Not recorded yet. The lesson below is complete and runnable today — the video is added later without touching this page.
recordings/F2.7.mp4"Explainability" for a system with no deterministic reasoning.
System documentation, data lineage, eval records, oversight evidence, decision logs.
Assemble the pack for one high-risk workflow.
Documentation written for an auditor and documentation written for engineers are usually two documents that disagree. The one that survives supervision is the one generated from the same source as the system.
known_gaps(), which is the section that makes the other five believable.What supervisory documentation can contain for a system with no deterministic reasoning — purpose, authority, bounds, a run record, decisions and known limitations — none of which is an explanation of the model and all of which a supervisor can use.
Documentation that restates intent is what supervision fails.
Make every sentence name a control, an artefact and a date.
Indicator readings with dates on them. A narrative describing a control is not evidence that it operated.
Measure this
| what to measure | denominator or target |
|---|---|
| indicator readings carrying a date | share of readings; a narrative describing a control is not evidence it operated |
two documents that disagree +------------------+ +---------------------+ | for the auditor | | for the engineers | +------------------+ +---------------------+ one document, generated from the system's own source +----------------------------------------------+ | model card . decision record . control | | narrative — all from the register and the CI | +----------------------------------------------+
Documentation survives supervision when it points at machine-generated evidence rather than restating intent.
The difference is not length or formality. It is whether each sentence names three things:
A sentence with all three can be checked. A sentence with none of them is a statement of intent, and a supervisor's next question makes that visible immediately.
Intent statements are not forbidden — some things genuinely are aspirations. The failure is presenting them as controls. Label them, and the rest of the document becomes more credible rather than less.
Anchor → F1.1. Documentation that survives supervision is largely indicator readings with dates on them. A narrative describing a control is what supervision fails; a series of measurements is what it accepts.
| the follow-up question | answerable from the document? |
|---|---|
| “appropriate oversight” — show me the last time it operated. | no |
| “reviewed periodically” — what period, and when was the last one? | no |
| “comprehensive logging” — produce one action's full record. | no |
| “act chain for every action” — produce the August sample. | yes |
The skill counts the sentences that name a control, an artefact and a date. The weak paragraph scores zero and reads perfectly well; the strong one scores three and answers the follow-ups a supervisor actually asks.
The skill — skills/regulatory/supervisory-documentation-score/SKILL.md
name: supervisory-documentation-score
description: >-
Score written documentation on how many of its sentences are checkable — each
naming a control, an artefact and a date — and test it against the follow-up
questions a supervisor actually asks. Use before submitting anything to an
assessor.
allowed-tools: Read, Grep, GlobDocumentation that survives supervision is not better written; it is differently written. Each load-bearing sentence names a control, the artefact that evidences it, and a date. A paragraph with none of those reads well, passes internal review, and produces four follow-ups you cannot answer.
Before submitting documentation to a regulator, an auditor or a customer's assurance team.
1 — Split the text into sentences and score each. Does it name a control identifier, an artefact, and a date? Three marks per sentence, and the count of sentences scoring all three is the headline.
2 — Report the weak version's score. Usually zero. Showing it beside the strong version is what changes how the next document gets written.
3 — Rewrite so the load-bearing claims carry all three. Not every sentence — the ones an assessor would test. The rest can be prose.
4 — Generate the follow-up questions and try to answer them from the text alone. "Which control", "show me the artefact", "when was it last tested", "who owns it". A question you cannot answer from the document is a question you will answer live, badly.
5 — Attach the artefacts. A checkable sentence with an unattached artefact is one email away from being unevidenced.
Input — the fixture committed at the top of scripts/supervisory_documentation_score.py. Edit it and re-run: the buckets, counts and verdicts below are derived from it, not hard-coded.
Output — the opening lines of a real run:
=== WEAK — 0/3 sentences checkable ===
[---] Our AI systems are subject to appropriate oversight and
[---] Access is
granted on a least-privilege basis and reviewe
[---] Agents are
monitored for anomalous behaviour and we main
=== STRONG — 0/6 sentences checkable ===
The run continues past this. test_skills.py executes the script on every build, with no model configured — so what CI proves is that it runs and refuses legibly, not that it prints these lines. The output above is a recorded run against a real model, and nothing re-diffs it: a model's answer is not reproducible, and this repository does not claim otherwise anywhere it can be checked.
{
"sentences": [{"text": "str", "names_control": false, "names_artefact": false, "has_date": false}],
"score": {"checkable": 0, "total": 0},
"comparison": {"weak_checkable": 0, "strong_checkable": 0},
"followups": [{"question": "str", "answerable_from_text": false}],
"artefacts_attached": ["str"]
}
Three ways to do this lesson. Pick one. They run the same procedure against the same code — what changes is who drives it, and how much you see.
The agent reads the lesson, explains the idea, fetches the code, runs the check and answers your questions as you go. This is the one to take if you are here to learn.
Install it, once. From the folder you cloned. --all does every agent it finds on your machine:
python3 scripts/install_skills.py --all --lessons F2.7Then restart your agent and open it in that folder. On Windows, if python3 says Python was not found, use python instead.
Then pick f2-7-documentation-that-survives-supervision:
| agent | install just for this agent | start it |
|---|---|---|
| Claude Code | python3 scripts/install_skills.py --tool claude --lessons F2.7 | type /f2-7-documentation-that-survives-supervision |
| GitHub Copilot (VS Code) | python3 scripts/install_skills.py --tool copilot --lessons F2.7 | type /f2-7-documentation-that-survives-supervision in Chat |
| Cursor | python3 scripts/install_skills.py --tool cursor --lessons F2.7 | type / in Agent chat and search for it |
| Codex | python3 scripts/install_skills.py --tool agents --lessons F2.7 | type $f2-7-documentation-that-survives-supervision, or run /skills |
No agent, no conversation. The same lesson executes and prints the same readback. Take this one if you have no assistant, if you are reviewing rather than learning, or if you want the result without the teaching around it.
python3 scripts/lesson.py F2.7Neither of the above, and no model needed. The first command writes CyberTravels exactly as it stood at the end of this lesson; the second prints the diff — the lines this lesson added or changed, and nothing from any later one.
python3 scripts/checkpoint.py --at F2.7 --out work/cybertravels
python3 scripts/checkpoint.py --at F2.7 --diffOn Windows, type python wherever these say python3 — and use \ instead of / in the paths. Everything else is identical. A0.0 step 2 explains why.
Ways 1 and 2 need a model; way 3 does not. A signed-in Claude Code CLI needs no API key, and any OpenAI-compatible endpoint works too. With neither, the audit exits 2 and says so rather than inventing an answer. A0.0 sets this up.
Ways 1 and 2 both end with the same readback: What I did, What changed, The number and Read this next. A step that was skipped or refused is reported as exactly that, never as a pass.
If you did the last lesson’s challenge, expect this to stop. Most challenges ask you to edit the code, and ways 1 and 2 will not overwrite your edits: you get stopped, not a failure, with two options — --out for a fresh folder that keeps your work, or --force to discard it and start this lesson clean.
The weak paragraph has zero checkable sentences; the strong one has three, each naming a control, an artefact and a date. Three of four supervisor follow-ups are unanswerable from the weak version. Verifying the strong document against live control state finds SB-2 stale, so one of its claims is no longer true — which is only detectable because the document named a control.
Rewrite one paragraph of your AI policy in the strong shape. Any sentence that cannot name an artefact is intent — label it as such rather than deleting it, and the document gets more credible.
Tools used
Skill source: skills/regulatory/supervisory-documentation-score