide a grade range and recommend professional grading. Never assign a confident grade with insufficient data.
build-tcg-deck — Deck building where card condition affects tournament legalitymanage-tcg-collection — Collection management with grade-based valuationmeditate — Source of the observation-without-prejudgment technique adapted for grading bias preventionnpx claudepluginhub pjt222/agent-almanacOrganizes, tracks, and values trading card game collections (Pokemon, MTG, Flesh and Blood, Kayou) with inventory methods, storage best practices, grade-based valuation, and want-list management.
Generates and drills flashcards for black-letter memorization with Leitner-style spacing, per-subject markdown storage, and self-assessment drill mode. Invoke via /flashcards.
Systematically evaluates used car listings to identify red flags, verify pricing against market data, and decide whether to pursue an in-person inspection.