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 SecurityTalent/bugskill-ai --skill observe-skillgit clone --depth 1 https://github.com/SecurityTalent/bugskill-aiWrote 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/securitytalent/bugskill-ai/observe-skill)<a href="https://agentmods.dev/skills/securitytalent/bugskill-ai/observe-skill"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/observe-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/skills/securitytalent/bugskill-ai/observe-skill"><img src="https://agentmods.dev/badge/skills/securitytalent/bugskill-ai/observe-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 | $0.00108 | $0.00846 |
| Opus 5.5 | $0.00043 | $0.00338 |
| Sonnet 5.5 | $0.00022 | $0.00169 |
| Haiku 4.5 | $0.00011 | $0.00085 |
Grade A, and why
observe-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 20d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observe Skill
Purpose
Track how skills perform in practice. A skill that works in theory may miss steps, use outdated payloads, or fire on the wrong triggers in real-world conditions. This skill captures that gap by recording execution outcomes to a per-skill log file.
Trigger Conditions
Activate this skill when the user says any of:
- "log this run"
- "skill worked" / "that worked"
- "skill failed" / "that didn't work" / "this didn't work"
- "log the outcome"
- "record this run"
- After any skill completes and the user describes a result (success, partial, fail)
Methodology
- Identify which skill was just used — ask if unclear. The skill name must match a directory name under
skills/. - Determine the outcome —
success(goal achieved),partial(some steps worked, some didn't), orfail(skill produced no useful result). - Collect details:
- Date (today's date)
- Task description (what was the user trying to do?)
- What specifically worked (payloads, steps, tools that produced results)
- What specifically failed (steps that produced no result, wrong output, or errors)
- Any error messages or unexpected responses verbatim
- Write the entry — append to
observations/<skill-name>/runs.md. Create the file and directory if they don't exist. - Confirm — show the user the appended entry.
Entry Format
---
date: YYYY-MM-DD
skill: <skill-name>
task: <one-line description of what was attempted>
outcome: success | partial | fail
what_worked: |
<describe what steps, payloads, or approaches produced results>
what_failed: |
<describe what steps or payloads did not work>
errors: |
<paste any error messages or unexpected responses>
notes: |
<any additional context — target type, environment quirks, etc.>
---
Example Interaction
User: "log this run — bola-idor worked, I found a sequential integer ID on /api/orders and was able to read other users' orders using my own session token"
Response: Creates or appends to observations/bola-idor/runs.md:
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.
- 20d ago First seen · 96 lines · 108 tokens per session scan A a5b71ea02144
observe-skill is a skill published in the GitHub repository SecurityTalent/bugskill-ai (13 stars, last pushed 21d ago), licensed MIT. It adds 108 tokens to every session and 846 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-27.
Other skills, from other repositories
user-research-cookiy
End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment…
email-marketing-bible
Email marketing operating manual for AI agents, with hard send-safety gates. Use when someone plans, writes, designs, audits or sends an email campaign, newsletter, welcome series, abandoned-cart, post-purchase or win-back flow; builds a list or segment; drives Klaviyo, Mailchimp, Brevo, Kit or any ESP through MCP, an…
globalpercent
GlobalPercent — build a global-macro-probability panel for an investment research system. Merges public probability data from prediction markets (Polymarket + Kalshi), classifies every market into macro modules (monetary policy / macro economy / AI / etc.), and shows the whole market's expected-probability state at a…
ui-ux-design-pro
Senior-level UI/UX design expert for building data-driven, premium production interfaces. Use when you need to: 1. Design complex applications (dashboards, SaaS, AI tools) from scratch 2. Generate comprehensive design systems (tokens, palettes, typography) 3. Audit existing UI for quality, accessibility, and "craft"…
playwright-best-practices
Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions…
open-map-stack
Use textual agent instructions for GIS and geospatial work: source discovery and provenance, vector/raster/point-cloud pipelines, CRS and metric analysis, spatial SQL, routing and isochrones, QGIS projects, tile generation, and web maps. Use advanced tools and formats such as OSM, Overture, STAC, Sentinel/Landsat…