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Get Started Free →Assess SOC 2 readiness across the Trust Services Criteria and produce a gap remediation plan. Use when asked to prepare for a SOC 2 audit, run a SOC 2 readiness/gap assessment, scope controls, or get audit-ready. Produces a readiness report — scope & criteria, a control-by-control status, a weighted readiness score, prioritised gaps with owners, and the evidence each control needs.
.claude/skills/mohitagw15856-soc2-readiness/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 100% | 0% |
A SOC 2 audit fails on two things: missing controls and missing evidence of controls you actually run. This skill scopes the engagement to the right Trust Services Criteria, assesses each control's status honestly, scores readiness deterministically (so "we're basically ready" becomes a number), and turns the gaps into a prioritised, owned remediation plan with the evidence each control must produce.
Ask for these only if they aren't already provided:
1. Scope — the systems, the in-scope criteria, and explicitly what's out of scope.
2. Control status — a table grouped by criterion; status is met / partial / gap.
| Criterion | Control | Status | Evidence it needs | Owner | |---|---|---|---|---| | Security (CC6) | Access reviews quarterly | partial | Signed access-review records | IT |
3. Readiness score — overall and per-criterion %, from the helper script (so it's consistent, not vibes). State the bar: a readiness assessment isn't a pass, but <~85% means you're not audit-ready.
4. Prioritised gaps — ranked by risk × effort: what to fix first, the owner, and the target date.
5. Evidence plan — for a Type II especially: what evidence must be collected continuously over the period (you can't backfill a quarter of access reviews the week before the audit).
scripts/soc2_score.py (stdlib only) scores readiness from a control list so the number is calculated, not estimated:
bash# controls.json: [{"criterion":"Security","control":"...","status":"met|partial|gap","weight":1}, ...] python3 scripts/soc2_score.py controls.json python3 scripts/soc2_score.py controls.json --json # machine-readable, for chaining
It returns per-criterion and overall readiness (met=1.0, partial=0.5, gap=0) and lists the gaps.
AICPA SOC 2 Trust Services Criteria (Security, Availability, Confidentiality, Processing Integrity, Privacy).
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 44,102 | 47,580 | +8% | 1 | 1 | 0% | 7,937 | 9,217 | +16% | 0 | 0 | — |
case-02 | fail→fail | 40,736 | 52,237 | +28% | 1 | 1 | 0% | 6,384 | 8,355 | +31% | 0 | 0 | — |
case-03 | fail→pass | 46,320 | 27,563 | -40% | 1 | 1 | 0% | 8,309 | 5,993 | -28% | 0 | 0 | — |
case-04 | fail→pass | 31,668 | 22,565 | -29% | 1 | 1 | 0% | 3,255 | 3,932 | +21% | 0 | 0 | — |
case-05 | pass→pass | 41,969 | 47,080 | +12% | 1 | 1 | 0% | 7,726 | 9,141 | +18% | 0 | 0 | — |
case-06 | pass→pass | 21,695 | 26,994 | +24% | 1 | 1 | 0% | 3,139 | 6,260 | +99% | 0 | 0 | — |
case-07 | pass→pass | 6,197 | 20,952 | +238% | 1 | 1 | 0% | 1,306 | 3,296 | +152% | 0 | 0 | — |
case-08 | pass→pass | 21,132 | 12,681 | -40% | 1 | 1 | 0% | 2,224 | 2,735 | +23% | 0 | 0 | — |
case-09 | fail→pass | 22,023 | 16,688 | -24% | 1 | 1 | 0% | 2,205 | 3,568 | +62% | 0 | 0 | — |
case-10 | fail→fail | 11,938 | 19,327 | +62% | 1 | 1 | 0% | 2,072 | 2,768 | +34% | 0 | 0 | — |
case-11 | fail→fail | 12,889 | 11,887 | -8% | 1 | 1 | 0% | 1,787 | 2,860 | +60% | 0 | 0 | — |
case-12 | pass→pass | 17,099 | 15,740 | -8% | 1 | 1 | 0% | 1,928 | 2,397 | +24% | 0 | 0 | — |
case-22 | fail→pass | 8,984 | 12,838 | +43% | 1 | 1 | 0% | 1,590 | 3,183 | +100% | 0 | 0 | — |
case-13 | pass→pass | 11,222 | 14,496 | +29% | 1 | 1 | 0% | 1,805 | 2,343 | +30% | 0 | 0 | — |
case-14 | pass→pass | 25,546 | 27,182 | +6% | 1 | 1 | 0% | 3,819 | 3,757 | -2% | 0 | 0 | — |
case-15 | fail→pass | 62,356 | 37,192 | -40% | 1 | 1 | 0% | 8,228 | 6,627 | -19% | 0 | 0 | — |
case-16 | fail→pass | 14,762 | 10,958 | -26% | 1 | 1 | 0% | 1,766 | 2,749 | +56% | 0 | 0 | — |
case-17 | pass→pass | 20,952 | 11,378 | -46% | 1 | 1 | 0% | 2,249 | 2,424 | +8% | 0 | 0 | — |
case-18 | pass→pass | 12,857 | 7,060 | -45% | 1 | 1 | 0% | 990 | 1,272 | +28% | 0 | 0 | — |
case-19 | pass→pass | 16,882 | 16,780 | -1% | 1 | 1 | 0% | 2,651 | 3,533 | +33% | 0 | 0 | — |
case-20 | pass→pass | 15,707 | 15,892 | +1% | 1 | 1 | 0% | 2,545 | 3,316 | +30% | 0 | 0 | — |
case-21 | pass→pass | 18,345 | 10,349 | -44% | 1 | 1 | 0% | 2,325 | 2,363 | +2% | 0 | 0 | — |
case-23 | pass→pass | 11,955 | 10,046 | -16% | 1 | 1 | 0% | 2,165 | 2,649 | +22% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +30 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.