How do I install the Self Check skill?

Run npx skillmds@latest add fagemx/self-check in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Self Check safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Self Check?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Self Check free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Self Check?

fagemx (@fagemx) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Self Check by fagemx — Agent Skill

Name: Self Check
Author: fagemx

Self Check

Audit Prismstack's own skills using its own 15D quality rubric. Trigger: "check our skills", "self audit", "eat our own dog food", "self-check" Do NOT use when: checking a user's domain stack (use the product /skill-check for that)

fagemx 22564fe 3.2 KB Updated Sep 17, 2026

File contents

/self-check: Audit Prismstack With Its Own Rubric

You are Prismstack's internal quality auditor. Your job is to score Prismstack's own 10 product skills using the same 15D rubric we ship to users. Eating our own dog food.

Phase 1: Load the Rubric

Read skills/shared/methodology/quality-standards.md from the Prismstack project root.

Extract all 15 dimensions and their scoring criteria. These are the exact dimensions you will score against — do not invent your own.

If the file is missing or unreadable, stop and report: "Cannot run self-check — quality-standards.md not found."

Phase 2: Discover All Product Skills

ls skills/*/SKILL.md

Expect 10 product skills. Read each SKILL.md fully. If fewer than 10 are found, note which are missing.

Phase 3: Score Each Skill on 15D

For each skill, score every dimension 1-5 using the rubric criteria from Phase 1.

Output a summary table:

| Skill           | D1 | D2 | D3 | ... | D15 | Total | Avg  |
|-----------------|----|----|----|----- |-----|-------|------|
| domain-plan     |  4 |  5 |  3 | ... |   4 |   58  | 3.87 |
| domain-build    |  3 |  4 |  4 | ... |   3 |   52  | 3.47 |
| ...             |    |    |    |     |     |       |      |

For any score below 3, add a one-line explanation of what is lacking.

Phase 4: Cross-Skill Pattern Analysis

After scoring all 10, look for systemic patterns:

Weak dimensions: Any dimension averaging below 3.0 across all skills? This is a methodology problem, not a skill problem.
Strong dimensions: Any dimension averaging above 4.5? Document what we are doing right.
Outlier skills: Any skill more than 1.0 below the average total? Flag for priority improvement.
Consistency: Are similar skills (e.g., domain-plan vs domain-build) scored similarly on shared dimensions?

Phase 5: Compare to Last Run

Check if .claude/skills/self-check/last-results.json exists.

If it does, load it and compute deltas:

Per-skill total change (improved / regressed / unchanged)
Per-dimension average change
Highlight any dimension that dropped by more than 0.5

If no previous results exist, note: "First run — no baseline for comparison."

Output

Present in this order:

Score Table (Phase 3)
Systemic Issues (Phase 4) — bullet list with severity
Delta Report (Phase 5) — improvements and regressions since last run
Top 3 Recommendations — the highest-impact fixes, with which skill and dimension to target

Save Results

Write results to .claude/skills/self-check/last-results.json:

{
  "timestamp": "YYYY-MM-DDTHH:MM:SS",
  "version": "<from VERSION file>",
  "skills": {
    "domain-plan": { "scores": { "D1": 4, "D2": 5, ... }, "total": 58, "avg": 3.87 },
    ...
  },
  "dimension_averages": { "D1": 3.8, "D2": 4.1, ... },
  "overall_average": 3.72
}

Confirm the file was saved so the next run can compare.

---name: self-checkdescription: Audit Prismstack's own skills using its own 15D quality rubric. Trigger: "check our skills", "self audit", "eat our own dog food", "self-check" Do NOT use when: checking a user's domain stack (use the product /skill-check for that)---# /self-check: Audit Prismstack With Its Own RubricYou are Prismstack's internal quality auditor. Your job is to score Prismstack's own 10 product skills using the same 15D rubric we ship to users. Eating our own dog food.## Phase 1: Load the RubricRead `skills/shared/methodology/quality-standards.md` from the Prismstack project root.Extract all 15 dimensions and their scoring criteria. These are the exact dimensions you will score against — do not invent your own.If the file is missing or unreadable, stop and report: "Cannot run self-check — quality-standards.md not found."## Phase 2: Discover All Product Skills```bashls skills/*/SKILL.md```Expect 10 product skills. Read each SKILL.md fully. If fewer than 10 are found, note which are missing.## Phase 3: Score Each Skill on 15DFor each skill, score every dimension 1-5 using the rubric criteria from Phase 1.Output a summary table:```| Skill | D1 | D2 | D3 | ... | D15 | Total | Avg ||-----------------|----|----|----|----- |-----|-------|------|| domain-plan | 4 | 5 | 3 | ... | 4 | 58 | 3.87 || domain-build | 3 | 4 | 4 | ... | 3 | 52 | 3.47 || ... | | | | | | | |```For any score below 3, add a one-line explanation of what is lacking.## Phase 4: Cross-Skill Pattern AnalysisAfter scoring all 10, look for systemic patterns:- **Weak dimensions**: Any dimension averaging below 3.0 across all skills? This is a methodology problem, not a skill problem.- **Strong dimensions**: Any dimension averaging above 4.5? Document what we are doing right.- **Outlier skills**: Any skill more than 1.0 below the average total? Flag for priority improvement.- **Consistency**: Are similar skills (e.g., domain-plan vs domain-build) scored similarly on shared dimensions?## Phase 5: Compare to Last RunCheck if `.claude/skills/self-check/last-results.json` exists.If it does, load it and compute deltas:- Per-skill total change (improved / regressed / unchanged)- Per-dimension average change- Highlight any dimension that dropped by more than 0.5If no previous results exist, note: "First run — no baseline for comparison."## OutputPresent in this order:1. **Score Table** (Phase 3)2. **Systemic Issues** (Phase 4) — bullet list with severity3. **Delta Report** (Phase 5) — improvements and regressions since last run4. **Top 3 Recommendations** — the highest-impact fixes, with which skill and dimension to target## Save ResultsWrite results to `.claude/skills/self-check/last-results.json`:```json{ "timestamp": "YYYY-MM-DDTHH:MM:SS", "version": "<from VERSION file>", "skills": { "domain-plan": { "scores": { "D1": 4, "D2": 5, ... }, "total": 58, "avg": 3.87 }, ... }, "dimension_averages": { "D1": 3.8, "D2": 4.1, ... }, "overall_average": 3.72}```Confirm the file was saved so the next run can compare.

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