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 softspark/ai-toolkit --skill introspectgit clone --depth 1 https://github.com/softspark/ai-toolkitWrote 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/softspark/ai-toolkit/introspect)<a href="https://agentmods.dev/skills/softspark/ai-toolkit/introspect"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/introspect.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 | $0.00042 | $0.01663 |
| Opus 5 | $0.00021 | $0.00831 |
| Sonnet 5 | $0.00008 | $0.00333 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
introspect 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 4d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Self-Debugging
$ARGUMENTS
Structured self-analysis for when the agent is stuck, looping, or producing degraded output.
Step 1: Capture Failure State
Before diagnosing, gather the facts. Answer each question concisely:
| Question | Answer |
|---|---|
| Last goal/task | What was the agent trying to accomplish? |
| Actions taken | List the last 3-5 actions in order |
| Errors or unexpected results | What went wrong? What was expected vs actual? |
| Attempt count | How many times has this been tried? |
| Time spent | Rough estimate of effort so far |
Step 2: Classify the Failure Pattern
Identify which pattern matches the current situation:
| Pattern | Symptoms | Common Cause |
|---|---|---|
| Loop | Same action repeated 3+ times | Missing exit condition, wrong approach |
| Drift | Actions diverge from original goal | Lost context, scope creep |
| Assumption Error | Working with wrong mental model | Didn't read code, assumed behavior |
| Tool Misuse | Wrong tool for the job | Grep when should Read, Bash when should Edit |
| Context Overflow | Forgetting earlier findings | Too much context, need compaction |
| Wrong Abstraction | Over-engineering simple task | Premature abstraction, YAGNI violation |
| Missing Information | Can't proceed without data | Need to ask user, read more code |
Pick the single best match. If multiple apply, pick the root cause pattern (the one that, if fixed, would resolve the others).
Step 3: Diagnose Root Cause
Answer these three questions:
- What assumption was wrong? — Identify the specific belief that led to failure.
- What information was missing? — What would have prevented the failure if known earlier?
- What would a fresh start look like? — If starting over with current knowledge, what would the first action be?
Step 4: Select Recovery Action
Choose the smallest recovery action and apply the smallest possible fix — do not restart from scratch unless absolutely necessary:
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.
- 4d ago First seen · 151 lines · 42 tokens per session scan A fcb5a1786cf4
introspect is a skill published in the GitHub repository softspark/ai-toolkit (170 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 1,663 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 2026-09-03.
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