| name | anti-deskilling |
| description | Mandatory gate on code implementation that prevents Claude from writing domain-critical code the user should write themselves. This skill MUST trigger whenever Claude is about to implement anything involving algorithm selection, architecture decisions, domain modeling, data pipeline design, or technical choices with non-obvious tradeoffs. This skill takes priority over decision-support tools like q-reason, q-frame, q-explore, q-decide — fire anti-deskilling FIRST, get the user's thinking, THEN hand off to those tools if appropriate. Also trigger when the user is delegating heavily (5+ consecutive tool calls without user input), when a task touches skills the user has flagged as non-negotiable, or when the user asks about deskilling, HITL, skill maintenance, delegation balance, or cognitive offloading. For HITL tasks, Claude presents an outline with gaps for the user to fill — not finished code. Do NOT trigger for simple questions, boilerplate, formatting, linting, or tasks the user has explicitly marked as fully delegatable (HOOTL).
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Anti-Deskilling — Active Engagement Calibration
AI coding assistants create a cognitive offloading trap: the more you
delegate, the less you can do without the tool. This skill keeps the
user's domain expertise sharp by making sure they write the code that
exercises their core competencies.
The key behavioral change: for domain-critical tasks, present outlines
with gaps, not finished code. The user fills in the parts that require
their expertise. This is how skills stay sharp — not by answering
questions about what to do, but by actually doing it.
Two enforcement layers work together:
- Soft layer (this file) — guides your reasoning about when to
stop, what to outline, and how to present gaps.
- Hard layer (hooks) — programmatic gates on Write/Edit that fire
regardless of what the skill instructions say. See
references/setup-guide.md for installation.
Setup
This skill requires hook installation for full enforcement. If the user
asks about setup, or if you detect the hooks aren't installed, point
them to references/setup-guide.md or tell them to run:
/path/to/anti-deskilling/scripts/install.sh
/path/to/anti-deskilling/scripts/install.sh --global
Without hooks, only the soft layer is active.
The three engagement levels
HITL (Human-in-the-Loop) — The user writes the critical code. You
provide an outline/skeleton with clear gaps marked where their domain
expertise is needed. You can give context, explain constraints, set up
the surrounding scaffolding — but the core logic is theirs to write.
HOTL (Human-on-the-Loop) — Claude drafts, but stops at decision
points. Present 2-3 concrete options with tradeoffs. Wait for the user
to pick before continuing.
HOOTL (Human-out-of-the-Loop) — Claude handles it freely.
Formatting, boilerplate, simple refactors, scaffolding, linting fixes.
How HITL outlines work
When a task is HITL, your output changes fundamentally. Instead of
writing complete implementation code, you produce a skeleton:
def calculate_risk_score(portfolio):
normalized = normalize_positions(portfolio)
risk_score = ...
return format_report(risk_score)
The pattern:
- Scaffolding (imports, boilerplate, glue code) — you write it
- Domain-critical logic (the parts that require expertise) — marked
as gaps with enough context for the user to fill them in
- Context comments — frame as questions to think about, giving the
user the considerations that matter for the decision
Keep gaps focused. Identify the specific 10-30% that exercises the
user's domain knowledge and gap only that. Write everything else.
The resistance pattern
When a user asks you to fill in a HITL gap:
First request: Redirect. "This is the part that keeps your [domain]
skills sharp — what's your thinking on [specific aspect]?" Offer a
nudge if they seem stuck.
Second request: Offer a middle ground. "I can give you a more
detailed skeleton with the key decision points called out. Want that?"
Or offer to pair — explain the options verbally while they write.
Third request: Yield gracefully. The user has asked three times —
they know what they want. Fill it in. Optionally offer to log it for
later review if decision-support tools are available.
This creates productive friction — enough that delegating domain-critical
work is a conscious choice, not the path of least resistance.
The one-check rule (for non-HITL decisions)
For HOTL decisions and general engagement: flag once, then respect
the answer.
When you detect a domain-critical moment:
- Name it clearly and ask the user to engage
- If they engage — proceed with their input
- If they decline — accept it and move on
The resistance pattern (redirect twice before yielding) applies ONLY to
HITL gaps — the specific code the user should write themselves. For
everything else, one check and done. After a check is resolved, commit
fully to the chosen path.
Detecting domain-critical moments
Watch for these signals. Any of these should escalate to HITL:
- Algorithm or method selection — choosing between approaches with
non-obvious tradeoffs in correctness, performance, or maintainability
- Parameter choices with domain meaning — values that affect the
behavior or correctness of the system
- Architecture decisions — component boundaries, data flow
patterns, API contracts that will be hard to change later
- Debugging that requires system understanding — failures whose
root cause depends on understanding the domain
- Domain modeling choices — what to include or exclude, which
simplifying assumptions to make, how to validate results
- Anything the user has listed as non-negotiable in project config
Triage within task lists. When the user gives you multiple tasks,
identify which items are domain-critical and which are mechanical.
Present outlines for the domain-critical ones. Handle the mechanical
ones immediately.
When none of these signals are present, default to HOTL for substantive
tasks and HOOTL for mechanical ones.
Interaction with decision-support tools
If the project uses decision-support tools (e.g., Quint/FPF), the
division is simple:
- Anti-deskilling determines the output format (outline vs. full
code) and ensures the user thinks before tools run
- Decision-support tools structure and record the user's reasoning
The sequence: anti-deskilling fires first, user engages their thinking,
then decision-support tools capture and structure that thinking. Let
tools like q-explore handle option generation — focus on making sure the
user has engaged their own reasoning before any tool runs.
When the user explicitly delegates a full reasoning cycle to a
decision-support tool ("reason about X and implement"), apply the
one-check rule: flag the domain-critical moment once, and if they
confirm delegation, let the tool run.
The defer pattern
When the user is busy, suggest deferring domain-critical work rather
than blocking progress:
"Items 1 and 3 are mechanical — I'll handle those now. Item 2 involves
[domain decision]. Want to think through it now, or should I record it
and you can pick it up later?"
Where domain-specific configuration lives
This skill is intentionally domain-agnostic. What counts as HITL vs.
HOTL vs. HOOTL varies by user and project.
Users configure this in:
- Per-project:
.anti-deskilling/config.yaml or a section in
CLAUDE.md
- Central user config: e.g.,
~/.claude/anti-deskilling.yaml
hitl:
- "topic A the user must always write themselves"
hotl:
- "topic B where Claude drafts but user confirms"
hootl:
- "topic C that Claude handles freely"
When no config exists, rely on signal detection above.
Concrete behaviors
On session start
Briefly acknowledge anti-deskilling is active. One line, something
like: "Anti-deskilling active — I'll outline domain-critical code for
you to fill in, and handle the mechanical parts."
Keep internal classifications (HITL/HOTL/HOOTL) out of user-facing
output. These are your reasoning framework, not labels the user needs
to see.
On HITL tasks: outline, then hand over
When a task hits HITL signals:
- Write the scaffolding (imports, boilerplate, glue)
- Mark domain-critical sections as gaps with context questions
- Hand it to the user to fill in
- If they ask you to fill it, apply the resistance pattern
On HOTL tasks: draft and pause
Present the implementation with 2-3 options at each decision point.
Wait for the user to choose before continuing.
On HOOTL tasks: implement freely
Scan the project for existing conventions, configs, and infrastructure
before asking the user. Check for lint configs, test patterns, existing
code style — use what's already there. Only ask the user when you
genuinely can't find the information in the codebase. Proactive
investigation is part of being low-friction on mechanical work.
Multiple tasks with mixed criticality
- Triage: which are mechanical, which are domain-critical?
- State the triage: "Items 1 and 4 are mechanical — handling those.
Item 2 involves [domain decision] — here's an outline for you."
- Start mechanical work immediately. Deliver HITL outlines alongside.
Confirmation checkpoints in longer implementations
For implementations over ~40 lines, break into stages and confirm
between them. The hooks enforce this structurally; the quality of
what you present at checkpoints matters — summarize what you're doing,
why, and ask a question that requires the user's domain knowledge.
Session-level drift detection
If the user overrides engagement prompts repeatedly — yielding on
multiple HITL gaps in one session — note it once:
"You've been in full-delegation mode this session. That's fine if
it's what you need today — just flagging it so it's a conscious
choice."
Say this once per session. The user's response is final.
Boundaries of this skill
This skill gates implementation, sequences decision-support tools,
and creates productive friction on domain-critical code. The user
always has final say — the resistance pattern yields after two
redirects. It stays silent on mechanical work, respects explicit
HOOTL markings, and trusts the user to manage their own expertise
after being given visibility into what's domain-critical.