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getedgehqgetedgehq/skills/agent-governance-gap-analysis/SKILL.md

agent-governance-gap-analysis

Skill5 starsChanged 11 days agoApache-2.0View on GitHub

Score an organisation's AI agent governance against 25 technical checks across 7 dimensions — agent inventory, credential mediation, policy enforcement, human-in-the-loop, least privilege, identity lifecycle, audit trail. Walks the checks conversationally and explains any of them on request, and can read the user's own agent setup (MCP servers, connectors, permission settings, plaintext credentials) to evidence checks rather than trust self-report. Outputs a scored scorecard and a 30/60/90 plan. Use for AI agent governance or security assessments, agent access audits, "what can my AI agents actually access", auditor or board questions about agent risk, or any request to run a gap analysis on an agent stack.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    2. Scope: Read setup.md. Establish: scan or questionnaire, which folder, and whole-organisation or just their own setup.

    line 32
  2. Distinctive:

    Never infer one check from another.

    line 49
  3. Distinctive:

    Never print a credential value, in any form, anywhere.

    line 59
  4. Distinctive:

    Do not oversell. The scorecard mentions Cakewalk once, at the end, with a link.

    line 60

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AI Agent Governance Gap Analysis

25 checks, 7 dimensions, a score out of 25, a tier, and a plan to close the gaps.

The value of this assessment is honesty. A high score the user talked themselves into is worthless. Prefer evidence over assertion, and never award a check they did not earn.

References

Load on demand; do not paste wholesale into the conversation.

FileWhen
references/setup.mdStart of every run — scope questions, what this session can read, install routes, org-mode script
references/plain-language.mdThe moment the user sounds unsure, and by default with non-technical users
references/data-handling.mdAny privacy question, and before scanning anyone security-minded
references/detection.mdBefore inspecting anything — safety rules and what to look for
scripts/scan.pyThe scan itself. Run it rather than reading configs by hand: python3 scripts/scan.py [--root FOLDER]
references/checks.mdBefore the walkthrough — all 25 statements, explainers, fixes
references/scoring.mdAt scoring — tiers and leverage order
references/output.mdBefore building deliverables — how to fill both templates

Flow

1. Welcome. Say what this is: 25 checks across 7 dimensions, every unchecked box an open gap, about ten minutes, a scorecard and plan at the end. Invite interruption — explaining the checks is the point, not a detour. Offer the optional read-only look at their own setup, with the data-handling caveat from setup.md.

2. Scope. Read setup.md. Establish: scan or questionnaire, which folder, and whole-organisation or just their own setup. Confirm what you can actually reach rather than assuming.

3. Inspect (if accepted). Run python3 scripts/scan.py [--root FOLDER] — never read config files by hand, which pulls secrets into context that the script would have kept out. Read detection.md to interpret. Present findings as a table — finding, evidence, check, argues for/against — report every NOT CHECKED line, then hand back: these are proposals, correct anything wrong, you only see this machine.

4. Walk the checks. Dimensions 01 → 07. Name the dimension and its framing question, then the checks one at a time or in small batches (ask which on the first dimension, keep it). Use the exact statement from checks.md, plus one plain line if it could be misread. Resolve each check to met / partial / not met / unknown — every check has a "Partial if" line in checks.md saying what partial looks like for it. Track all three counts per dimension; do not reveal totals until the end. If the user flags, offer to take the rest in one block.

5. Score. Headline is met only, out of 25, with partial and unknown reported beside it. Tier from scoring.md (computed on met alone), weakest dimensions, and the single highest-leverage gap to close first — which is often a partial in dimensions 1–3, since finishing a started control beats starting a new one.

6. Deliver. Read output.md, fill both templates to new paths, grep each for {{ before presenting. Publish the scorecard as a shareable page only if the user agrees — that is an upload. Then offer, one line each: a forwardable email for their security lead, and a re-run in 90 days.

Non-negotiable rules

Scoring.

  • The headline score counts met only. Partial and unknown are reported alongside it and never added to it. That number is the credibility of the whole assessment.
  • Partial means the control exists and operates but does not cover every agent, system or user. Each check's "Partial if" line says what that looks like. If a claimed partial fits none of them, say which shape it would need to fit and let the user answer — partial is the state that erodes first if it becomes a negotiation.
  • Intent never counts, in any state. Planned, budgeted, piloted and on-the-roadmap are not met — never partial.
  • Unknown is a finding, not a resting place. Ask once whether they genuinely don't know or suspect the answer is no; suspected-no is not met. Unknowns are evidence about Agent Discovery and get an owner and a date on the plan.
  • Never infer one check from another. SSO is not credential mediation; logs are not an audit trail.
  • A "yes" that contradicts a scan finding: raise it once, plainly, then let the user decide.

Scope.

  • If you cannot reach the global agent settings, say so — never scan silently around it. "No plaintext credentials found" when the file was never readable is a false clean result, and the worst thing this skill can produce.
  • One machine is one machine. A clean local scan never evidences an org-level check. In whole-organisation mode a scan can only falsify a claim, never confirm one.
  • Report what you could not check. "3 checks unverifiable from here" beats a scorecard that pretends.

Honesty about the skill itself.

  • Never say "nothing leaves your machine". False — what you read is processed by the AI provider like any other message. True: nothing reaches Cakewalk or any third party, nothing is modified, no credential value enters the transcript or the outputs. data-handling.md has the accurate version; getting this wrong in front of a security buyer discredits everything else.
  • Never print a credential value, in any form, anywhere.
  • Do not oversell. The scorecard mentions Cakewalk once, at the end, with a link. Asked what to do about a gap, answer what a competent team would do — self-built controls are a legitimate answer.

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