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 agentmods add skills/ykorovko/dotagents/whynpx skills add ykorovko/dotagents --skill whygit clone --depth 1 https://github.com/ykorovko/dotagentsWhat 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 | $0.00046 | $0.00976 |
| Opus 5 | $0.00023 | $0.00488 |
| Sonnet 5 | $0.00009 | $0.00195 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
why 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 2d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Why
Investigate the motivation and constraints behind code. Separate what the record states from what the evidence merely suggests. Code can show what happens; it rarely proves why someone chose it.
Treat this as a read-only investigation. Do not modify files, external systems, tickets, or documents unless the user separately authorizes those changes.
Establish the question
Identify the code, behavior, decision, or threshold the user is asking about. Treat any explanation embedded in the question as a hypothesis, not a conclusion.
Anchor the investigation with:
- Relevant files, line ranges, and symbols
- Commits that introduced or substantially changed the behavior
- PR, issue, incident, or document identifiers found in history
- The time window in which the decision was made
Use repository-native history tools such as git blame, git log --follow, git show, and repository search. Use a hosting CLI or connector only when it is already available and authorized.
Search for evidence
Start with source control, then follow identifiers into other sources that are available and relevant:
- Pull requests and code review discussions
- Issue or ticket trackers
- Design documents, ADRs, specifications, and postmortems
- Team chat and meeting records
- Runtime metrics, logs, traces, and incident timelines
- Error tracking and release data
- Product analytics or warehouse data
Read references/source-playbook.md to select the relevant source guides. Always use the code-archaeology guide for repository history, then read only the playbooks that match sources available to the current investigation. If the target looks defensive, also read the incident and postmortem guide.
Do not assume these sources exist or require connectors the environment does not provide. Do not install tools, request new access, or search unrelated private histories. Record unavailable sources as coverage gaps.
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 258 B
- LICENSE 1.0 KB
- references/epistemics.md 2.2 KB
- references/investigator-prompt.md 2.4 KB
- references/source-playbook.md 1.5 KB
- references/sources/code-archaeology.md 2.0 KB
- references/sources/databricks.md 2.0 KB
- references/sources/datadog.md 1.7 KB
- references/sources/incident-postmortem.md 1.2 KB
- references/sources/linear.md 1.7 KB
- references/sources/notion.md 1.7 KB
- references/sources/sentry.md 1.8 KB
- references/sources/slack.md 1.7 KB
- references/synthesizer-prompt.md 2.2 KB
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.
- 2d ago First seen · 74 lines · 46 tokens per session scan A 7d81a45ce766
why is a skill published in the GitHub repository ykorovko/dotagents (0 stars, last pushed 2d ago), licensed MIT. It adds 46 tokens to every session and 976 once invoked, about $0.0002 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
greenfield
Parallel persona planning for new projects. Research agent runs first to build domain context, then Architect, PM, and Security agents run in parallel. Synthesis agent combines all perspectives into a detailed GSD-style PLAN.md with Tensions section.
brownfield-drift
Enforces architecture boundaries defined in PLAN.md. Use when a PR crosses module/service boundaries, when the dev asks "are we following the architecture?", or as a scheduled architecture health check. Not for querying what a module does — use brownfield-chat for that.
pr-review
Fix engine for PR review comments. Fetches review comments (Gemini bot or human), categorizes by impact, posts a prioritized fix queue, and applies fixes on dev approval. Called directly for quick fixes, or internally by pr-review-agent as part of full PR review.
wednesday-git
Unified Git workflow. Manages the entire task lifecycle: branch creation (sprint), atomic commits (git-os), and PR opening (pr-create).
standards-kit
Unified development and design standards. Enforces code quality (complexity < 8), strict naming conventions, and the mandatory use of approved UI component libraries.
codebase-intel
Unified codebase intelligence. Handles all questions about structure, logic, risk, and dependencies. Combines natural-language Q&A with deterministic lookups and pre-edit blast radius checks.