# Session Analyst > > - **Type:** Skill - **Install:** `agentstack add skill-tough-tongue-toughtongue-skills-session-analyst` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [tough-tongue](https://agentstack.voostack.com/s/tough-tongue) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [tough-tongue](https://github.com/tough-tongue) - **Source:** https://github.com/tough-tongue/toughtongue-skills/tree/main/skills/session-analyst - **Website:** https://www.toughtongueai.com/agents ## Install ```sh agentstack add skill-tough-tongue-toughtongue-skills-session-analyst ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Session Analyst Pull session data → aggregate patterns → produce a structured report → optionally hand off to slides/email tools for distribution. ## Prerequisites - The **ttai** MCP server must be connected. Tool references below use the `ttai:` server prefix (e.g. `ttai:list_sessions`); some agents surface these as `mcp__ttai__list_sessions`. If the tools are missing, point the user at the repo README and for a `TTAI_PAT` token. ## Data model (what a session gives you) Each session from `ttai:list_sessions` / `ttai:get_sessions_batch` includes: - Identity: `scenario_id`, `scenario_name`, `user_name`, `user_email` - Lifecycle: `status`, `created_at`, `completed_at`, `duration_minutes` - `evaluation_results`: `final_score`, `strengths`, `weaknesses`, and `report_card[]` — per-topic `{topic, score, note, weight}` - `improvement_results`: `improvement_areas`, `action_items`, `resources` - `extraction_results`: structured variables (if the scenario extracts them) - `transcript_url` (signed URL — fetch it for the conversation text) and `analytics_url` (human-viewable analysis page) `report_card` topics are the backbone of aggregation: they are consistent within a scenario because they come from its rubric. ## Workflow ### Step 1: Scope 1. Call `ttai:list_organizations`. Team analysis almost always needs an `org_id` — pass it on every call, along with `is_org: true` on `ttai:list_sessions` for org-wide data. 2. Resolve the scenario: `ttai:list_scenarios` if the user gave a name, not an ID. 3. Confirm the window and population: which scenario(s), which date range (`from_date` / `to_date`), which people (`user_email` filter), how many sessions. ### Step 2: Pull - `ttai:list_sessions` with `scenario_id`, date filters, and pagination (`page`, `limit`). Iterate pages until you have the requested population — check the page metadata rather than assuming one page is everything. - Sessions missing `evaluation_results`: either exclude them from scoring aggregates (note the count), or backfill — call `ttai:post_process_session` for each, then re-fetch after a wait and check that `evaluation_results` appeared. Backfill only when the user needs completeness. - Deep dives (outliers, disputed scores): `ttai:get_sessions_batch` with the session IDs, then fetch `transcript_url` contents for the actual conversation. ### Step 3: Aggregate Compute, at minimum: - **Score distribution**: mean, median, range of `final_score`; flag the count of unanalyzed sessions excluded. - **Per-topic breakdown**: average `report_card` score per topic, weighted by `weight`. The lowest topics are the improvement areas. - **Recurring weaknesses**: cluster `weaknesses` and `improvement_areas` text across sessions into themes; count occurrences. Name each theme by the behavior, not an abstraction ("jumps to price before discovery" beats "communication issues"). - **Trend**: score over time if the window is long enough (week buckets work well); per-person averages for team views. - **Evidence**: for each top theme, pull 1-2 short transcript quotes from representative sessions. Reports without evidence read as opinion. For org-wide rollups (usage, member breakdown, time series), `ttai:get_analytics` with `is_org_wide: true` complements per-session aggregation. ### Step 4: Report Use the matching template from [references/report-templates.md](references/report-templates.md): - **Team performance report** — "how is my team doing?" - **Scenario health report** — "is this scenario working?" (pairs with the scenario-refiner skill when the answer is no) - **Individual coaching report** — one person, one skill gap, action items Always include: population and window, score summary, top 3-5 improvement areas with evidence, concrete action items, and `analytics_url` links for sessions worth reviewing by a human. ### Step 5: Distribute (optional) If the user wants a deck, email, or document, hand the report content to their connected tools (slides MCP, email MCP, docs). Keep the structure: one improvement area per slide/section, evidence quote included. ## Recipes ### "Top 5 improvement areas for scenario X, last 50 sessions" `ttai:list_scenarios` (resolve ID) → `ttai:list_sessions` (scenario_id, limit 50, org context) → aggregate report_card topics + weakness themes → Team performance report → deck if asked. ### "Pull the 5 lowest-scoring sessions and find out what went wrong" `ttai:list_sessions` (scenario_id + window) → sort by `evaluation_results.final_score` ascending, take 5 → `ttai:get_sessions_batch` → fetch transcripts → diagnose common failure patterns → if the fault is in the scenario (not the users), hand off to the **scenario-refiner** skill with the diagnosis. ### "How did [person] do this month?" `ttai:list_sessions` (user_email + from_date) → per-topic averages, trend across their sessions → Individual coaching report with action items from `improvement_results`. ### Automated post-call coaching (webhook-driven) For teams wiring this into pipelines (e.g. every real sales call gets a coaching report): see the recipe in [references/report-templates.md](references/report-templates.md) — `ttai:create_session` ingests an external transcript against a coaching scenario, `ttai:post_process_session` triggers analysis, poll until `evaluation_results` appears, then format and send the report. ## Pitfalls - **Don't average across different scenarios' report cards** — topics and weights differ per rubric. Aggregate per scenario, compare qualitatively. - **Small samples**: below ~10 analyzed sessions, report observations, not statistics — and say so. - **Session status**: only `completed` sessions have meaningful duration and results; exclude `in_progress` and `failed` from aggregates. - **Privacy**: coaching reports name individuals. Confirm the audience before distributing anything per-person to a group channel. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [tough-tongue](https://github.com/tough-tongue) - **Source:** [tough-tongue/toughtongue-skills](https://github.com/tough-tongue/toughtongue-skills) - **License:** MIT - **Homepage:** https://www.toughtongueai.com/agents Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-tough-tongue-toughtongue-skills-session-analyst - Seller: https://agentstack.voostack.com/s/tough-tongue - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.