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 CodeAlive-AI/ai-driven-development --skill investigating-repository-historygit clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-developmentWrote 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/codealive-ai/ai-driven-development/investigating-repository-history)<a href="https://agentmods.dev/skills/codealive-ai/ai-driven-development/investigating-repository-history"><img src="https://agentmods.dev/badge/skills/codealive-ai/ai-driven-development/investigating-repository-history/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/codealive-ai/ai-driven-development/investigating-repository-history"><img src="https://agentmods.dev/badge/skills/codealive-ai/ai-driven-development/investigating-repository-history.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.01776 |
| Opus 5 | $0.00042 | $0.00888 |
| Sonnet 5 | $0.00017 | $0.00355 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
investigating-repository-history 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- investigating-repository-history — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository History Investigator
Use this skill to reconstruct the historical intent behind code before changing it. The goal is not merely “find the blame commit”; the goal is to return a compact, cited history note explaining relevant PRs, review comments, constraints, rejected approaches, and anomalies.
Contents
- Trigger conditions
- Core rule
- Fast path
- Progressive disclosure
- Investigation workflow
- Evidence confidence rules
- Output template
- Gotchas
- Available scripts
Trigger conditions
Use this skill when the user asks any of these:
- “Why is this code written this way?”
- “Can I remove/simplify/change this check, constraint, branch, migration, public API, or feature flag?”
- “Which PR introduced this behavior or regression?”
- “Find the relevant PR/review discussion/history for this code.”
- Before editing code that touches API compatibility, security, concurrency, persistence, migrations, performance, generated interfaces, feature flags, or unclear legacy/workaround logic.
Do not use this skill for trivial new code with no dependency on existing behavior.
Core rule
Before making a risky edit, produce a history note answering:
- What code scope was inspected?
- Which commits and PRs are relevant?
- Which review comments or PR discussions explain intent?
- What constraints, risks, rejected approaches, or tests were found?
- Is the evidence strong, weak, contradictory, stale, truncated, or unknown?
- How should the implementation plan change?
If the evidence is weak, say UNKNOWN and lower confidence. Never invent intent from a semantic match alone.
Fast path
From the repository working tree, run the collector first. If the skill directory is not the current directory, prefix the script path with the installed skill path and pass --repo-dir /path/to/repo.
What ships with it
14 files 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.
- agents/openai.yaml 374 B
- assets/history-note-template.md 233 B
- LICENSE 1.0 KB
- README.md 1.9 KB
- references/ANOMALIES.md 4.5 KB
- references/DECISION_ATOMS.md 1.8 KB
- references/EVALUATION.md 1.1 KB
- references/GH_CLI.md 3.4 KB
- references/OUTPUT_SCHEMA.md 1.9 KB
- scripts/compact_pr.py 6.0 KB runs code
- scripts/history_context.py 43 KB runs code
- scripts/validate_skill.py 2.9 KB runs code
- tests/__init__.py 0 B runs code
- tests/test_skill.py 10.0 KB runs code
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.
- 10d ago First seen · 166 lines · 84 tokens per session scan A 8dd137259143
investigating-repository-history is a skill published in the GitHub repository CodeAlive-AI/ai-driven-development (132 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 1,776 once invoked, about $0.0004 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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