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/dfridkin/clawops/plannpx skills add dfridkin/clawops --skill plangit clone --depth 1 https://github.com/dfridkin/clawopsWhat 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 | $0.00000 | $0.00529 |
| Opus 5 | $0.00000 | $0.00264 |
| Sonnet 5 | $0.00000 | $0.00106 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
plan 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 yesterday.
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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/plan
Structured planning and review workflow. Use before making significant code changes.
Engineering preferences (apply throughout)
- DRY — flag repetition aggressively.
- Well-tested — more tests rather than fewer; non-negotiable.
- Engineered enough — not fragile/hacky, not prematurely abstracted.
- Edge cases — thoughtfulness over speed; handle more, not fewer.
- Explicit over clever — always.
Before starting
Ask the user which mode they want:
1 / BIG CHANGE — Work through all four sections interactively (Architecture → Code Quality → Tests → Performance), with at most 4 top issues per section. Pause for feedback after each section before moving on.
2 / SMALL CHANGE — Work through interactively with ONE question per section.
Review sections
1. Architecture review
Evaluate:
- Overall system design and component boundaries.
- Dependency graph and coupling concerns.
- Data flow patterns and potential bottlenecks.
- Scaling characteristics and single points of failure.
- Security architecture (auth, data access, API boundaries).
2. Code quality review
Evaluate:
- Code organisation and module structure.
- DRY violations — be aggressive.
- Error handling patterns and missing edge cases (call these out explicitly).
- Technical debt hotspots.
- Areas that are over-engineered or under-engineered.
3. Test review
Evaluate:
- Test coverage gaps (unit, integration, e2e).
- Test quality and assertion strength.
- Missing edge case coverage.
- Untested failure modes and error paths.
4. Performance review
Evaluate:
- N+1 queries and data access patterns.
- Memory-usage concerns.
- Caching opportunities.
- Slow or high-complexity code paths.
Format for every issue found
For each specific issue (bug, smell, design concern, or risk):
- Describe the problem concretely, with file and line references.
- Present 2–3 options, including "do nothing" where reasonable.
- For each option specify: implementation effort, risk, impact on other code, maintenance burden.
- Give an opinionated recommendation mapped to the engineering preferences above. Always list the recommended option first.
- Use
AskUserQuestion— number each issue and letter each option (e.g. Issue 3, Option A) so the user can respond unambiguously.
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.
- yesterday First seen · 76 lines · 0 tokens per session scan A dacc764f28a1
plan is a skill published in the GitHub repository dfridkin/clawops (1 stars, last pushed 1mo ago), licensed MPL-2.0. It costs nothing until one of its globs matches a file; then it loads 529 tokens. 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
systematic-debugging
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brainstorming
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chat-pet-sprite-creation
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cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.