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 commands/knowledgexlab/skill-git/scangit clone --depth 1 https://github.com/KnowledgeXLab/skill-gitWhat 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 | $0.00047 | $0.06430 |
| Opus 5 | $0.00023 | $0.03215 |
| Sonnet 5 | $0.00009 | $0.01286 |
| Haiku 4.5 | $0.00005 | $0.00643 |
Grade A, and why
skill-git:scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 597 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running /skill-git:scan. Follow these steps exactly.
All output shown to the user must be in English.
Task Tracking
You MUST create a task for each item below and update each task's status as you progress (pending → in_progress → completed):
- Resolve target skills — validate requested skills or use all registered skills
- Pre-filter by description — group skills by domain similarity, skip unrelated pairs
- Extract rules (Phase 1) — load from cache or dispatch subagents to extract rules per skill
- Cluster and analyze pairs (Phase 2) — pool rules into topics, derive overlap metrics per pair
- Save results and output report — persist to
latest.jsonand display merge suggestions
Prelude
!bash "${CLAUDE_PLUGIN_ROOT}/scripts/sg-prelude.sh" $ARGUMENTS
Parse $ARGUMENTS for remaining flags (after the prelude has consumed -a <value>):
- Scan for
-for--force. If present, setforce = true; otherwiseforce = false. - Extract all remaining tokens (non-flag, non-flag-value) as
requested_skillslist.
If STATUS is not ok:
- Re-examine RAW_ARGUMENTS — check if the user expressed an agent name or intent that can be semantically resolved (e.g. a typo, a paraphrase, or an implicit default).
- If you can determine intent, note the resolution and proceed.
- If STATUS is
not_initializedand INITIALIZED_AGENTS is non-empty, suggest/skill-git:init -a <agent>. - If STATUS is
error, display REASON as a plain-language error and stop. - If intent cannot be resolved, display a clear error and stop.
Use AGENT, GLOBAL_BASE, and SKILLS_JSON from prelude output for all subsequent steps. The skills map referred to throughout this document is the parsed content of SKILLS_JSON.
Agent name in positional position: If $ARGUMENTS contains a known agent name (claude, gemini, codex, openclaw) as a standalone positional token not preceded by -a, silently treat it as -a <agent>: override AGENT, re-read GLOBAL_BASE and SKILLS_JSON from ~/.skill-git/config.json for that agent. Do not pass it to requested_skills.
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 · 597 lines · 0 tokens per session scan A 70b6600c41a7
skill-git:scan is a command published in the GitHub repository KnowledgeXLab/skill-git (41 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 6,430 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.