post-run-review

post-run-review is a skill for Claude Code from Rockielab/rockie-claude. It costs 91 tokens per session (1,001 once invoked), scanned A, original, Apache-2.0.

A post-experiment review process that reads a training or evaluation log and records what happened, the measured result, and whether the failure was a bug, a bad setting, or a bad research idea.

In plain words
What is it for?
Use it after a tracked experiment to classify failures, record metrics, close the experiment journal entry, and file learnings or dead ends.
Why use it?
It prevents completed experiments from becoming unexplained logs and captures lessons that can guide later work.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the rockie-claude plugin — 29 skills, 1 MCP server shipped together

Good fit Use it after a tracked experiment to classify failures, record metrics, close the experiment journal entry, and file learnings or dead ends.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rockielab/rockie-claude/post-run-review
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.

Any agent
npx skills add Rockielab/rockie-claude --skill post-run-review
Clone the repo
git clone --depth 1 https://github.com/Rockielab/rockie-claude

Made for: Claude Code.

Or install rockie-claude, the plugin that ships this one along with the rest of its 29 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for post-run-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/rockielab/rockie-claude/post-run-review/github.svg)](https://agentmods.dev/skills/rockielab/rockie-claude/post-run-review)
Your own site
<a href="https://agentmods.dev/skills/rockielab/rockie-claude/post-run-review"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/post-run-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for post-run-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/rockielab/rockie-claude/post-run-review"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/post-run-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,001 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00091 $0.01001
Opus 5 $0.00046 $0.00500
Sonnet 5 $0.00018 $0.00200
Haiku 4.5 $0.00009 $0.00100

Measured 12d ago against content hash a28d4ce2025b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

post-run-review 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 12d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

project-harness/skills/post-run-review/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/post-run-review — structured Assess → Codify

Ports the AIDE submit_review pattern (MIT, aide/agent.py L19–44 + parse_exec_result L296–339). Ours adds the C4 failure-class classification — bug | bad-hyperparam | bad-hypothesis — which no other autonomous-research harness currently cleanly separates.

When to invoke

Automatically, as the final step of the Assess phase, after any experiment with a journal node (journal.py add … was called at Plan time). Don't invoke on trivial scripts.

What the skill does

Given:

  • Journal node id (--node N)
  • Log / stdout from the run (--log PATH)
  • Optional metric override (--metric NAME=VAL)

The agent reads the log, forms a structured verdict, and writes:

{
  "is_buggy": 0 | 1,
  "failure_class": null | "bug" | "bad-hyperparam" | "bad-hypothesis",
  "metric_name": "val_loss",
  "metric_value": 3.42,
  "lower_is_better": 1,
  "summary": "one-paragraph what-happened",
  "learn_block": "<optional [LEARN] emitted separately>",
  "dead_end_block": "<optional [DEAD-END] emitted separately>"
}

and then:

  1. Calls journal.py close <node> --metric ... --is-buggy ... --failure-class ... --analysis "...".
  2. If is_buggy=1 and it's a clear durable rule: emits a [LEARN] block in its response (Stop hook captures it).
  3. If failure_class=bad-hypothesis: emits a [DEAD-END] block.
  4. If actual_delta for this run was predicted: calibration.py close <run_id> "<hypothesis>" <actual>.

Failure-class meanings (IMPORTANT)

class means action
bug implementation error (shape mismatch, off-by-one, NaN from wrong init) fix the code, retry; emit [LEARN] with the gotcha
bad-hyperparam logic is sound but config misses (lr too high, batch wrong, seed bad) tune; keep the hypothesis alive
bad-hypothesis idea itself doesn't work at the scale tested emit [DEAD-END]; do NOT re-propose this direction

Mis-classifying a bad-hypothesis as a bug will cause the agent to loop on a fundamentally broken idea. Mis-classifying a bug as a bad-hypothesis will kill directions that would have worked after a fix. Be honest. When uncertain, prefer bad-hyperparam — it keeps the option open and triggers the cheapest next action (tune).

Read the full file on GitHub · 90 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. 12d ago First seen · 90 lines · 91 tokens per session scan A a28d4ce2025b

Subscribe to this mod's changes

post-run-review is a skill published in the GitHub repository Rockielab/rockie-claude (21 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 91 tokens to every session and 1,001 once invoked, about $0.0005 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.

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