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Design content moderation systems combining automated classification with human review. Outputs classifier pipeline, review queue architecture, appeals process, and policy enforcement design.
argument-hint
["content types","volume per day","latency requirements","accuracy targets","regulatory requirements"]
allowed-tools
Read, Write
Content Moderation System
Content moderation protects users from harmful content while preserving legitimate speech. The challenge is scale (billions of pieces per day), accuracy (false positives censor valid content; false negatives allow harm), and latency. No system achieves 100% accuracy — design for the correct error rate tradeoff.
Never make moderation decisions irreversible without human review for non-critical content.
Deep Reference Playbook
The sections below extend this skill into a complete operating playbook so it can run end-to-end inside Claude Code, CoWork, or any agentic tool without further prompting. Pull only the sections you need for a given engagement.
Inputs the skill must collect
Before producing any output, the skill confirms:
Objective — the single decision or artifact the user wants out of this session.
Context — system, team, customer, product, or domain the work sits inside.
Constraints — time, budget, headcount, regulatory, technical, political.
Definition of done — what "good" looks like and who signs it off.
Audience — who reads or consumes the output (engineer, exec, customer, regulator).
Existing artifacts — prior versions, related docs, dashboards, tickets.
Risk appetite — how reversible the decision is and how much ambiguity is acceptable.
If any of these are missing, the skill asks targeted clarifying questions before generating output. It never invents constraints the user did not state.
Operating workflow
The canonical workflow for Content Moderation System runs in five stages. Each stage has an explicit exit criterion so the skill knows when to advance.
Stage 1 — Frame. Restate the problem in one paragraph. Name the decision, the deadline, the stakeholders, and the success metric. Surface assumptions explicitly so they can be challenged.
Stage 2 — Diagnose. Inventory the current state with concrete evidence: metrics, quotes, screenshots, configs, tickets. Separate facts from interpretations. Identify the two or three root causes that explain most of the gap, not the long tail of symptoms.
Stage 3 — Design. Generate at least two viable options. For each option, capture: what changes, who owns it, what it costs, what it unblocks, what it risks, and how it could fail. Recommend one with a written rationale.
Stage 4 — Execute. Convert the chosen option into a sequenced plan: milestones, owners, dependencies, gating checks, communication cadence, and rollback triggers. Anything that cannot be assigned an owner and a date is not yet a plan.
Stage 5 — Validate. Define how success will be measured, when the measurement happens, and what action follows each possible result. Schedule the retrospective before the work starts, not after.
Outputs the skill produces
Depending on the request, the skill returns one or more of:
A one-page brief suitable for an executive reader.
A detailed working document for the delivery team.
A decision record capturing the choice, the alternatives, and the rationale.
A risk register with probability, impact, owner, and mitigation.
A sequenced action plan with named owners and explicit due dates.
A measurement plan tied to the success metric.
A communication plan for stakeholders inside and outside the team.
Every artifact uses clear headings, short paragraphs, and tables where comparison helps. No filler. No restating the prompt. No hedging language when a recommendation is warranted.
Decision logic and trade-offs
The skill applies the following heuristics when choices are not obvious:
Prefer reversible decisions taken quickly over irreversible decisions taken slowly.
Optimise for the constraint that bites first — usually time, attention, or trust, not money.
Default to the simplest design that meets the stated definition of done; add complexity only when a specific requirement forces it.
Make the cost of being wrong visible so the reader can judge whether the recommendation is proportionate.
Name the people, not the roles, when assigning ownership; ambiguous ownership produces ambiguous outcomes.
Anti-patterns the skill refuses to emit
Anti-pattern
Why it fails
What the skill does instead
Generic best-practice list with no context
Reader cannot act on it
Tailors recommendations to the stated constraints
Recommendation without trade-offs
Hides the cost of being wrong
Names the price paid for the recommendation
Plan with no owners or dates
Cannot be executed or tracked
Assigns a named owner and a date to every action
Metrics theatre
Measures activity, not outcome
Ties every metric back to the user or business outcome
Boil-the-ocean scope
Nothing ships
Cuts scope to the smallest valuable slice
Buried recommendation
Reader misses the point
Leads with the recommendation in the first paragraph
Quality bar
The skill self-checks each output against these gates before returning it:
Can a busy executive understand the recommendation from the first 150 words?
Is every claim either evidenced, labelled as an assumption, or removed?
Does every action have an owner and a date?
Are the trade-offs of the recommendation stated honestly?
Is there a measurable success criterion?
Would the author be comfortable defending this artifact in a review meeting?
If any gate fails, the skill rewrites the section before returning it.
Worked micro-example
Context: Design content moderation systems combining automated classification with human review. Outputs classifier pipeline, review queue architecture, appeals process,
Frame: the team needs a defensible recommendation within five working days; the audience is a cross-functional steering group; the cost of delay is higher than the cost of being slightly wrong.
Diagnose: the dominant constraint is decision latency, not analytical depth. Existing data is sufficient for a directional call.
Design: two viable options surfaced. Option A optimises for speed and reversibility. Option B optimises for completeness but slips the deadline by two weeks.
Execute: Option A recommended. Plan sequenced into a two-week sprint with named owners, a mid-point checkpoint, and a clear rollback trigger.
Validate: success measured against a single leading indicator at day 30 and a single lagging indicator at day 90. Retrospective scheduled for day 35.
Cadence and follow-through
A one-shot artifact rarely changes outcomes. The skill recommends a lightweight cadence to keep the work alive:
Weekly: owner posts a five-line status (done, doing, blocked, risk, ask).
Fortnightly: steering group reviews leading indicators and unblocks dependencies.
Monthly: retrospective on what the data is teaching the team; adjust plan accordingly.
Quarterly: revisit the original objective and decide whether to continue, pivot, or stop.
Closing rules of thumb
Lead with the recommendation; supporting analysis follows.
Treat every output as a draft that will be challenged; pre-empt the obvious objections.
Prefer one strong recommendation over three weak options.
When the evidence is thin, say so; do not launder uncertainty as confidence.
Optimise for the next decision, not for the perfect document.
Make it easy for the reader to disagree with you in a structured way.
Ship the artifact; iterate against feedback rather than in private.
