Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/BanibrataChatterjee/AwesomeSalesforceSkillsnpx agentmods add commands/banibratachatterjee/awesomesalesforceskills/new-skillWrote 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/commands/banibratachatterjee/awesomesalesforceskills/new-skill)<a href="https://agentmods.dev/commands/banibratachatterjee/awesomesalesforceskills/new-skill"><img src="https://agentmods.dev/badge/commands/banibratachatterjee/awesomesalesforceskills/new-skill/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/commands/banibratachatterjee/awesomesalesforceskills/new-skill"><img src="https://agentmods.dev/badge/commands/banibratachatterjee/awesomesalesforceskills/new-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00729 |
| Opus 5 | $0.00000 | $0.00365 |
| Sonnet 5 | $0.00000 | $0.00146 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
new-skill 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 12d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/new-skill — New Skill Workflow
Triggers the skill-builder agent.
Usage
/new-skill
The agent checks local coverage first, asks targeted questions, then uses new_skill.py to scaffold a compliant package. Content is filled into the scaffold — the structure is never written from scratch.
What Happens
Step 1 — Coverage Check (do not skip)
python3 scripts/search_knowledge.py "<topic>" --domain <domain>
If has_coverage: true is returned, review the existing skill before creating a new one. Extend or differentiate — do not duplicate.
Step 2 — Scaffold
python3 scripts/new_skill.py <domain> <skill-name>
This creates the full directory structure:
skills/<domain>/<skill-name>/
├── SKILL.md ← pre-filled with TODO markers (includes Recommended Workflow section)
├── references/
│ ├── examples.md ← pre-filled with TODO markers
│ ├── gotchas.md ← pre-filled with TODO markers
│ ├── well-architected.md ← official sources PRE-SEEDED for domain
│ └── llm-anti-patterns.md ← 5+ AI-specific mistakes to avoid
├── templates/<skill-name>-template.md
└── scripts/check_<noun>.py ← stdlib-only checker stub
new_skill.py will warn if coverage already exists and ask for confirmation.
Step 3 — Fill All TODOs
Every file created by the scaffold has TODO: markers. Fill them all:
SKILL.md— description (must include "NOT for ..."), triggers (3+, 10+ chars each), tags, inputs, outputs, well-architected-pillars, full body (300+ words), and## Recommended Workflow(3–7 numbered agent steps)references/examples.md— real examples with context, problem, solutionreferences/gotchas.md— non-obvious platform behaviorsreferences/well-architected.md— WAF notes; official sources are pre-seeded, add usage contextreferences/llm-anti-patterns.md— 5+ mistakes AI assistants make in this domain: wrong output, why it happens, correct pattern, detection hintscripts/check_<noun>.py— implement the actual checks (stdlib only)
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.
- 12d ago First seen · 92 lines · 0 tokens per session scan A ca458c205617
new-skill is a command published in the GitHub repository BanibrataChatterjee/AwesomeSalesforceSkills (3 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 729 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 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.