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 skills add bdfinst/agentic-dev-team --skill agent-type-advisorgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWrote 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/skills/bdfinst/agentic-dev-team/agent-type-advisor)<a href="https://agentmods.dev/skills/bdfinst/agentic-dev-team/agent-type-advisor"><img src="https://agentmods.dev/badge/skills/bdfinst/agentic-dev-team/agent-type-advisor.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.01019 |
| Opus 5 | $0.00045 | $0.00509 |
| Sonnet 5 | $0.00018 | $0.00204 |
| Haiku 4.5 | $0.00009 | $0.00102 |
Grade A, and why
agent-type-advisor 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Type Advisor
Role: worker. Recommend markdown vs script for a plugin capability, citing the shared decision rules so the recommendation is auditable, not a vibe.
Constraints
- Cite, do not paraphrase. Every recommendation names at least two rule IDs
(
R1–R10) fromknowledge/agent-type-decision-rules.md. That file is the single source of truth — read it first, do not restate its reasoning from memory. - Classify the input mode first. Prose → forward-looking. File path → retrospective. Both modes are first-class.
- Behavior decides type, not extension. Judge an existing file by what its
body actually does, never by its
.md/.shsuffix alone. - Be concise. Emit only the recommendation block. No preamble.
Step 1 — Read the rules
Read knowledge/agent-type-decision-rules.md in full. It is short and is the
rule source (R1–R10, the markdown/script columns, and the confidence ladder).
Step 2 — Detect input mode
Inspect $ARGUMENTS:
- If it resolves to an existing file (ends in
.md/.sh/.pyand exists on disk, or Glob finds it) → retrospective mode (Step 3b). - Otherwise treat the whole argument as a prose use-case → forward-looking mode (Step 3a).
If $ARGUMENTS is empty, ask the user for a prose use-case or a file path and
stop.
Step 3a — Forward-looking (prose use-case)
- Extract the unit's job: what does it produce, how often does it run, does it gate anything, does the answer vary with phrasing/context?
- Walk the rules. Collect every rule that applies and the column it points to.
- Decide
markdownorscriptby the dominant column. - Set confidence per the file's ladder (high / medium / low).
Output the recommendation block (Step 4) with recommendation: = markdown or
script.
Step 3b — Retrospective (existing file)
- Read the target file. Determine its observable behavior from the body — what it actually computes or produces — independent of its extension.
- Determine the type it should be by the same rule walk as Step 3a.
- Compare to the type it currently is (markdown unit =
.mdagent/SKILL.md; script =.sh/.py):- should-be matches current →
recommendation: KEEP - should-be differs from current →
recommendation: CHANGE(name the target type and what to extract/convert) - mixed mechanical + judgment → apply R10: if the markdown unit already
delegates the mechanical half to a script it calls,
KEEP; if it inlines mechanical enumeration,CHANGE(extract the script).
- should-be matches current →
- Set confidence per the ladder.
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 · 97 lines · 90 tokens per session scan A 9e4c6ef8d469
agent-type-advisor is a skill published in the GitHub repository bdfinst/agentic-dev-team (280 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 1,019 once invoked, about $0.0005 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 2026-09-05.
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