Refinement Tribunal

Refinement Tribunal

Iteratively refine any text-based content to the highest achievable quality through a parallel three-agent tribunal. A harsh critic identifies flaws and areas for improvement. An advocate identifies and bolsters effective patterns for retention and amplification. A neutral researcher independently discovers what excellence looks like, unanchored by the existing text. The orchestrating agent synthesizes the tribunal's perspectives and improves the content between each cycle.

Objective

Bring text-based content to production-ready quality by repeatedly subjecting it to a tribunal of three independent agents — harsh critique, friendly advocacy, and neutral research — synthesizing their findings, and applying improvements until diminishing returns are reached.

Scope

In-Scope

• Refinement of any text-based content: documentation, skills, instructions, prompts, specifications, architecture decisions, policies, runbooks, templates
• Multi-iteration improvement through parallel tribunal cycles
• Convergence detection and automatic termination
• Domain-contextualized sub-agent prompt construction
• Protection of effective elements from erosion across iterations

Out-of-Scope

• Initial content creation from scratch — content MUST exist before this skill is invoked
• Code compilation, execution, or automated testing
• Factual verification against external sources beyond the agent's training data
• Subjective aesthetic decisions that lack measurable quality criteria

Inputs

Required

Optional

Content Resolution

Parse the user's input to identify the content to refine:

1. If the input contains a recognizable file reference (has a file extension, with or without path separators), attempt to read it. A bare filename like README.md is treated as a file in the current directory.
   • If the file exists, use its contents as the content to refine.
   • If the file does not exist, fail immediately with: "File not found: [path]. Provide the content inline or correct the path."
2. If the input is substantial text (approximately 20 or more words) with no file reference, treat it as inline content. Use judgment near the threshold.
3. If the input is a short description with no file reference and no substantial text, fail with: "Provide the content to refine as a file path or inline text."

Separate any non-content instructions (e.g., "with focus on developer experience") from the content reference before processing.

Outputs

1. Refined content — The final improved version, ready for use or further review.
2. Refinement log — Per-iteration summary containing:
   • Researcher insights applied (with novelty rate)
   • Harsh critic findings addressed (with severity distribution)
   • Advocate findings: strengths protected, amplifications applied, advocate-critic conflicts and resolutions
   • Changes made and whether each was clear-cut or a judgment call
   • Remaining concerns (if any)
3. Convergence rationale — Why the process stopped: convergence reached, or cap hit with unresolved items flagged.
4. Diff summary — If content originated from a file, a summary of changes between the original and refined versions, presented before any file write.

Constraints

Information Barriers — MUST Enforce

These barriers are the mechanism that makes tribunal refinement produce stronger results than self-review. Violating them collapses the technique into a weaker single-perspective analysis.

Why the researcher never sees the content: The researcher's value comes from independently discovering what excellence looks like, unanchored by the existing text. Showing the content causes anchoring bias — the researcher gravitates toward confirming what already exists rather than imagining what could exist.

Why the harsh critic never sees the orchestrator's framing: The critic's value comes from evaluating the content on its own merits. Knowing what the orchestrator thinks is strong or weak biases which issues get surfaced.

Why the advocate never sees the orchestrator's framing: The advocate must independently assess what is effective. If the advocate knows which elements the orchestrator already values, the advocate's assessment collapses into confirmation of the orchestrator's existing beliefs rather than independent validation.

Model Selection — MUST Follow

• All three sub-agents MUST use a high-capability model. The quality ceiling of refinement is bounded by the reasoning depth of the sub-agents.
• Use general-purpose agent type for all three sub-agents.
• Preferred: Opus-tier or equivalent top-tier model (e.g., claude-opus-4.6).
• Acceptable fallback: If the preferred tier is unavailable (rate-limited, not on the user's plan), use the next-best available model (e.g., Sonnet-tier). Note the downgrade in the refinement log.
• Minimum floor: Do NOT use models below the Sonnet tier. If no model at or above Sonnet-tier is available, inform the user and do not proceed.

Parallel Execution — SHOULD Follow

• All three tribunal agents within each iteration are independent by design (information barriers guarantee this). Launch them in parallel to minimize wall-clock time.
• Do NOT parallelize across iterations. Each iteration's synthesis MUST complete before the next iteration begins.

Synthesis Discipline — MUST Follow

The orchestrator's synthesis step is where the majority of refinement value is created or destroyed. Sub-agent quality matters, but the orchestrator's judgment in reconciling and applying findings determines the outcome.

Processing order:

Process each agent's output in this specific order to prevent cognitive anchoring:

1. Researcher first — Provides unanchored domain knowledge. Note which findings suggest genuine gaps or improvements.
2. Advocate second — Establishes which elements are effective. Build the protection list (standout strengths that MUST be preserved) and the amplification list (high-impact elements to push further). Reading the advocate before the harsh critic prevents the orchestrator from entering a "fix everything" mindset that erodes effective elements.
3. Harsh critic third — With the protection list in mind, evaluate each finding. Flag any finding that targets a protected element for conflict resolution.

Only AFTER evaluating each agent's output in isolation, cross-reference for reinforcement and conflicts.

Why the advocate exists in the tribunal:

Without the advocate, iterative harsh critique systematically strips personality, voice, and effective unconventional choices from content — a form of regression to the mean. The advocate identifies what works so the orchestrator can protect it. This is not optional: the advocate is a structural corrective to the harsh critic's erosive pressure.

Advocate-critic conflict resolution:

When the harsh critic recommends changing an element the advocate identified as a standout strength:

• If the critic's finding is CRITICAL (severity 8): the orchestrator MUST find a way to fix the critical issue while preserving the strength. If preservation is impossible, document why and accept the strength loss.
• If the critic's finding is below CRITICAL (significant gap, weakness, or minor): the burden of proof is on the critic. Prefer the advocate's assessment unless the critic's evidence is compelling and specific. Protected elements are sticky — they require strong evidence to weaken.

Anti-bias protocol:

• Document which synthesis decisions were clear-cut (multiple agents agree, or evidence is unambiguous) vs. judgment calls (orchestrator chose between conflicting or ambiguous signals). This transparency enables the user to audit decisions.

Anti-pattern avoidance:

• Synthesis-by-averaging: Do NOT take the middle ground between agents when they conflict. Instead, identify the underlying concern behind each recommendation and ask: "Is there a solution that addresses ALL concerns simultaneously?" Only if genuine synthesis is impossible, choose the position with stronger evidence. Averaging dilutes all perspectives and produces mediocre output. True synthesis creates something that transcends all inputs rather than splitting the difference.
• Anchored revision trap: Do NOT limit changes to line-level edits of the prior version. If findings suggest structural reorganization, section reordering, or section removal, implement those structural changes. Each iteration MUST be willing to make bold changes, not just incremental tweaks.
• Semantic drift: Over multiple iterations, cumulative reinterpretation can cause the artifact to drift from its original intent without any single step being wrong. Re-validate every iteration against the original purpose, target audience, and constraints — not just against the prior iteration's version.
• Strength erosion: Over multiple iterations, the harsh critic's pressure can systematically erode effective unconventional elements until the content becomes bland and generic. Use the advocate's protection list to resist this force. If a protected element survives multiple iterations of harsh critique unscathed, it is likely genuinely effective.

Conflict resolution criteria (in priority order):

When agents' findings conflict, prefer the finding that:

1. Cites a specific, concrete flaw or piece of evidence.
2. Explains causation (why something fails or succeeds) rather than correlation (it seems off / it seems good).
3. Proposes a testable, verifiable improvement rather than a subjective opinion.

If multiple findings meet these criteria equally, prefer the finding from the agent with the most relevant perspective: the critic for content flaws, the advocate for content strengths, the researcher for domain practices.

Change tracking:

• Track all changes across iterations. Do NOT regress — never re-introduce issues fixed in prior iterations.
• Do NOT weaken or remove elements identified as standout strengths by the advocate unless a CRITICAL issue requires it.
• Apply the most impactful changes first. Minor polish comes last.

Iteration Limits — MUST Follow

• Minimum iterations: 2. The first iteration nearly always surfaces substantial improvements; the second catches issues introduced by the first round of changes.
• Maximum iterations: 5 (hard ceiling). Default: 3. Beyond 5 iterations, diminishing returns are consistently observed and cumulative context pressure increases the risk of synthesis errors.
• If the user supplies a custom maximum, clamp it to the range [2, 5].

Context Window Awareness — MUST Follow

• Before constructing the critic and advocate prompts, estimate whether the full content plus the prompt template plus response headroom fits within the model's context window.
• If content is too long for a single prompt, segment it as described in the Edge Cases section.
• Monitor the orchestrator's own context consumption across iterations. The orchestrator holds full content, all three sub-agent outputs, synthesis notes, protection lists, change tracking, and iteration history simultaneously. If orchestrator context pressure becomes a concern, summarize prior iteration details before proceeding.
• Use content length in tokens (not lines) as the sizing metric. As a rough heuristic, one line of dense prose is approximately 30–50 tokens.

Resource Awareness — SHOULD Follow

• A single invocation generates 3 sub-agent calls per iteration (or 2 if the researcher is dropped). At the default of 3 iterations, expect 7–9 Opus-tier sub-agent calls. With segmented content, the total is: (segments × 3) + (holistic iterations × 3).
• Before starting the first iteration, briefly inform the user of the expected scope: number of iterations, agents per iteration, and estimated total sub-agent calls.
• If resource constraints are a concern, the user MAY specify a lower max iterations (minimum 2) or a lower-tier model preference to reduce cost.

Procedure

Phase 1 — Preparation

1. Resolve the content input using the Content Resolution rules.
2. Read and internalize the content.
3. Identify: domain, content type, purpose, target audience.
4. Determine which quality dimensions matter most for this content type:
   • Completeness — All necessary topics covered?
   • Precision — Language concrete, specific, unambiguous?
   • Structure — Logically organized, easy to navigate?
   • Correctness — Claims accurate, internally consistent?
   • Actionability — Reader can act without guessing?
   • Conciseness — No unnecessary repetition or filler?
5. If the user specified focus areas, identify which quality dimensions they map to. Weight those dimensions above others throughout the process.
6. If the content references external files (e.g., "See ARCHITECTURE.md"), attempt to read them. Include relevant context in the critic's and advocate's domain framing. Do NOT pass external file contents to the researcher (information barrier).
7. Record the original purpose statement, audience, and constraints as the intent anchor. Re-validate against this anchor every iteration to prevent semantic drift.
8. Set the iteration counter to 0.

Phase 2 — Tribunal Launch

Launch all three sub-agents simultaneously (parallel). Replace all bracketed placeholders with actual values derived from Phase 1.

If the researcher was dropped due to diminishing returns (see 2A), launch only the harsh critic and advocate. Skip researcher-dependent steps in Phase 3 (steps 2 and 5) and treat the researcher novelty rate as 0% in Phase 4.

When constructing sub-agent prompts, follow these prompt construction conventions (derived from the writing-ai-instructions skill):

• Use imperative voice ("Evaluate X" not "You should evaluate X").
• Ban vague language ("improve", "better", "appropriate") — replace with specific, measurable terms.
• Use explicit conditionals with clear structure: [IF condition]: ... [END IF].
• Use MUST/MUST NOT/MAY modality to signal rule strength.
• Every instruction should be independently testable — an observer should be able to determine whether the agent followed it.

2A — Researcher

Launch a general-purpose agent (most capable available model) with a prompt built from this template:

You are a world-class expert in [DOMAIN] with deep knowledge of [CONTENT TYPE]
best practices.

Your task: independently research and report what constitutes excellence for
[CONTENT TYPE] intended for [AUDIENCE].

Produce a structured report with these sections:

1. ESSENTIAL QUALITIES — What properties MUST a high-quality [CONTENT TYPE]
   exhibit? Be specific and concrete.
2. COMMON FAILURE MODES — What mistakes do authors of [CONTENT TYPE] most
   frequently make? Why do these matter?
3. ADVANCED BEST PRACTICES — What separates exceptional [CONTENT TYPE] from
   merely adequate ones? Focus on techniques that require expertise to know.
4. STRUCTURAL PATTERNS — What organizational approaches work best for this
   content type? Why?
5. OVERLOOKED DIMENSIONS — What aspects or audiences are commonly neglected?

[IF focus areas specified]:
Prioritize the following quality dimensions above all others: [FOCUS AREAS].
Weight your findings heavily toward these dimensions.
[END IF]

[IF iteration == 0]:
Cover both fundamental and advanced aspects of excellence for this content type.
[END IF]

[IF iteration == 1]:
Prior research covered fundamentals. Focus exclusively on ADVANCED, SUBTLE, and
NON-OBVIOUS insights that require deep domain expertise to identify.
[END IF]

[IF iteration >= 2]:
Prior research covered fundamentals and advanced practices. Adopt a contrarian
stance: challenge conventional wisdom about [CONTENT TYPE]. Identify best
practices that are actually harmful in specific contexts, or unconventional
approaches that outperform standard ones. Question underlying assumptions.
[END IF]

For each finding, state your confidence: HIGH (clear evidence), MEDIUM (strong
inference), or LOW (possible concern). Do not present LOW-confidence findings as
definitive.

Prioritize depth over breadth. Five profound insights outweigh twenty surface
observations. Do NOT reference or propose changes to any specific document.
Report general findings only.

NEVER include the content being refined in the researcher prompt.

Researcher diminishing returns: The researcher never sees the content, so later iterations produce increasingly generic domain knowledge untethered from the actual artifact. This is an inherent tradeoff of the information barrier — allowing the researcher to see content would increase relevance but destroy independence. The novelty rate mechanism detects the diminishing returns downstream. If the researcher's novelty rate (see Phase 3) drops below 20% in two consecutive iterations, drop the researcher for subsequent iterations and run only the harsh critic and advocate. This optimization primarily applies when max iterations is 4 or higher; at the default of 3, the drop rarely triggers because the broad iteration-0 prompt nearly always produces high novelty.

2B — Harsh Critic

Launch a general-purpose agent (most capable available model) with a prompt built from this template:

You are a harsh, meticulous, and deeply knowledgeable critic specializing in
[DOMAIN]. You have zero tolerance for mediocrity and an expert eye for
[CONTENT TYPE].

Review this [CONTENT TYPE] and identify every flaw, gap, weakness,
inconsistency, and missed opportunity:

---
[INSERT FULL CURRENT CONTENT]
---

Produce a structured critique with findings sorted by descending severity:

1. CRITICAL ISSUES — Flaws that undermine the content's core purpose or would
   mislead the audience. For each: state what is wrong, why it matters, and
   what a correct version looks like.
2. SIGNIFICANT GAPS — Important topics, scenarios, or edge cases missing
   entirely. For each: state what is missing, why it matters, and where it
   belongs.
3. WEAKNESSES — Areas present but underdeveloped, vague, or substantially
   improvable. For each: state what is weak and how to strengthen it.
4. MINOR ISSUES — Polish items: formatting, word choice, organization. Batch
   related items together.
5. OVERALL VERDICT — One blunt paragraph: current quality level, readiness for
   production use, and the single most impactful improvement remaining.

[IF focus areas specified]:
Prioritize the following quality dimensions above all others: [FOCUS AREAS].
Weight your critique heavily toward these dimensions. A flaw in a focus
dimension is one severity level higher than it would otherwise be.
[END IF]

[IF iteration == 0]:
This is the first review. Evaluate comprehensively across all severity levels.
[END IF]

[IF iteration > 0]:
This is iteration [N] of refinement. Prior iterations addressed earlier
feedback. Focus on what REMAINS problematic despite prior changes. If the
content is now genuinely strong, say so — do not invent issues to justify your
role. Your credibility depends on accuracy, not volume of findings.
[END IF]

Be ruthless. Do not hedge, soften, or qualify. Prioritize findings by impact —
the most damaging issues first.

If you find no significant issues, explicitly state "No significant issues
found" rather than inventing minor ones to appear thorough. Your credibility
depends on accuracy, not volume. For each finding, state your confidence: HIGH
(clear evidence), MEDIUM (strong inference), or LOW (possible concern).

2C — Advocate

Launch a general-purpose agent (most capable available model) with a prompt built from this template:

You are an intelligent, deeply knowledgeable advocate and encouraging reviewer
specializing in [DOMAIN]. You have a sharp eye for what works well in
[CONTENT TYPE] and a talent for recognizing effective patterns that others
overlook.

Review this [CONTENT TYPE] with an honest but positive lens. Your role is NOT
to ignore problems — it is to identify and champion what is genuinely effective:

---
[INSERT FULL CURRENT CONTENT]
---

Produce a structured assessment:

1. STANDOUT STRENGTHS — Elements that are exceptionally well-executed and MUST
   be preserved during any revision. For each: state what works, WHY it works
   (the underlying principle that makes it effective), and how important it is
   to preserve this element.
2. HIGH-IMPACT ELEMENTS — Strong elements that could be pushed even further for
   greater effect. For each: state what is already working and specifically how
   to amplify it without breaking what makes it work.
3. EFFECTIVE PATTERNS — Recurring approaches, structures, or techniques in the
   content that work well. For each: identify the pattern, explain why it
   succeeds, and note where it could be applied more broadly in the content.
4. UNDERAPPRECIATED QUALITIES — Subtle strengths the author may not realize are
   effective. For each: explain the quality and why it matters to the audience.
5. OVERALL ASSESSMENT — An honest positive evaluation: what is genuinely
   excellent about this content, and what is its single greatest asset that MUST
   be preserved above all else?

[IF focus areas specified]:
Prioritize the following quality dimensions above all others: [FOCUS AREAS].
Weight your assessment heavily toward strengths in these dimensions.
[END IF]

[IF iteration == 0]:
This is the first review. Evaluate comprehensively — identify both broad and
subtle strengths across the full content.
[END IF]

[IF iteration > 0]:
This is iteration [N] of refinement. Prior iterations may have changed the
content. Evaluate the CURRENT version on its own merits. If previously
identified strengths have been weakened or removed, flag this explicitly as
"STRENGTH EROSION" with HIGH severity. If new strengths emerged from prior
revisions, note them as new additions.
[END IF]

Be honest. Do not manufacture praise — only highlight genuine effectiveness.
Your credibility depends on identifying real strengths, not flattering the
author. For each finding, state your confidence: HIGH (clearly effective),
MEDIUM (likely effective), or LOW (potentially effective but uncertain).

Advocate erosion detection limitations: The advocate never sees prior advocate output (information barrier). It detects erosion by evaluating the current version independently — missing or obviously weakened elements are detectable, but subtle degradation of nuanced qualities may not be. This is an inherent tradeoff. The orchestrator SHOULD cross-reference the current advocate's protection list with prior iterations' protection lists to catch subtle erosion that the advocate alone might miss.

Phase 3 — Synthesis

1. Collect outputs from all three sub-agents (or harsh critic and advocate only if the researcher was dropped).
2. Process researcher output (do this FIRST to prevent anchoring on content evaluations). Skip this step if the researcher was dropped.
   • For each finding, categorize as: novel (not addressed in current content), already addressed, or not applicable.
   • Calculate the researcher novelty rate: novel / total findings.
   • Rank novel findings by potential impact on the quality dimensions identified in Phase 1 (weight focus-area dimensions 2× if specified).
3. Process advocate output (do this SECOND to establish the protection and amplification lists before reading the harsh critique):
   • Build the protection list: all STANDOUT STRENGTHS with HIGH or MEDIUM confidence. These elements MUST NOT be weakened or removed unless a CRITICAL issue from the harsh critic requires it.
   • Build the amplification list: all HIGH-IMPACT ELEMENTS with HIGH or MEDIUM confidence. These are positive change candidates.
   • Record EFFECTIVE PATTERNS for application elsewhere in the content.
   • If iteration > 0, check for STRENGTH EROSION flags. If the advocate reports previously identified strengths were weakened, treat as a per-dimension regression signal.
4. Process harsh critic output (do this THIRD, with the protection list in mind):
   • Record the severity distribution: count of critical, significant, weakness, and minor findings.
   • Record per-dimension severity: for each quality dimension from Phase 1, note the count and severity of findings that affect it. When a finding spans multiple dimensions, assign it to the dimension it most directly impacts. If ambiguous, assign to all affected dimensions at the finding's stated severity.
   • For each finding, check whether it targets a protected element. If so, flag it for advocate-critic conflict resolution (see Synthesis Discipline).
   • For each critical issue and significant gap NOT targeting a protected element, draft a specific change.
   • For weaknesses, draft changes where they align with researcher findings (reinforcement signals high priority).
   • Batch minor issues for a single polish pass.
5. Cross-reference (skip researcher portion if researcher was dropped): Identify areas where multiple agents independently flagged the same concern or strength. Treat findings flagged by all three agents as highest priority. Note the specific combinations:
   • All three agents agree → highest priority.
   • Harsh critic + researcher agree → strong signal for a gap or flaw.
   • Advocate + researcher agree → strong signal for an effective pattern.
   • Harsh critic + advocate agree on the same element → the element has both strengths and flaws; handle with surgical precision to fix the flaw without destroying the strength.
6. Resolve advocate-critic conflicts: For each flagged conflict from step 4, apply the advocate-critic conflict resolution protocol from the Synthesis Discipline constraint.
7. Re-validate against intent anchor: Before applying changes, re-read the original purpose statement, audience, and constraints recorded in Phase 1. Verify that the planned changes do not drift from the original intent.
8. Retain the current content version as the rollback target in case degradation is detected in Phase 4.
9. Apply changes in priority order:
   1. Cross-referenced findings (all three agents independently agree).
   2. Critical issues from the harsh critic (not targeting protected elements).
   3. Resolved advocate-critic conflicts (preserving strength where possible).
   4. Significant gaps from the harsh critic.
   5. Novel researcher findings with high impact.
   6. Amplification of advocate's high-impact elements.
   7. Weaknesses reinforced by researcher findings.
   8. Application of advocate's effective patterns to new areas.
   9. Remaining weaknesses.
   10. Minor issues.
   • After each change, verify it does not regress a fix from a prior iteration.
   • After each change, verify it does not weaken a protected element.
   • Verify no individual quality dimension regressed (per-dimension check).
   • For each change, note whether it was clear-cut or a judgment call.
10. Increment the iteration counter.
11. Record the iteration summary for the refinement log, including:
    • Severity distribution from the harsh critic (aggregate and per-dimension)
    • Researcher novelty rate (or "researcher dropped" if applicable)
    • Advocate findings: strengths protected count, amplifications applied, strength erosion flags (if any)
    • Advocate-critic conflicts and their resolutions
    • Count of changes applied vs. findings discarded (with reasons)

Phase 4 — Convergence Check

Minimum iteration gate: If the iteration counter is less than the minimum (default 2), return to Phase 2 unconditionally. Do NOT evaluate stop conditions until the minimum is reached.

After the minimum is met, evaluate whether another iteration is warranted.

Severity score formula:

severity_score = (critical × 8) + (significant_gaps × 4) + (weaknesses × 2) + (minor × 1)

The weights reflect that critical issues make content actively harmful (8×), significant gaps make it materially incomplete (4×), weaknesses reduce effectiveness (2×), and minor issues affect polish only (1×). These weights are heuristic — the orchestrator MAY adjust them if domain context warrants it (e.g., in safety-critical documentation, weaknesses may warrant 4× weight).

Severity scores are approximate due to model stochasticity — different critic invocations may rate identical content differently. Treat degradation and stagnation as signals to investigate, not automatic triggers. Inspect the specific findings before reverting or stopping.

STOP if ANY of these conditions is true:

• Iteration counter ≥ maximum (default 3).
• Harsh critic found 0 critical issues AND 0 significant gaps, AND the researcher's novelty rate is below 20% (or the researcher was dropped).
• Harsh critic explicitly assessed the content as strong or excellent without manufacturing concerns.
• Degradation detected: The severity score increased compared to the prior iteration. Inspect the specific findings to confirm real degradation (not stochastic noise). If confirmed, revert to the retained rollback version from Phase 3, record the degradation event in the refinement log, and stop. This check applies only when two or more severity scores exist (the first iteration establishes the baseline).
• Strength erosion detected: The advocate flagged STRENGTH EROSION in the current iteration — previously identified standout strengths were weakened or removed. Inspect the specific erosion findings. If confirmed, revert to the retained rollback version, record the erosion event, and stop.
• Oscillation detected: Changes applied in this iteration revert changes from a prior iteration. This indicates the process is cycling between competing solutions rather than converging. Stop and present both versions to the user with an explanation of the competing perspectives.
• Stagnation detected: The severity score did not decrease by more than 10% across two consecutive iterations, indicating the process is not making meaningful progress.
• Per-dimension regression: Any individual quality dimension worsened between iterations without documented justification, even if the aggregate score improved.

CONTINUE if ALL of these conditions are true:

• Iteration counter is less than the maximum.
• Harsh critic found at least 1 critical issue or significant gap, OR the researcher's novelty rate is at or above 20%.
• Severity score decreased by more than 10% compared to the prior iteration.
• No individual quality dimension regressed without justification.
• No unresolved strength erosion flags from the advocate.

DEFAULT: If neither all STOP conditions are false nor all CONTINUE conditions are true (e.g., severity score decreased but by less than 10% on a single iteration), treat this as a soft stagnation signal. Record it. If the next iteration also triggers this default, stagnation is confirmed and STOP applies. On the first occurrence, continue to Phase 2.

If continuing, return to Phase 2 with the updated content. If stopping, proceed to Phase 5.

Phase 5 — Delivery

1. If the content originated from a file: a. Present a diff summary showing the key changes between the original and the refined version. b. If the host environment supports interactive confirmation, wait for user approval before writing. If not, proceed with the write. c. Write the updated content to the file.
2. If the content was provided inline, present the refined content directly.
3. Present the refinement log (all iterations).
4. Present the convergence rationale.
5. If the process hit the iteration cap with unresolved critical issues, flag them explicitly and recommend further action to the user.

Validation

Pass Conditions

• Minimum 2 iterations completed.
• All critical issues identified across all iterations were either resolved or explicitly justified as not applicable (with rationale documented).
• Final harsh critic assessment contains 0 critical issues and 0 significant gaps.
• No unresolved strength erosion flags from the advocate.
• Content is internally consistent — no contradictions introduced by merging feedback from different iterations.
• Information barriers were maintained throughout every iteration.

Failure Conditions

Failure conditions take precedence over pass conditions when both apply:

• Maximum iterations reached with unresolved critical issues: deliver the best version achieved, flag every unresolved issue, and inform the user.
• A sub-agent fails to produce structured output: re-prompt once with tighter formatting instructions. If the second attempt also fails, proceed with the output from the functioning sub-agents and note reduced confidence in the refinement log.

Edge Cases

• Content already excellent — If the first harsh critic finds no critical issues or significant gaps, still complete the minimum 2 iterations. The researcher may surface non-obvious improvements, and the advocate may identify amplification opportunities the critic would not think to look for.
• Agents fundamentally disagree — Apply the conflict resolution criteria from the Synthesis Discipline constraint. Document the disagreement and resolution rationale in the refinement log.
• Advocate and harsh critic target the same element — This is the most informative signal in the tribunal: the element is simultaneously strong and flawed. Apply surgical precision — fix the flaw without destroying the strength. Document the resolution.
• Very long content (estimated over 60K tokens) — Segment into logical sections. Run a single iteration per segment (no minimum for segments), then reassemble and run a 2-iteration holistic pass on the full content. Inter-section consistency issues (terminology, cross-references) are addressed during the holistic pass. Total sub-agent calls are bounded by: (segments × 3) + (holistic iterations × 3).
• Embedded code in text content — Code blocks (YAML frontmatter, JSON examples, shell commands) embedded in text content are in-scope for refinement of their surrounding documentation but out-of-scope for execution or compilation verification. The critic MAY flag obviously incorrect code syntax; the advocate MAY champion effective code examples. Treat code correctness findings at MEDIUM confidence unless the agent provides specific evidence.
• Content size growth — If the refined content exceeds 150% of the original size without corresponding quality improvement, flag this in the refinement log and recommend the user review whether the growth is justified.
• Partial rollback — When degradation is detected (Phase 4), full reversion is the default. However, if the orchestrator can clearly identify that specific changes caused the degradation while others were beneficial, it MAY perform a partial rollback: revert the harmful changes while preserving the beneficial ones. Document the rationale for partial vs. full rollback.
• Degradation detected — See Phase 4. Revert to the version from the prior iteration and stop.

Examples

Example 1 — Refining a Copilot Agent Skill

User input: #refinement-tribunal features/skills/testing-standards/SKILL.md

Phase 1 — Preparation:

• Reads the file (content resolution: path with extension, file exists).
• Domain = "software testing methodology". Content type = "Copilot agent skill". Audience = "AI coding assistants performing test generation and review."
• No focus areas specified; all quality dimensions weighted equally.

Phase 2, Iteration 1 — Tribunal Launch:

Researcher prompt (instantiated):

You are a world-class expert in software testing methodology with deep knowledge of Copilot agent skill best practices. Your task: independently research and report what constitutes excellence for Copilot agent skills intended for AI coding assistants performing test generation and review. [Sections 1–5 as defined in template.] Cover both fundamental and advanced aspects of excellence for this content type. Prioritize depth over breadth. Five profound insights outweigh twenty surface observations. Do NOT reference or propose changes to any specific document. Report general findings only.

Harsh critic prompt (instantiated):

You are a harsh, meticulous, and deeply knowledgeable critic specializing in software testing methodology. You have zero tolerance for mediocrity and an expert eye for Copilot agent skills. Review this Copilot agent skill and identify every flaw, gap, weakness, inconsistency, and missed opportunity: [full skill content inserted here]. [Sections 1–5 as defined in template.] This is the first review. Evaluate comprehensively across all severity levels. Be ruthless. Do not hedge, soften, or qualify.

Advocate prompt (instantiated):

You are an intelligent, deeply knowledgeable, and encouraging reviewer specializing in software testing methodology. You have a sharp eye for what works well in Copilot agent skills. Review this Copilot agent skill with an honest but positive lens. [full skill content inserted here]. [Sections 1–5 as defined in template.] This is the first review. Evaluate comprehensively — identify both broad and subtle strengths across the full content. Be honest. Do not manufacture praise.

Phase 3, Iteration 1 — Synthesis:

• Researcher (processed first): 12 findings total. 8 novel (novelty rate: 67%). Highest-impact novel finding: mutation-testing concepts not covered in the skill's anti-pattern catalog.
• Advocate (processed second): 4 standout strengths (protection list built), 3 high-impact elements (amplification list built), 5 effective patterns identified. Greatest asset: the skill's constraint-driven structure forces consistent test quality.
• Harsh critic (processed third): 2 critical issues (ambiguous constraint language, missing edge case for framework mocking), 3 significant gaps, 5 weaknesses, 4 minor issues. Severity score: (2 × 8) + (3 × 4) + (5 × 2) + (4 × 1) = 42. One significant gap targets a protected element — flagged for conflict resolution.
• Cross-reference: All three agents independently flagged the testing-framework mocking gap. Elevated to highest priority.

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