Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add pitimon/8-habit-ai-dev/plugin install 8-habit-ai-devWrote 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/pitimon/8-habit-ai-dev/ai-dev-log)<a href="https://agentmods.dev/skills/pitimon/8-habit-ai-dev/ai-dev-log"><img src="https://agentmods.dev/badge/skills/pitimon/8-habit-ai-dev/ai-dev-log.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.01693 |
| Opus 5 | $0.00031 | $0.00847 |
| Sonnet 5 | $0.00012 | $0.00339 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
ai-dev-log 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Development Log Generator
Habit: H4 (Win-Win — honest disclosure to stakeholders) + H1 (Be Proactive — produce audit trail before audit) Anti-pattern: Hiding AI involvement from stakeholders, or scrambling to reconstruct AI usage at audit time
Why This Exists
EU AI Act Article 11 requires technical documentation kept up-to-date through the system lifecycle. Article 13 (3)(d) requires disclosure of how AI assists the development process. Beyond compliance, transparency about AI involvement is a deposit in the Emotional Bank Account — stakeholders trust you more when you're upfront.
This skill generates a chronological dev log showing:
- Which commits had human + AI collaboration
- Which AI model(s) contributed
- Date ranges of AI activity
- Human authors who reviewed/approved
When to Use
- Before major release for EU-targeted high-risk AI systems (Article 11 evidence)
- Quarterly for projects using AI coding assistants
- During audit preparation (any framework that requires AI disclosure)
- When onboarding new team members (show how AI fits into workflow)
When to Skip
- Project has no AI-authored commits (no
Co-Authored-Bytrailers) - Project is fully human-authored (no AI assistance in scope)
- Already have a comprehensive AI usage report from another tool
Quick Run (one command)
# Default: last 90 days, markdown to stdout
bash ${CLAUDE_PLUGIN_ROOT}/scripts/generate-ai-dev-log.sh
# Save to compliance folder
bash ${CLAUDE_PLUGIN_ROOT}/scripts/generate-ai-dev-log.sh \
--since 2026-01-01 \
--out docs/compliance/eu-ai-act/ai-dev-log/2026-Q1.md
# Reproduce a previously generated report from its recorded boundary
bash ${CLAUDE_PLUGIN_ROOT}/scripts/generate-ai-dev-log.sh \
--since 2026-01-01 \
--snapshot <sha-from-report>
# JSON for CI ingestion
bash ${CLAUDE_PLUGIN_ROOT}/scripts/generate-ai-dev-log.sh --json
# One-line stats
bash ${CLAUDE_PLUGIN_ROOT}/scripts/generate-ai-dev-log.sh --summary
The script handles all 6 process steps below (discover, extract, group, oversight, report, fallback). Read the script source if you want to customize: scripts/generate-ai-dev-log.sh
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 137 lines · 62 tokens per session scan A f5835fefb0a9
ai-dev-log is a skill published in the GitHub repository pitimon/8-habit-ai-dev (3 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 1,693 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
onboard
Scan and diagnose a project, or scaffold a new client from scratch. Works for code and venture clients. Auto-detects project type.
commit
Git commit workflow with precommit hook handling, lint/type checking, README updates, and API reference updates. Use when the user wants to commit changes. Handles precommit hooks that modify files (formatting, linting) by re-staging and retrying. Runs ruff lint and pyright type checks on staged Python files, and…
plagiarism-checker
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
employment-compliance
Covers the employment rules that carry real penalties — exempt and non-exempt classification, overtime and hours, employee versus contractor status, work authorization and recordkeeping, accommodation requests, and the notices and retention obligations that go with them. Use this to classify a role, review a…
next-step
Change-aware next step advisor. Use when: user asks what to do next, workflow progression is unclear, session just started with dirty worktree. Not for: executing the suggested command (user decides), auto-loop decisions (hooks handle that). Output: findings-based suggestions or session summary with commit seed.
precommit-fast
Quick pre-commit checks — lint:fix -> test.