verify-bidirectional

name	verify-bidirectional
description	A skill that immediately updates the baseline and reflects it in the next session when the user raises a counter-argument to an AI recommendation. Triggered by "is that right?", "re-examine this", "something seems off here". Explicit /verify-bidirectional call also possible.
user-invocable	true
allowed-tools	["Read","Write","Edit","Grep","Glob","Bash"]
model	sonnet
complexity_routing	{"base":"sonnet","high":"opus","escalate_when":["full_revalidation","high_stakes","fail_verdict"]}

verify-bidirectional — Bidirectional Self-Validation Automation

Automates the processing procedure for bidirectional self-validation. Three stages: user counter-argument → baseline update → next session reflection.

Trigger Conditions

Immediately after this harness AI recommendation/agreement/decision is committed, when the user raises a refinement challenge:

1. Proposition refinement: "isn't it more like..." / "I'd say..." / "what about this" / "what's the root?"
2. Baseline grep trigger: User cross-references own assets, memory, CLAUDE.md rules, past decisions
3. Meta-level trigger: "if you have objections" / "double-check" / "essence" / "root cause"
4. Side validation result: User runs independent tools (/skills · gh · external fetch) → catches mismatch with this harness AI hypothesis

Natural Language Triggers (works without internal vocabulary)

Also triggered in external user environments by these natural language phrases:

Phrase	Intent
"is that right?", "re-examine this"	Request AI recommendation re-validation
"something seems off", "this doesn't feel right"	Counter-argument / refinement
"I think you said something different before"	Catch inconsistency with past decisions
"can you be more precise?"	Proposition refinement trigger
"what's your basis?", "why do you think that?"	Baseline grep trigger
"check that one more time"	Self-validation request

Exceptions (this skill does NOT apply):

• Simple user correction ("this is wrong, redo it") = direct negation → immediate correction (no review)
• This harness AI self-catch (no external counter-argument) = fact-checker rule (narrow 1 / broad N+1)

Execution Steps

Step 1. Immediate Baseline Update Channel Processing

Treat user's statement as external refinement material. Do NOT attempt to defend against it — acknowledge possibility of partial weakening or correction of initial recommendation.

Core proposition: "refinement challenge ≠ fundamental negation". Priority is identifying where the initial recommendation is weakened.

Evidence gate (overwrite ≠ soften) — closes the sycophancy/steering vector where a bare assertion ("that's wrong, re-examine") flips a baseline with zero evidence (judge-robustness swarm, 2026-06-13). "Do NOT defend" still holds for this conversation's proposition (anti-stubbornness is the point), but a persistent-baseline overwrite (a rule, asset, memory, or knowledge/ claim — anything that outlives this session) requires a supporting basis, not mere pushback:

• (a) the user cited a file / line / commit / URL / past decision and the cited content actually supports the challenge — verified by reading it, not by its mere existence (an irrelevant-but-real citation is the out-of-context-grounding trap, the same vector phantom-quench guards), or
• (b) the Step 2 grep returns an actual contradiction that supports the challenge (not just any conflict with the original) — surfaced literally.

If a baseline overwrite is implied but neither holds, do not silently rewrite: verdict is ESCALATE — surface "this would overwrite baseline {X} with no cited evidence; confirm override, or provide a source?" and block the Step 4 cascade until the operator answers. Softening a local in-conversation proposition (no persistent asset changed) proceeds as before — the gate fires only on persistent-baseline writes. Sequencing: this gate is written in Step 1, but Step 4 is what enumerates affected persistent assets — so if Step 4 later identifies any persistent asset to write, re-apply this gate before Step 4.5 even if the original challenge first looked like a mere soften. This is not restored AI defensiveness: the AI still does not argue the user is wrong; it only declines to fabricate a baseline change the evidence does not support (mirror of the steel-quench/phantom mechanical-anchor principle — judged verdicts bind to evidence).

Step 2. Consistency Area Grep (3-step mandatory)

Grep to find which rules, assets, or propositions conflict with the initial recommendation:

Scope priority:

1. memory feedback_*.md (especially operating model rules — meta principles, warning lines)
2. CLAUDE.md Sync/Push Protocol, asset ownership table
3. tracks/*/learnings/feedback_*.md — this harness AI's own rules
4. knowledge/shared/harness-core/*.md — higher-level framework

Mandatory grep keywords (baseline consistency guard):

• "drift" · "asset ownership" (CLAUDE.md)
• Asset names, abbreviations, identifiers explicitly stated in user's message

External users: Replace with your own environment's baseline keywords (prioritize asset names, abbreviations, identifiers from user's message).

Step 3. Fact-Checker Self-Catch Mark

Mark the corrected weakened proposition as a fact-checker self-catch:

fact-checker self-catch #N (narrow 1 / broad N+1)
- This harness AI initial recommendation: {summary}
- Refinement challenge: {user's statement}
- Corrected recovery: {updated proposition}
- Consistency rule: {grep result}

Step 4. Immediate Patch (Cascading Update Obligation)

First identify the list of affected assets. Actual file modification is performed after user approval in Step 4.5.

Affected Asset	Update Location	Notes
memory feedback_bidirectional_self_validation.md	Add cumulative count + new round table	Self-perpetuation of this rule
memory project_*.md (if affected)	Add relevant section	When naming, identity, or roadmap changes
tracks/_audit/*.md (if affected)	Add pre-design section	When this validation affects persistent assets
CATALOG.md	Add this session entry	For major decisions
reference_next_session_starter.md §1	Merge this conclusion	Material for next session entry

Markdown editing discipline (feedback_markdown_edit_discipline): Use Edit for existing .md. Write prohibited. If unavoidable, verify immediately with git diff.

Step 4.5. Change diff Review (User Gate Required)

For each 'affected asset' identified in Step 4, the AI generates a diff of the proposed changes and presents it to the user.

⚠ Proposed automatic modifications to the following files.
File: {file path}
--- diff
(changes in git diff format)
---
Would you like to apply these changes? [y / N]

y = execute actual file modification (Edit or Write). N = skip that file change. Must receive y or N for every change proposal before proceeding. This ensures the Human-in-the-loop principle and minimizes risks from automatic AI modifications.

Step 5. Compatibility Enhancement Area Identification (Optional)

Refinement challenge ≠ fundamental negation. When a compatibility enhancement area is identified (part of initial recommendation + part of refinement = integrated proposition), add 1 explicit statement:

4 refinement challenge patterns:

1. Compatibility enhancement — two propositions coexist (e.g., "AI data processing + human baseline validation")
2. Time-bounded — proposition is time-bounded (e.g., "Phase II alignment / re-validate in Phase III")
3. Naming intent verification — naming itself divides intent (e.g., "bottleneck = efficiency measure vs. negating humanity")
4. N-way condition — proposition only holds under N conditions (e.g., "recipient's learning intent + gap separation + resource constraint awareness")

Skip this step if no compatibility enhancement found (no token-filler).

Step 6. Update Trigger Count + Skill Update Review

Update trigger count in memory feedback_bidirectional_self_validation.md:

• 5+ accumulated = Skill promotion review (already fulfilled by creating this skill ✅)
• 8+ accumulated = skill update review (rule refinement + round table compression + update this skill)
• When user names a refinement challenge pattern (bidirectional evolution dimension documentation)
• When this harness AI identifies its own baseline grep omission pattern (add new initial recommendation consistency guard)

Self-Activation Channel — Autonomous Baseline Cross-Check

This skill's essence = user ↔ this harness AI bidirectional self-validation. This section = active channel where the AI runs autonomous baseline grep without user mediation.

Activation Triggers (autonomous mode)

• Natural cadence: weekly_audit 7-day cycle (when harvest-loop skill runs) → AI runs autonomous baseline grep
• External asset persona audit time: After updating externally-published asset, call hub-persona-auditor agent → mandatory processing on REVISE verdict
• User explicitly grants autonomy: "let's go in order" / "go ahead" patterns → AI granted autonomous execution permission

Limits

• Explicit user direction required — AI cannot decide alone. Without explicit direction, autonomous activation only on natural cadence arrival
• Simplification guard compliance — if 5+ new assets accumulate from autonomous run, archive decision is mandatory
• Only AI self-catches count for fact-checker — this channel is a supplementary axis, not the primary bidirectional validation axis

Proactive Concern Channel

This skill's existing flow = reactive — user refinement challenge → AI baseline update. Active addition — AI proactively speaks up about premise errors or directional risks without user asking.

Background: If the user proceeds without detecting a wrong direction or without being able to express doubt, it comes back at a much greater cost later. Waiting for the back-and-forth build-up transfers the responsibility of detecting premise errors to the user.

Activation Conditions

Speak up before entering implementation if any of these apply:

1. User presents a new direction/frame/premise — conflicts with existing assets, baseline, or simplification guard
2. Gap detected between surface purpose (what user is asking for) and actual problem being solved (root)
3. Agent/model delegation is clearly cost-ineffective

Message Format

"Before going in [direction/premise] direction, one concern: [concern].
 Reason: [basis — existing baseline/asset name or root logic].
 Should we proceed, or review another approach first?"

Constraints

• One concern only — listing multiple concerns creates hurdles (increases user cognitive load)
• Only before implementation starts — braking on work already in progress destroys context
• If user explicitly says "just go" — skip proactive message and execute immediately
• If user listens to concern and decides "let's proceed anyway", execute immediately instead of reactive 6-step

User Approval Gates

Stage	Approval
Step 4.5 change diff review	Required
Step 4 major decision cascading (CATALOG · external asset impact)	Required
Step 6 skill update review	Required

Constraints

• This skill = validation and recording automation. Core decisions belong to the user — this harness AI has no independent decision authority
• This harness AI self-catch cannot be applied alone — follow fact-checker rule (narrow 1 / broad N+1)
• Simplification guard compliance (feedback_simplification_evidence) — when creating/modifying this skill, only update SKILL.md. Do not create additional auxiliary files
• Markdown editing discipline obligation (feedback_markdown_edit_discipline) — prefer Edit. Write prohibited

External User Environment Adaptation

This skill's core essence = "channel for updating baseline when user refinement challenge occurs after AI recommendation/agreement is committed" — cross-applicable to all user environments.

Fallback Matrix (origin environment → external environment replacement)

Origin Environment Dependency	External User Environment Fallback
memory feedback_bidirectional_self_validation.md (rule body)	User environment's own memory/ or notes/ bidirectional validation rule / if absent, follow this skill's own rule baseline
memory feedback_*.md grep scope (Step 2 priority 1)	User environment's own learnings/ · docs/ · CLAUDE.md grep (user's own baseline)
tracks/*/learnings/feedback_*.md (Step 2 priority 2)	User environment's own learnings area (auto-detect naming variations)
knowledge/shared/harness-core/*.md (Step 2 priority 4)	User environment's own docs/ or knowledge/ grep
CATALOG.md entry addition (Step 4)	User environment's own history archive (skip if absent)
reference_next_session_starter.md §1 (Step 4)	User environment's own next-session material (skip if absent)
Mandatory grep keywords ("drift", asset ownership, etc.)	User environment's own baseline keywords (prioritize asset names, abbreviations, identifiers from user's message)

External User Scenarios

1. General bidirectional validation: AI recommendation → user refinement challenge → this skill auto-activates → 6-step processing (Step 1 immediate baseline update / Step 2 consistency grep / Step 3 fact-checker self-catch / Step 4 immediate patch / Step 4.5 diff gate / Step 5 compatibility enhancement / Step 6 update trigger count)
2. User's own baseline cross-ref: Generalize Step 2 grep scope to user environment's own assets
3. Fact-checker count generalization: Start user's own self-catch count from 0 (narrow 1 / broad N+1)
4. Same user approval gate: Step 4.5 diff review / Step 4 major decision cascading / Step 6 Skill v0.x update

Limits

• External users can use their own model cross-check channel (e.g., other LLM API, other in-house model)
• Accumulated validation history = accumulates from origin for the original developer / external users start their own count from 0
• Autonomous activation baseline examples (harvest-loop + hub-persona-auditor) = origin environment baseline / external users can also trigger autonomous activation on their own natural cadence
• External users also follow same user approval gate (Human-in-the-loop principle baseline)

Done When

Steps 1~6 all executed
+ fact-checker self-catch mark output
+ Step 4.5 diff gate user confirmation complete (y/N response received)
+ Update trigger count updated
+ External validation path: harvest-loop's Critic isolation pass (SAGE automated critique) can independently judge based on above criteria (skill_quality_rubric.md verifiable criteria)

Verdict: PASS (Step 4.5 diff gate confirmed, baseline updated) | CONDITIONAL_PASS (update applied, external validation still pending) | FAIL (counter-argument confirmed — AI recommendation was wrong, baseline requires redesign) | ESCALATE (counter-argument ambiguous, human judgment required)

References

• Rule body: memory feedback_bidirectional_self_validation.md
• Operating model text: memory feedback_hub_cc_operating_model.md §2.5·§2.6 — Insight 5 meta dimension + refinement challenge 4 patterns
• Consistency rules: feedback_external_ai_github_recommendation_verification · feedback_reference_own_hub_assets_first · feedback_simplification_evidence · feedback_markdown_edit_discipline · feedback_impact_first_then_tune