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 pantheon-org/tekhne --skill session-reflectiongit clone --depth 1 https://github.com/pantheon-org/tekhneWrote 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/pantheon-org/tekhne/session-reflection)<a href="https://agentmods.dev/skills/pantheon-org/tekhne/session-reflection"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/session-reflection/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/pantheon-org/tekhne/session-reflection"><img src="https://agentmods.dev/badge/skills/pantheon-org/tekhne/session-reflection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00170 | $0.02685 |
| Opus 5.5 | $0.00068 | $0.01074 |
| Sonnet 5.5 | $0.00034 | $0.00537 |
| Haiku 4.5 | $0.00017 | $0.00268 |
Grade A, and why
session-reflection 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 1mo 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session-End Reflection
Catch blind spots and under-investigated areas before concluding a session by asking two questions:
- Confidence audit: What am I least confident about right now?
- Blind-spot check (Sam Altman): What's the biggest thing I'm missing about this situation? What don't I realize?
~1 in 4 sessions, one of the answers reveals a critical gap that would silently invalidate work. This skill catches those gaps at the cheapest possible moment: before the user has walked away.
Prerequisites
- A session that appears to be concluding (user signals completion, asks for summary, or starts wrap-up language)
- A project-level behavioural rule for "always conduct session-end reflection" should already be active, if this project has one -- check the project's own rules file
- For persisting uncovered findings: the
context-fileskill, and optionallyadr-captureif a binding decision emerges - For genuine gaps that aren't being fixed in this session: the
context-fileskill's known-issue document type -- this is the primary, intended source of those entries; see Workflow step 6
When to Use
- A session appears to be concluding: the user signals completion, asks for a summary, or starts wrap-up language.
- All identified tasks are marked complete and the agent is about to hand back to the user.
When NOT to Use
- During a brief query that is clearly complete (e.g., "what's the capital of France?") -- the reflection overhead is not justified.
- When the user has explicitly said "don't do the reflection this time" or similar -- honour it, don't insist.
- In automated/CI contexts -- this is a human-interactive skill only.
- In the middle of active work -- only at session-end boundaries.
Workflow
- Detect session-end signals: "we're done", "thanks", "that's all", a request for a summary or next steps, or all identified tasks marked complete.
- Choose the reflection mode -- inline (default, for short sessions) or sub-agent spawn (preferred for deep sessions with significant work; see Sub-Agent Spawn Pattern).
- Initiate the reflection with a natural opening, e.g. "Before we wrap up, I'd like to do a quick reflection." Ask the two questions sequentially, never both at once -- wait for the user's response to each.
- Question 1, confidence audit: "What am I least confident about right now?" Generate 3-7 specific items, each naming what was done, what was not verified, and why confidence is low -- not a vague feeling.
- Question 2, blind-spot check: "What's the biggest thing I'm missing about this situation?" Target assumptions the user stated but that went unverified, alternatives not explored, and signals dropped mid-conversation.
- Follow up. If the user flags an item, investigate before concluding. If a finding warrants preservation, use
context-file. If an item is a verified, concrete gap that is NOT being fixed in this session, create a known-issue document (status: ACTIVE,severity: CRITICAL | HIGH | MEDIUM | LOW) viacontext-file-- this is the primary source of those entries; a reflection item that only lives in chat scrollback is the failure mode this step exists to prevent. Skip this only when the item is being fixed right now instead. - Conclude only once the investigation loop is resolved. Note clearly if new work was spawned.
- Verify outcome: confirm with the user that the reflection addressed their concerns, summarize any investigation performed, confirm any known-issue file was actually created, and explicitly ask whether anything else from the reflection needs addressing.
What ships with it
17 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.
- .audits/2026-10-02/Analysis.md 489 B
- .audits/2026-10-02/audit.json 552 B
- .audits/2026-10-02/Remediation.md 1.4 KB
- evals/instructions.json 3.0 KB
- evals/scenario-1/capability.txt 124 B
- evals/scenario-1/criteria.json 1.4 KB
- evals/scenario-1/task.md 1.9 KB
- evals/scenario-2/capability.txt 143 B
- evals/scenario-2/criteria.json 1.1 KB
- evals/scenario-2/task.md 1.8 KB
- evals/scenario-3/capability.txt 140 B
- evals/scenario-3/criteria.json 1.3 KB
- evals/scenario-3/task.md 2.1 KB
- evals/summary.json 127 B
- references/recommended-subagent-models.md 4.6 KB
- references/session-reflection-reference.md 2.6 KB
- references/subagent-spawn-pattern.md 2.9 KB
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.
- 1mo ago First seen · 187 lines · 170 tokens per session scan A 0cde381a408a
session-reflection is a skill published in the GitHub repository pantheon-org/tekhne (10 stars, last pushed today), licensed MIT. It adds 170 tokens to every session and 2,685 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for generating K8s YAML manifests (use gke-manifest-generation) or disaster recovery…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (auditcluster.sh), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (gVisor), enforcing Pod…
vite
Vite build tool configuration, plugin API, SSR, and Vite 8 Rolldown migration. Use when working with Vite projects, vite.config.ts, Vite plugins, or building libraries/SSR apps with Vite.
comet-github
A routing guide for Comet-related GitHub work. It directs requests about pull requests, issues, CI failures, ideas, and fixes to the appropriate review or implementation process.