How do I install the Critic skill?

Run npx skillmds@latest add hashgraph-online/critic in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Critic safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Critic?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Critic free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Critic?

hashgraph-online (@hashgraph-online) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Critic by hashgraph-online — Agent Skill

Name: Critic
Rating: 4.666666666666667 (3 reviews)
Author: hashgraph-online

Critic

HarnessX Critic (Tier 2.1) — adversarial review of evolved-skill proposals against trace evidence. Detects reward hacking and manifest/evidence contradictions. Out-of-band LLM counterpart to the in-loop deterministic critic in src/evolve/critic.rs.

File contents

Critic — Adversarial Proposal Review

In-loop vs out-of-band. epic-harness forbids external LLM calls from production, so the reflect loop ships a deterministic critic (src/evolve/critic.rs) that gates seeding when reward hacking is suspected. THIS skill is the out-of-band LLM version a meta-agent or human runs during /evolve review for the cases the deterministic check cannot catch (non-local effects, manifest/evidence nuance).

When to Trigger

During /evolve review of newly seeded skills
When reward_hacking_suspected is true in metrics
After a seesaw-regression round, before re-proposing

Process

1. Gather the proposal + evidence

Read the evolved skill proposal(s) from this round
Read the EditManifest (edit_type, target, intended_effect, predicted_impact)
Read the round's TaskDigests (outcome, failure_categories, evidence_excerpts)
Read recent score_history dimension_averages (tool_success, output_quality, execution_cost)

2. Falsify the manifest (paper §4.3, Table 9)

For each proposal, ask: does the trace evidence support the predicted_impact?

If the manifest claims "Lift avg_score_with" but output_quality is regressing → Reject
If the manifest claims a tool fix but the implicated component shows no change → Warn
If the evidence corroborates the predicted effect → Approve

3. Reward-hacking cross-check

Is execution_cost rising while output_quality falls across the window?
Could the skill be gaming a metric (fewer tool calls inflating cost score) rather than improving outcomes?
If yes, the skill must NOT ship — flag for the rejected buffer.

4. Non-local effect scan

Will this skill's guard rules conflict with existing skills (overlapping triggers, contradictory rules)?
Does it interact with shared state (context, memory, control) in a way the manifest didn't account for?

Anti-Rationalization

Excuse	Rebuttal	Do instead
"The score went up, so it works"	Score can rise via metric gaming	Verify the outcome improved, not just the score
"The seesaw passed, it's safe"	Seesaw is coarse; sub-threshold coupling evades it	Check dimension deltas, not just aggregate pass
"It's just a prompt tweak"	Prompt edits have non-local effects on shared context	Trace the effect across skills, not just the target

Evidence Required

Manifest's predicted_impact checked against observed dimension deltas
reward_hacking_suspected consulted
No conflict with ≥1 existing skill demonstrated
Verdict (Approve/Warn/Reject) recorded per proposal with reason

Red Flags

Approving a skill whose only evidence is "score went up"
Ignoring a falling output_quality because execution_cost rose
Shipping after a seesaw-regression round without explicit justification
Treating the deterministic critic as sufficient for non-local effects (it is not — it only checks reward hacking + score-claim contradiction)

Source: hashgraph-online/awesome-codex-plugins → plugins/epicsagas/epic-harness/skills/_critic/SKILL.md

---name: criticdescription: HarnessX Critic (Tier 2.1) — adversarial review of evolved-skill proposals against trace evidence. Detects reward hacking and manifest/evidence contradictions. Out-of-band LLM counterpart to the in-loop deterministic critic in src/evolve/critic.rs.--- # Critic — Adversarial Proposal Review > **In-loop vs out-of-band.** epic-harness forbids external LLM calls from > production, so the reflect loop ships a **deterministic** critic > (`src/evolve/critic.rs`) that gates seeding when reward hacking is > suspected. THIS skill is the **out-of-band LLM** version a meta-agent or > human runs during `/evolve` review for the cases the deterministic check > cannot catch (non-local effects, manifest/evidence nuance). ## When to Trigger - During `/evolve` review of newly seeded skills - When `reward_hacking_suspected` is true in metrics - After a seesaw-regression round, before re-proposing ## Process ### 1. Gather the proposal + evidence - Read the evolved skill proposal(s) from this round - Read the EditManifest (edit_type, target, intended_effect, predicted_impact) - Read the round's TaskDigests (outcome, failure_categories, evidence_excerpts) - Read recent score_history dimension_averages (tool_success, output_quality, execution_cost) ### 2. Falsify the manifest (paper §4.3, Table 9) For each proposal, ask: does the trace evidence support the `predicted_impact`? - If the manifest claims "Lift avg_score_with" but output_quality is regressing → **Reject** - If the manifest claims a tool fix but the implicated component shows no change → **Warn** - If the evidence corroborates the predicted effect → **Approve** ### 3. Reward-hacking cross-check - Is execution_cost rising while output_quality falls across the window? - Could the skill be gaming a metric (fewer tool calls inflating cost score) rather than improving outcomes? - If yes, the skill must NOT ship — flag for the rejected buffer. ### 4. Non-local effect scan - Will this skill's guard rules conflict with existing skills (overlapping triggers, contradictory rules)? - Does it interact with shared state (context, memory, control) in a way the manifest didn't account for? ## Anti-Rationalization | Excuse | Rebuttal | Do instead | |--------|----------|------------| | "The score went up, so it works" | Score can rise via metric gaming | Verify the *outcome* improved, not just the score | | "The seesaw passed, it's safe" | Seesaw is coarse; sub-threshold coupling evades it | Check dimension deltas, not just aggregate pass | | "It's just a prompt tweak" | Prompt edits have non-local effects on shared context | Trace the effect across skills, not just the target | ## Evidence Required - [ ] Manifest's predicted_impact checked against observed dimension deltas - [ ] reward_hacking_suspected consulted - [ ] No conflict with ≥1 existing skill demonstrated - [ ] Verdict (Approve/Warn/Reject) recorded per proposal with reason ## Red Flags - Approving a skill whose only evidence is "score went up" - Ignoring a falling output_quality because execution_cost rose - Shipping after a seesaw-regression round without explicit justification - Treating the deterministic critic as sufficient for non-local effects (it is not — it only checks reward hacking + score-claim contradiction) --- **Source:** [`hashgraph-online/awesome-codex-plugins`](https://github.com/hashgraph-online/awesome-codex-plugins) → `plugins/epicsagas/epic-harness/skills/_critic/SKILL.md`

Frequently asked questions