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 serejaris/personal-corp-os --skill pm-feedbackgit clone --depth 1 https://github.com/serejaris/personal-corp-osWrote 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/serejaris/personal-corp-os/pm-feedback)<a href="https://agentmods.dev/skills/serejaris/personal-corp-os/pm-feedback"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/pm-feedback/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/serejaris/personal-corp-os/pm-feedback"><img src="https://agentmods.dev/badge/skills/serejaris/personal-corp-os/pm-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00132 | $0.02569 |
| Opus 5 | $0.00066 | $0.01285 |
| Sonnet 5 | $0.00026 | $0.00514 |
| Haiku 4.5 | $0.00013 | $0.00257 |
Grade A, and why
pm-feedback 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pm-feedback — User feedback analysis
Part of the Personal Corp framework — running a one-person business through AI agents. Structure raw feedback into a decision-driving insight report. Built-in classification, sentiment, theme clustering, NPS, trend analysis, source triangulation, and persona extraction.
Inputs
| Field | Required | Notes |
|---|---|---|
| Feedback data | yes | Excel / CSV / pasted text / review screenshots |
| Purpose | no | Product improvement / satisfaction / topic-specific (e.g. post-launch reaction); default product improvement |
| Time range | no | For freshness tagging and trend analysis |
| Source channels | no | Multiple channels enable triangulation |
Mode: ≤ 20 items → close-read mode (item-by-item with detailed reading); > 20 → statistical mode (auto-classify + aggregated report).
Step 1 — Pre-process data
- Drop exact duplicates
- Merge near-duplicates (similarity > 90%), record merge count
- Ultra-short items (< 5 chars, no substance like "good"/"bad") → counted separately, not in deep analysis
- If a rating column exists (1-10 or 1-5 stars) → extract for NPS
- Identify source channel (in-app feedback, app store, support ticket, social media, etc.)
Step 2 — Classification
Six-category taxonomy:
| Category | Criterion | Example |
|---|---|---|
| Feature request | User wants something not yet built | "I'd like batch export" |
| Bug report | Existing feature behaves incorrectly | "Save button loses my data" |
| Usage question | User can't find or doesn't know how | "How do I change my password?" |
| UX complaint | Feature exists but experience is poor | "Loading is too slow" / "UI too cluttered" |
| Positive review | Satisfaction, praise, recommendation | "Love this feature!" |
| Other | Unclassifiable or off-topic | Spam, ads, noise |
When ambiguous (one item spans multiple), tag primary + secondary.
Step 3 — Sentiment analysis
| Sentiment | Signals | Calibration |
|---|---|---|
| Positive | Likes, praise, recommends, thanks | Pure factual praise ("works") = neutral, not positive |
| Neutral | Statement of fact, question, calm suggestion | Feature requests = neutral by default unless angry |
| Negative | Complaint, anger, disappointment, threats | "I wish you supported X" = neutral; "Why don't you support X yet?" = negative |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 251 lines · 132 tokens per session scan A 035775d8f6b2
pm-feedback is a skill published in the GitHub repository serejaris/personal-corp-os (225 stars, last pushed 16d ago), licensed MIT. It adds 132 tokens to every session and 2,569 once invoked, about $0.0007 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.
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