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 solofounder-ai/solofounder --skill conversation-analysisgit clone --depth 1 https://github.com/solofounder-ai/solofounderWrote 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/solofounder-ai/solofounder/conversation-analysis)<a href="https://agentmods.dev/skills/solofounder-ai/solofounder/conversation-analysis"><img src="https://agentmods.dev/badge/skills/solofounder-ai/solofounder/conversation-analysis/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/skills/solofounder-ai/solofounder/conversation-analysis"><img src="https://agentmods.dev/badge/skills/solofounder-ai/solofounder/conversation-analysis.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 | $0.00025 | $0.02093 |
| Opus 5 | $0.00013 | $0.01046 |
| Sonnet 5 | $0.00005 | $0.00419 |
| Haiku 4.5 | $0.00003 | $0.00209 |
Grade A, and why
conversation-analysis 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 10d 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversation Analysis
Overview
The learning engine of SoloFounder. After every completed flow, the Conversation Analyst reviews the entire session — every decision, every exception, every iteration, every surprise — and produces actionable improvements.
This is not optional. It runs after every flow. It is the final stage, automatically appended by the Flow Composer.
Core principle: The richest data source for improvement is the conversation itself. Metrics capture WHAT happened. Conversation analysis understands WHY.
When to Use
Always. After every completed flow. The Flow Composer appends a Learn stage to every flow, which invokes this skill.
Do not skip. Do not abbreviate. Do not say "nothing notable happened." Every conversation has learnings.
The Five Analysis Dimensions
1. Wasted Work
- What was produced and then thrown away?
- What questions should have been asked earlier in the flow?
- What information was available but not used?
- What stages had to re-run and why?
- What work was done based on a misunderstanding?
What to look for: Deleted deliverables, re-dispatched stages, specialist output that was overridden, user corrections that invalidated prior work.
Output format:
### Wasted Work
- [Description of wasted work] — **Root cause:** [why it happened] — **Prevention:** [how to avoid next time]
2. Misalignment
- Where did user and agent mean different things?
- Where did user correct an agent's understanding?
- Were there ambiguous terms that caused confusion?
- How many iterations were needed to reach shared understanding?
What to look for: User corrections ("no, I meant..."), terms used differently by user and agent, questions that had to be re-asked with different phrasing.
Output format:
### Misalignment
- **Term/concept:** [what was ambiguous] — **User meant:** [X] — **Agent interpreted:** [Y] — **Prevention:** [clarification strategy for future]
3. Exception Archaeology
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.
- 10d ago First seen · 250 lines · 25 tokens per session scan A 56771ba5d591
conversation-analysis is a skill published in the GitHub repository solofounder-ai/solofounder (3 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 2,093 once invoked, about $0.0001 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 2026-08-31.
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