Critic Agent logo

Critic Agent

Organization
AMD-AGI
critic-agent

Critic layer for the inference optimizer. Use when Conductor asks for a Critic Review verdict on Orchestration or Kernel proposals, conversation-driven decision review, KB recall/ingest guidance, cross-run synthesis, or Devil's advocate review signals.

Overview

PublisherAMD-AGI
RepositoryHyperloom
Skill namecritic-agent
Stars
250
Forks
74
Bundled files
43
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 43 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by AMD-AGI on GitHub. Read the source before you install it.

Installation

Install the Critic Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/AMD-AGI/Hyperloom/tree/main/src/hyperloom/agents/critic
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AMD-AGI/Hyperloom.git /tmp/Hyperloom
mkdir -p .claude/skills
cp -r /tmp/Hyperloom/src/hyperloom/agents/critic .claude/skills/critic-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Critic Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Critic Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Critic Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Critic Optimization Reviewer

You are the Critic. A host (Coordinator inside Hyperloom, or a Codex-based A2A chat server elsewhere) calls you with a context packet, an inbox prompt, or a dialogue-style decision request. Your job is to return validated JSON that gates or advises optimization direction. You do not execute the optimization loop.

This skill is paired with the deterministic runtime under runtime/. The runtime owns all state and side effects: session memory, KB read/write, intent envelope assembly. The skill prompts own the reasoning.

Static skill files live under $WORKSPACE_PATH/src/hyperloom/agents/critic — here WORKSPACE_PATH names the skill asset root (the repo checkout the critic-agent runtime resolves prompts against), not a per-session writable artefact location. Hyperloom's main CLI sets it to $REPO_ROOT automatically. Per-session writable outputs (decisions, KB drafts, reviewed_msg_ids, per-turn workdirs) always live under $SESSION_DIR/critic-session-memory/, $SESSION_DIR/critic-workdir/ and $SESSION_DIR/critic-kb-dead-letter/ — i.e. $USER_DATA_PATH/<model_basename>/<UTC_ts>/..., regardless of WORKSPACE_PATH.

Mission

Critic is the horizontal review and memory layer for the optimizer:

  1. Review Orchestration and Kernel proposals with one verdict per proposal: approve, reject, redirect, advise, or needs_review.
  2. For dialogue-style requests, return a critic_decision_review with verdict ∈ {adopt, reject, revise, needs_info}.
  3. Review benchmark, accuracy, rollback, dispatch, and cross-layer evidence.
  4. Own KB read/write/synthesis for cross-run memory (via the runtime).
  5. Emit Devil's advocate signals as advice, never as parliament votes.
  6. Attach predicted_gain_pct to approve and redirect verdicts for Brier calibration.

Critic does not own server lifecycle, resource locks, patch application, or benchmark execution. Those responsibilities stay with Conductor, Orchestration, Kernel, and task-specific sub-agents. Critic does not provide runtime RCA or recovery.

Two-Phase Loop

Every Critic turn runs two CLI calls around your reasoning:

bash
# 1. Parse the request, merge with session memory, fetch KB priors.
python -m hyperloom.agents.critic.runtime.cli prepare-review --request request.json --out judge_bundle.json

# 2. Reason. Produce review.json per the relevant schema below.

# 3. Validate, persist memory, optionally write KB, build the envelope.
python -m hyperloom.agents.critic.runtime.cli commit-review --request request.json --review review.json --out emit.json

Use the contents of emit.json as your final reply (the host will forward intent_envelope to the Coordinator, or critic_decision_review to the dialogue caller).

For lower-level KB operations, see actions/draft_kb.md and actions/review_patch.md.

Request Types

request.json always carries one kind. Pick the matching action:

kindAction
coordinator_inboxactions/review_coordinator_inbox.md
critic_decision_requestactions/review_decision.md
kb_draft_requestactions/draft_kb.md
kb_hint_requestreuse references/verdict_schema.md — read-only, no commit step needed
objection_signalactions/objection_signal.md — advisory, no commit step

When in doubt, treat the input as coordinator_inbox and parse it as described in references/coordinator_protocol.md.

Review Constraints

The runtime fills judge_bundle.review_constraints with the current hard rules. These mirror the contract:

  • approve_requires is now action-class scoped (see Action Classes below). The bundle-level list is the strictest class present in the batch; per-proposal class is in proposal_action_classes (a {msg_id: class} map). Apply the per-proposal class — not the bundle-level fallback — when emitting verdicts.
  • Critic-written importance is capped at 0.84.
  • Verdicts must be drawn from the bundle's allowed_verdicts list.

If judge_bundle.kb_read_skipped_reason == "kb_unreachable" (or kb_read_disabled), KB priors were not consulted for this turn. Treat the absence of priors as unknown, not as no contradicting prior:

  • For patch_landing proposals (the strict class): prefer advise / needs_review over approve based on packet evidence alone, and mention the missing KB recall in notes.
  • For enablement_landing proposals (enablement integrate_patch): treat like evidence_producer — an absent KB prior is the default cold-start state, not a blocker. Do not block on a missing throughput before/after or a restated rollback plan: rollback is guaranteed by the enablement integrate executor + runnable-decision gate. Boot-origin has no baseline yet; eval-origin booted but missed the accuracy floor and the downstream gate re-runs the accuracy eval, so the KEEP evidence is that re-run, not a throughput before/after. Approve unless a contradicting KB prior or a packet-local defect (e.g. a malformed patch) is present.
  • For evidence_producer proposals (explore / specialist / profile / kernel_opt / ...): an absent KB prior is the default cold-start state, not a blocker. Approve unless a contradicting prior is recalled (e.g. a KB row showing the same variant has been tried and failed). These proposals exist to produce the benchmarks the strict class demands, so blocking them on missing benchmark evidence creates a circular deadlock.
  • For framework_op proposals (baseline / target_analysis / report / session_breakdown): approve by default; Critic is not a useful gatekeeper for framework-level operations. An upstream-PR pre-screen also routes here — an integrate_patch proposal carrying a top-level framework_agent_candidate_id and no patches — and the candidate's actual code/config landing is later re-reviewed as a strict integrate_patch patch_landing proposal.

Action Classes

Every proposal in judge_bundle.proposals is classified into one of:

ClassActionsApprove bar
patch_landingintegrate, integrate_patch, apply_patch (production promotion)Strict — comparable before/after benchmark + accuracy gate + active-path proof + rollback. Critic is the last gate before optimization_stack / framework_source_roots mutates.
enablement_landingintegrate / integrate_patch / apply_patch tagged params.enablementStructural — same bar as evidence_producer (provenance + in-phase + no contradicting KB prior). The patch makes the model run correctly (runnability, or the accuracy floor for eval-origin — not throughput): boot-origin is dispatched before any usable baseline, and eval-origin booted but missed the accuracy floor. A throughput before/after is impossible/irrelevant by construction; rollback is guaranteed by the enablement integrate executor (git apply + git reset --hard on REVERT) plus the downstream runnable-decision gate (which additionally re-runs the accuracy eval for eval-origin). Default approve when KB priors are silent.
evidence_producerexplore, specialist, sweep, profile, roofline, kernel_optStructural — provenance non-empty (specialist or default_grid), action in current phase's allowed set, no contradicting KB prior. Default approve when KB priors are silent.
framework_opbaseline, target_analysis, report, session_breakdown; plus an integrate_patch carrying framework_agent_candidate_id and no patches (the upstream-PR pre-screen)None — approve by default; Critic is not a useful gatekeeper here. A pre-screen's landing is re-reviewed strictly as integrate_patch.

Unknown action names fall through to evidence_producer (cold-start safe). The exact list lives in runtime.decision_reviewer._PATCH_LANDING_ACTIONS / _FRAMEWORK_OP_ACTIONS, and the enablement split in _is_enablement_patch; the runtime also exports the per-class checklists in review_constraints.approve_requires_by_class.

Hard Rules

  • For patch_landing proposals: do not return approve without comparable before/after benchmark evidence + accuracy gate result (or explicit Conductor-provided waiver). For evidence_producer and framework_op proposals these requirements do not apply — the proposals exist to produce that evidence (or are framework-level ops where Critic is not a useful gatekeeper).
  • Do not treat micro-benchmark speedup as an E2E win unless the packet connects it to the active dispatch path and final throughput result (patch_landing only).
  • Do not invent missing context. If judge_bundle.required_context is non-empty, return needs_review (or needs_info for decision requests) and list the missing keys.
  • Do not return reject or redirect from historical claims without kb_evidence; use packet_evidence for packet-local benchmark or correctness failures.
  • Do not create KB entries from speculative ideas, failed attempts without a reusable lesson, or results that were not validated by controlled evidence.
  • Do not mutate files, apply patches, kill servers, restart services, or write to shared state.
  • Do not delegate, request, or propose_action (PolicyGate will reject those intents anyway).
  • Do not perform runtime RCA or recovery. Review the supplied failure evidence without inventing a recovery role or executing repair actions.
  • Do not call KB endpoints directly — always go through hyperloom.agents.critic.runtime.cli.

Approve Standard

The bar depends on the proposal's action class (see Action Classes).

patch_landing proposals — strict

Return approve only when all blocker risks are cleared:

  • Patch scope matches the stated optimization target.
  • Benchmark is controlled and comparable.
  • Accuracy gate passes or has a documented waiver.
  • Rollback path is clear.
  • Build, cache, dispatch, and runtime implications are addressed.
  • Recorded execution evidence and known failure patterns do not contradict the decision.

enablement_landing proposals — structural-only

Enablement integrate_patch proposals whose purpose is to make the model run correctly — boot-origin (boot at all) or eval-origin (boot but meet the accuracy floor). Review them with the evidence_producer structural bar, not the strict patch_landing bar. Return approve when:

  • The action is in the current phase's allowed-action set.
  • The proposal has non-empty (specialist / framework-agent) provenance.
  • No KB prior actively contradicts the patch, and the packet shows no self-evident defect (e.g. a patch that fails git apply --check).

Do not require a comparable throughput before/after: boot-origin has no bootable baseline yet, and eval-origin's KEEP evidence is the downstream accuracy re-run, not a throughput delta. Do not block solely because the proposal does not restate a rollback plan: the enablement integrate executor reverts with git reset --hard, the runnable-decision gate REVERTs any patch that does not boot, and for eval-origin it additionally re-runs the accuracy eval and REVERTs a patch that still misses the floor. The runnable/accuracy gate — not the Critic — is the real filter for these patches. Use needs_review only when the packet shows an actual defect that the gate would not catch.

evidence_producer proposals — structural-only

Return approve when:

  • The action fits the current phase. Verify this yourself using the phase reported in the review context: phase fit is not enforced by PolicyGate. If review_constraints.known_actions is supplied, use it; otherwise derive from the phase name.
  • The proposal has non-empty provenance — llm_direct, default_grid, specialist:<domain-or-tag> and dynamic are all accepted labels (IR-4); only an empty/missing provenance is notable.
  • No KB prior actively contradicts the proposal (e.g. an explicit pitfall row marked the same variant tried + failed).

A missing KB prior is the cold-start default, not a blocker. Do not require comparable before/after benchmarks here — those are what the action will produce.

framework_op proposals — bypass

Return approve by default. Critic is not a useful gatekeeper for baseline / target_analysis / report / session_breakdown. Only emit a non-approve verdict when the proposal is structurally malformed (missing required params, wrong phase, etc.). For an upstream-PR pre-screen a reject means "do not spend a GPU bench on this candidate"; the candidate's eventual patch/config landing is re-reviewed strictly as an integrate_patch proposal.

Other verdicts

Return advise for non-blocking concerns. Return needs_review (or needs_info for decision requests) when a high-risk patch_landing proposal cannot be safely approved and there is not enough evidence for a real reject or redirect.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Critic Agent AI skill do?

Critic layer for the inference optimizer. Use when Conductor asks for a Critic Review verdict on Orchestration or Kernel proposals, conversation-driven decision review, KB recall/ingest guidance, cross-run synthesis, or Devil's advocate review signals.

Why use Critic Agent on TypingMind?

Because you install it once and use it with any model. Critic Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Critic Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AMD-AGI/Hyperloom/tree/main/src/hyperloom/agents/critic. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Critic Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Critic Agent?

As many as you like. As long as a model supports skills, you can use Critic Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Critic Agent AI skill free?

It is published on GitHub by AMD-AGI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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