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pre-done-self-review

pre-done-self-review is a skill for Claude Code, Codex from ShaftHQ/SHAFT_ENGINE. It costs 41 tokens per session (625 once invoked), scanned A, original, MIT.

A checklist for reviewing work before calling it finished or handing it to someone else. It compares the work with all applicable rules, records gaps and alternatives, and checks that summaries and diagrams match the underlying status.

In plain words
What is it for?
Use it before sign-off, handoffs, or claiming a task is done, especially when the work has standing rules or tracked completion requirements.
Why use it?
Passing tests alone may miss required rules, unfinished items, or inaccurate status documents.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 .chaos-engine/tool.py pre-done report.md # 0 pass, 1 findings, 2 usage.

not rated 417repo +3 today A scan 41 tokens original MIT

Good fit Use it before sign-off, handoffs, or claiming a task is done, especially when the work has standing rules or tracked completion requirements.

Compare 6 skills from other repositories ↓
View source ↗ ShaftHQ/SHAFT_ENGINE shafthq.github.io
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/ShaftHQ/SHAFT_ENGINE
agentmods
npx agentmods add skills/shafthq/shaft_engine/pre-done-self-review

Made for: Claude Code, Codex.

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 pre-done-self-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/shafthq/shaft_engine/pre-done-self-review/github.svg)](https://agentmods.dev/skills/shafthq/shaft_engine/pre-done-self-review)
Your own site
<a href="https://agentmods.dev/skills/shafthq/shaft_engine/pre-done-self-review"><img src="https://agentmods.dev/badge/skills/shafthq/shaft_engine/pre-done-self-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 pre-done-self-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/shafthq/shaft_engine/pre-done-self-review"><img src="https://agentmods.dev/badge/skills/shafthq/shaft_engine/pre-done-self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 625 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 $0.00041 $0.00625
Opus 5.5 $0.00016 $0.00250
Sonnet 5.5 $0.00008 $0.00125
Haiku 4.5 $0.00004 $0.00063

Measured today against content hash f9ecc50454fb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pre-done-self-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 today.

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.

chaos-engine/skills/pre-done-self-review/SKILL.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pre-done self-review

Green tests are not Done. Before you say "Done", sign off as owner or reviewer, or write a handoff note, run this review on the status report (PR body, handoff note, or status doc). Run python3 .chaos-engine/tool.py retrieve --store graphify "<task> standing rules" and python3 .chaos-engine/tool.py retrieve --store mempalace "<task> standing rules" first.

Steps

  1. Rules reconcile. List every standing rule that applies: user memory, project and harness skills, the overlay, and the item's Definition of Done. Check every tracked item against each rule, not only against tests.

  2. Gaps cite a rule and an alternative. Every Blocked or Skipped row carries rule: <rule that permits it> and alt: <mock, fixture, stub, fake, simulator, or recording tried>. If a rule says a gap needs a substitute (for example blocked-on-data needs mock coverage), build the substitute; it is not a gap you can report.

  3. Derived views match the source. Diagrams (mermaid), summaries, and counts are views of the status table. Regenerate them, or edit them in the same change as the table.

  4. Adversarial pass. Write a ## What's missing section that answers the user's next question, "are we missing any?": items not tracked, rules not checked, and evidence not opened this turn.

  5. Machine check. Run the gate (pre_done_review.py), fix every finding, then re-run it:

    python3 .chaos-engine/tool.py pre-done report.md          # 0 pass, 1 findings, 2 usage
python3 .chaos-engine/tool.py pre-done report.md --json

What the gate enforces

  • A status table exists (first markdown table with a Status column; the ID is in the first column).
  • Mermaid drift: a node ID[status] or class ID status that disagrees with the row's status fails.
  • Blocked/Skipped rows without rule: or without alt: naming a substitute fail.
  • Declared counts in prose (Blocked: 3) that differ from the table fail.
  • A missing or empty ## What's missing section fails.

Read the full file on GitHub · 53 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. today First seen · 53 lines · 41 tokens per session scan A f9ecc50454fb

Subscribe to this mod's changes

pre-done-self-review is a skill published in the GitHub repository ShaftHQ/SHAFT_ENGINE (417 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 625 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-24.

PlanningProject managementautomationautomation-enginetest-automationtest-automation-frameworktesting-tools
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