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.
npx skills add Rockielab/rockie-codex --skill post-run-reviewgit clone --depth 1 https://github.com/Rockielab/rockie-codexWrote 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.
[](https://agentmods.dev/skills/rockielab/rockie-codex/post-run-review)<a href="https://agentmods.dev/skills/rockielab/rockie-codex/post-run-review"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-codex/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.
<a href="https://agentmods.dev/skills/rockielab/rockie-codex/post-run-review"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-codex/post-run-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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.
This is a copy
100% identical to post-run-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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:
- Calls
journal.py close <node> --metric ... --is-buggy ... --failure-class ... --analysis "...". - If
is_buggy=1and it's a clear durable rule: emits a[LEARN]block in its response (Stop hook captures it). - If
failure_class=bad-hypothesis: emits a[DEAD-END]block. - If
actual_deltafor 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).
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.
- 12d ago First seen · 90 lines · 91 tokens per session scan A a28d4ce2025b
post-run-review is a skill published in the GitHub repository Rockielab/rockie-codex (20 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. It is 100% identical to post-run-review, differing in 0 lines, and is treated as a copy.
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