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/tomsej/pi-ext/semnpx skills add tomsej/pi-ext --skill semgit clone --depth 1 https://github.com/tomsej/pi-extWhat 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 | $0.00075 | $0.00726 |
| Opus 5 | $0.00037 | $0.00363 |
| Sonnet 5 | $0.00015 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
sem 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sem
Use the pi-sem tools as a semantic lens, not as a universal replacement for raw git diff.
Default decision tree
Choose the smallest useful tool first:
-
Focused understanding of one entity →
sem_context- Best for a single function, method, class, block, or config section
- Prefer this before reading a whole large file
-
Blast radius / affected tests / hidden dependents →
sem_impact- Use when reasoning about what could break
- Prefer
scope=testsfor test selection - Prefer
scope=allwhen validating broader impact
-
Structural inventory of a file →
sem_entities- Use before drilling into a suspicious file
- Good for large files and mixed code/config files
-
What changed across a commit/range/working tree →
sem_diff- Use for semantic summaries, entity counts, and review overviews
- Do not default to it when you only need exact patch details or a single entity
-
History / ownership of an entity →
sem_log,sem_blame- Use for regressions, archaeology, and ownership questions
Review workflow
For commit / branch / PR review:
- Run
sem_diffonce for a semantic overview - Pick the riskiest changed entities
- Run
sem_impacton those entities - Run
sem_contexton the suspicious ones you need to understand deeply - Confirm final findings with raw
git diff,read, or direct file inspection before citing line numbers
For snapshot / folder review:
- Start with
sem_entities - Use
sem_contexton the most relevant entities - Use
sem_impactonly after you identify something suspicious
Important caveats
sem diff --format jsonis not always smaller than rawgit diffsemmay under-cover tests, assets, generated files, or non-semantic glue code- Do not cite
semoutput alone as final evidence for line-level review comments - If semantic coverage looks incomplete, fall back to raw
git diff,read,grep, and file inspection
Good prompts / tool choices
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 · 73 lines · 75 tokens per session scan A 0ef8a0f86fff
sem is a skill published in the GitHub repository tomsej/pi-ext (69 stars, last pushed 15d ago), licensed MIT. It adds 75 tokens to every session and 726 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
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.