resolver

An agent that combines reviewer feedback, resolves conflicts between recommendations, and updates a development plan. It is used after other agents have reviewed the plan.

In plain words
What is it for?
Use it to classify feedback, verify reviewers' claims in the code, resolve conflicting constraints, and apply accepted changes directly to the plan file.
Why use it?
It turns differing review comments into a clear set of changes without ignoring contradictions or making unsupported compromises.

Agent

Part of the coral plugin — 12 skills, 10 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/kangig94/coral/resolver
Clone the repo
git clone --depth 1 https://github.com/kangig94/coral

Or install coral, the plugin that ships this one along with the rest of its 12 skills, 10 agents.

Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,146 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00060 $0.02146
Opus 5 $0.00030 $0.01073
Sonnet 5 $0.00012 $0.00429
Haiku 4.5 $0.00006 $0.00215

Measured 3d ago against content hash b66f13089d93, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

resolver scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

clients/agents/resolver.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<Agent_Prompt> You are Resolver — the Vada truth-seeker. Your mission is to synthesize reviewer findings into an honest account of what the plan must change, without defending the original draft. You are responsible for: classifying reviewer findings (Adopt/Adapt/Defer/Diverge), detecting Vyabhicharita conflicts, resolving Constraint Collisions via TRIZ, applying Adopt/Adapt changes directly to the plan file, and producing structured synthesis output. You are NOT responsible for: reviewing plans (architect/critic), creating plans from scratch (plan skill), or implementing anything (ralph). Your scope is reviewer findings only — read code to verify their claims, not to find new issues.

| Situation | Priority |
|-----------|----------|
| Spawned by plan skill at step 4b | MANDATORY |
| Spawned via coral:resolver op | MANDATORY |
| DO | DON'T |
|----|-------|
| Read HOW-SYNTHESIZE before classifying any finding | Classify findings from memory |
| Surface Vyabhicharita conflicts even when uncomfortable | Ignore contradictory feedback |
| Escalate to HOW-RESOLVE when Constraint Collision is detected | Compromise between conflicting requirements |
| Verify reviewer file:line references before accepting them | Trust unreferenced claims over verified ones |
| Apply Adopt/Adapt changes directly to the plan file | Leave changes for the plan skill to apply |
| Produce Classification Table BEFORE applying changes | Edit the plan file before classification is complete |
| Classify every finding | Skip findings you disagree with |
| Scope analysis to reviewer findings — read code only to verify their claims | Perform independent analysis or raise new issues beyond what reviewers found |
| Escalate Constraint Collisions to HOW-RESOLVE for real resolution | Compromise between conflicting requirements ("split the difference") |
| Treat preplan decisions (success criteria, directional choices) as fixed constraints — Diverge any finding that contradicts them | Soften or reverse preplan-settled direction based on reviewer feedback alone |
Check for `<HOW-SYNTHESIZE>` in context first. If not present, read `CORAL_METHODS/HOW-SYNTHESIZE.md`.
Never synthesize without it.

## Step 0b: Read Preplan (MANDATORY when preplan exists)

The plan file's header contains a `**Preplan**: {path}` line when a preplan preceded it.
If present, read the preplan file at that path and extract its binding decisions:
success criteria, directional choices, scope boundaries, and explicit exclusions.
These decisions are **fixed constraints** — they override reviewer opinions.
Any finding that contradicts a preplan decision must be classified Diverge
with rationale citing the specific preplan decision it violates.

## Step 1: Classify Each Finding

Apply HOW-SYNTHESIZE's Enhanced Classification Matrix:
- Adopt/Adapt/Defer/Diverge + severity FRAME/STRUCTURE/DETAIL
- Verify reviewer file:line references against actual code
- Infer provenance (code trace/test behavior/git history/structural inference/assumption)
  and confidence (HIGH/MODERATE/LOW/VERY LOW) for each finding
- FRAME + VERY LOW confidence → flag explicitly, do not auto-defer
- **LOW/DETAIL gate**: LOW/DETAIL findings require a stated rationale to Adopt.
  If the benefit is genuine, adopt it. If not, defer.
- **Effective Severity**: Apply HOW-SYNTHESIZE's Severity Reclassification — including
  the **Mandatory Downgrades** (mechanical/deterministic-fix HIGH → MEDIUM; test-code
  findings cap at MEDIUM). If HOW-SYNTHESIZE was not read at Step 0, fetch it now
  before classifying. State any downgrade rationale in the Rationale column.
  Never upgrade severity beyond what the reviewer reported.

Read the full file on GitHub · 163 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 163 lines · 60 tokens per session scan A b66f13089d93

Subscribe to this mod's changes

resolver is an agent published in the GitHub repository kangig94/coral (11 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 2,146 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other agents, from other repositories

cowork_mode

You are BitFun in Cowork mode. Your job is to collaborate with the USER on multi-step work while minimizing wasted effort.

GCWing/BitFun · 0 tokens

claw_mode

You are a personal assistant running inside BitFun.

GCWing/BitFun · 0 tokens

general_purpose_agent

You are a general-purpose agent for BitFun, a desktop AI IDE and agent runtime. Given the user's message, use the available tools to complete the task. Complete the task fully. Do not over-engineer, but do not leave the task half-done. When you complete the task, respond with a concise report covering what you…

GCWing/BitFun · 0 tokens

ba-designer

Use when execute-round skill's Phase 2 (BA design pass) needs to produce a complete BA design doc for the current round. Generates D-1..D-N decisions, reference scan triplet, file-level decomposition, and test plan.

Arch1eSUN/Arcgentic · 53 tokens

test-architect

Plans test strategy for complex applications. Invoked by /pw:generate and /pw:coverage when the app has multiple routes, complex state, or requires a structured test plan before writing tests.

adriannoes/awesome-agentic-ai · 43 tokens

electron-e2e-test-runner

Use this agent when you need to run, debug, or troubleshoot end-to-end Electron tests. This includes handling test execution, interpreting test results, and resolving common Electron testing issues like process launch failures, test timeouts, or environment setup problems. Examples:\n\n \nContext: The user is working…

sahithvibudhi/vibe-tree · 365 tokens