Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add JuanMarchetto/agent-skills/plugin install post-run-analysisWrote 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/juanmarchetto/agent-skills/post-run-analysis)<a href="https://agentmods.dev/skills/juanmarchetto/agent-skills/post-run-analysis"><img src="https://agentmods.dev/badge/skills/juanmarchetto/agent-skills/post-run-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/juanmarchetto/agent-skills/post-run-analysis"><img src="https://agentmods.dev/badge/skills/juanmarchetto/agent-skills/post-run-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 | $0.00081 | $0.02660 |
| Opus 5 | $0.00041 | $0.01330 |
| Sonnet 5 | $0.00016 | $0.00532 |
| Haiku 4.5 | $0.00008 | $0.00266 |
Grade A, and why
post-run-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 6d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post-Run Analysis
Trigger events:
- A background task completes (you receive a task completion notification)
- A test suite finishes (jest, vitest, pytest, cargo test, go test, etc.)
- A build or deploy completes (webpack, vite, docker build, cargo build, etc.)
- A migration run ends (database migration, code migration, data migration)
- A benchmark or performance test completes
- Any long-running process that produces structured output finishes
After ANY of these events:
- Locate the output/log from the completed process
- Run the full 5-phase analysis protocol
- Present the results to the user
- Persist findings according to the confidence gate rules
This applies even if the user did not ask for analysis. If a run completes, you act.
Analyze completed iterative runs, extract learnings, compare with history, and close the feedback loop automatically.
Why This Exists
Iterative processes (code migration, test suites, deployments, benchmarks) produce valuable data that gets lost because nobody systematically reviews it. This skill ensures every run's lessons get captured, compared, and fed back into the next iteration -- turning trial-and-error into structured improvement.
When To Use
- A migration/build/test run just completed (background task notification or log file)
- User asks "what happened" or "analyze the results"
- User wants to compare this run vs previous runs
- After any process that logs structured output (compiler errors, test results, timing data)
- Proactively: when you see a completed background task with a log file
Run History Schema
Track every analyzed run in a standard format. Store in MEMORY.md or a dedicated .claude/run-history.md file:
What ships with it
10 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.
- 6d ago First seen · 240 lines · 81 tokens per session scan A ba658e91d736
post-run-analysis is a skill published in the GitHub repository JuanMarchetto/agent-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 81 tokens to every session and 2,660 once invoked, about $0.0004 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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