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 agentmods add skills/codagent-ai/agent-skills/session-reportnpx skills add Codagent-AI/agent-skills --skill session-reportgit clone --depth 1 https://github.com/Codagent-AI/agent-skillsWrote 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/codagent-ai/agent-skills/session-report)<a href="https://agentmods.dev/skills/codagent-ai/agent-skills/session-report"><img src="https://agentmods.dev/badge/skills/codagent-ai/agent-skills/session-report.svg" alt="Measured on agentmods" 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 | $0.00054 | $0.00944 |
| Opus 5 | $0.00027 | $0.00472 |
| Sonnet 5 | $0.00011 | $0.00189 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
session-report 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 3d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Report
Audit your own session against the task file(s) you were given. Surface assumptions you made and context gaps that caused struggles. These are observations for the human — not things you can fix after the fact.
Review Process
Work through each dimension in order. For every finding, cite specific evidence.
Dimension 1: Assumption Audit
Identify every place you made a decision that was not dictated by the task file or spec. For each assumption:
- Ambiguity — what the spec didn't say
- Choice — what you decided and why (the reasoning, not just the outcome)
- Risk — what could go wrong if this choice is wrong. Be specific: name the scenario, the affected code path, and what would break. "Could surprise future workflows" is not specific enough — say which workflow shape triggers it and what the symptom would be.
- Impact classification:
benign— reasonable default, no real consequence (do not report)notable— meaningful choice that could have gone differentlyrisky— could cause problems, warrants review
- Recommendation — what the reviewer should do: verify behavior, add a test, clarify the spec, or approve as-is with rationale.
Report only risky and notable assumptions. Drop benign entirely — they dilute the signal. Sort risky first, then notable.
Dimension 2: Context Gaps
A context gap is a major, avoidable hole in the task file or project context that sent you down a fundamentally wrong path, caused you to build the wrong thing, or wasted significant effort that better upfront information would have prevented entirely.
The bar is high. Most sessions have zero context gaps. Fixing a linter warning is not a context gap. Searching for a convention is not a context gap. Iterating on validator feedback is not a context gap — it is the normal development loop.
Examples of real context gaps:
- The task said to extend module X, but module X was deleted last week and replaced by module Y — you built against a nonexistent target
- The task required integrating with service A but didn't mention that service A requires an auth token from a separate provisioning step — you couldn't have known this without tribal knowledge
- The task file pointed you to the wrong directory or package, wasting significant effort before you found the right one
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.
- 3d ago First seen · 83 lines · 54 tokens per session scan A e14049dfdfb2
session-report is a skill published in the GitHub repository Codagent-AI/agent-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 944 once invoked, about $0.0003 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.
Other skills, from other repositories
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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