Bias Check Skill

Pre-research and pre-decision bias review.

Preflight: Read target canvas file(s) before any Write/Edit

Hard rule. Before issuing Write or Edit against any .claude/canvas/*.yml, use the Read tool on that file in this session. Claude Code's Read-before-Write check requires the Read tool specifically — cat/head/grep via Bash do NOT satisfy it.

Edit vs Write — different cost profiles (verified 2026-05-14):

• Edit (exact-string replacement): Read with limit: 1 satisfies the check at ~50 tokens. State-tracking is per-file, not per-byte — subsequent Edit calls work anywhere in the file. Use this for partial updates against large canvas files (e.g., purpose.yml at 800+ lines).
• Write (full replacement): do a full Read first. Write obliterates the file; you should see what you're about to replace. The limit:1 shortcut is not appropriate here.

ID-bearing entries — scan the ID space before assigning (added 2026-05-15, v0.23.19): When adding a new component, opportunity, solution, or any other ID-bearing entry to a canvas file, run a Bash grep first to confirm the next ID in your prefix sequence is actually free:

grep -o "<prefix>-[0-9][0-9]*" .claude/canvas/<file>.yml | sort -u -t- -k2 -n | tail -3

Replace <prefix> with the canvas's ID prefix (comp for landscape, opp for opportunities, sol for solutions, ht for human-tasks, etc.). Then pick the next free integer, matching the zero-padding already used in that file. The sort is NUMERIC (-t- -k2 -n) rather than lexical, and that is not pedantry: a plain sort -u orders ht-1 after ht-080, so on a canvas with inconsistent padding it reports the wrong maximum and the next ID collides. Verified on the dogfood repo 2026-08-13, where lexical sort returned ht-1 as the highest human-task ID against an actual ht-080. grep -o is also deliberate: it matches IDs wherever they appear, including cross-references and prose, so an ID that was promised somewhere but not yet defined is not handed out twice. validate_canvas.py has a per-file duplicate-ID check (it reports duplicate id '<id>') that catches the failure on CI, but a duplicate can persist in the working tree for days if CI isn't run between edit and discovery; that happened on 2026-05-15, when a duplicate ID was created in landscape.yml.

Original failure mode: anti-pattern #7 instance #5, 2026-05-09 — agent conflated Bash head with the Read tool, lost ~14k tokens to a Write-fail → remedial-full-Read → re-Write loop. The limit:1 discipline (graduated 2026-05-14, v0.23.18) prevents the second-order cost where the agent correctly follows the rule but full-Reads every time. The ID-scan discipline (graduated 2026-05-15, v0.23.19) prevents the related class where the agent reads enough of the file to satisfy the Edit check but not enough to see existing ID assignments — kin to anti-pattern #8 (Stale State Read).

If this skill writes to multiple canvas files, register each one first (limit:1 for Edit-only paths; full Read for Write paths) AND ID-scan any prefix you intend to assign.

See ${CLAUDE_PLUGIN_ROOT}/engine/agent-operating-contract.md Canvas writes — Read before Write for the canonical rule.

Workflow

1. Identify context: What stage (L0-L5)? What activity (research, decision, evaluation)?

2. Load stage-specific checklist from ${CLAUDE_PLUGIN_ROOT}/harness/cognitive-biases.md for the current scale.

3. For each bias on the checklist:

   • Is this bias likely active in the current situation? (Yes/No/Maybe)
   • If Yes/Maybe: What specific mitigation will be applied?
   • Document the assessment.

4. Check agent's own biases (from ${CLAUDE_PLUGIN_ROOT}/harness/cognitive-biases.md agent section):

   • Sycophancy: Am I agreeing with the user when evidence says otherwise?
   • Recency: Am I overweighting recent context over earlier evidence?
   • Pattern matching: Am I assuming this is like something I've seen before without checking?
   • Completionism: Am I filling gaps with speculation rather than acknowledging uncertainty?

5. Output bias briefing:

   ## Bias Check: [Activity]
   Stage: [L0-L5]
   
   ### Active Risks
   | Bias | Risk Level | Mitigation |
   |------|-----------|------------|
   | ...  | High/Med/Low | ... |
   
   ### Agent Self-Check
   - Sycophancy: [OK/Risk]
   - Recency: [OK/Risk]
   - Pattern matching: [OK/Risk]
   - Completionism: [OK/Risk]
   
   ### Proceed? [Yes / Yes with caution / Pause and address]
   

6. System-level bias diagnosis (Meza): Before attributing resistance or poor outcomes to cognitive bias, check if the "bias" is actually a rational response to a badly designed system. Ask:

   Phase	System Question	If Yes: Fix the System, Not the Person
   Awareness	Does the person know the desired behavior?	Fix communication, not cognition
   Motivation	Does the system reward the desired behavior?	Fix incentives, not mindset
   Ability	Can the person realistically do it?	Fix resources/process, not training
   Reinforcement	Is the behavior sustained by the environment?	Fix feedback loops, not habits
   Sustainability	Can it persist without ongoing intervention?	Fix structure, not willpower

   If 2+ phases point to systemic causes, the primary intervention is system redesign, not individual debiasing. Update the bias briefing output above to flag the system-level root cause.

   The 5 phases above are a systemic diagnostic inspired by behavioral design models (COM-B, Fogg Behavior Model), not a named framework. They are prompts for investigation, not rigid steps.

   Source: Robert Meza (The Bias Gap — diagnostic tool, aimforbehavior.com). Not to be confused with Thaler & Sunstein's "Nudge" theory.

When to Surface This Skill

This skill should be triggered:

• Before any research activity (interviews, surveys, data analysis)
• Before significant decisions at any diamond scale
• At L5 market transitions: Before launch tier classification and go-to-market planning, check for optimism bias (overweighting positive signals), confirmation bias (seeking validation of "ready to ship"), and anchoring (fixating on initial positioning)
• When /mycelium:diamond-progress or /mycelium:diamond-assess detects bias-related gate failures

Canvas Output

Record bias check in .claude/canvas/opportunities.yml under bias_checks section with date, biases mitigated, and research design adjustments made.

Recording the Bias gate

When this check feeds a diamond's bias gate, apply the pass criteria in ${CLAUDE_PLUGIN_ROOT}/engine/theory-gates.md §5 as written. A planned mitigation counts as documented: a named action and the test, owner or later transition that will carry it out. A disconfirming source that is cited and weighed has been sought, even though it is not yet resolved. "A citation is not a resolution" is true, and it is not this gate's test: resolution is demanded by the Cynefin gate (start_experiment, complex domain) and the Four Risks gate. Record not-passed for what the fail column names, or for a named bias with no mitigation at all.

Decision Log (MANDATORY per G-P4)

APPEND a ### Bias Check entry to .claude/harness/decision-log.md with: activity assessed, active bias risks found, mitigations applied, agent self-check results.

Theory Citations

• Kahneman: Thinking, Fast and Slow (System 1/System 2)
• Shotton: The Choice Factory (behavioral biases in decision-making)
• Torres: Continuous Discovery Habits (bias in research)
• Robert Meza: The Bias Gap (systemic bias reframing -- when "bias" is a rational response to a badly designed system)