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SKILL verified MIT Self-run

Session Reflect

skill-dannote-dot-pi-session-reflect · by dannote

Analyze a user's pi coding-agent session history for recurring behavior, prompting habits, workflow loops, friction, and preferences. Use when the user asks to inspect or reflect on pi sessions, common behavior patterns, agent/user interaction style, prompting habits, or personal pi workflow quality.

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Install

$ agentstack add skill-dannote-dot-pi-session-reflect

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

Session Reflect

Use this skill to help the user understand their own pi usage patterns from local session logs.

Data location

By default this skill writes only to:

~/.pi/agent/cache/session-reflect/

It never writes analysis databases into the current project by default. Use --db only when the user explicitly asks for another location. Use clean to remove the default cache.

The skill's job is evidence retrieval + agent judgment. Do not turn the helper script into the analyst. Use the script to load/search/query session evidence; use your own reasoning to infer patterns cautiously.

Core rules

  • Discuss the analysis approach before implementing new tooling or making broad claims.
  • Do not hardcode English behavior categories or phrase lists as conclusions.
  • Treat repeated text, n-grams, FTS hits, and SQL aggregates as evidence leads, not interpretations.
  • Inspect surrounding context before interpreting short user turns like “go ahead” or “what next”.
  • Preserve multilingual text, typos, shorthand, pasted logs, and frustration markers as meaningful evidence.
  • Cite session paths or message keys for important claims.
  • Separate: observed facts, interpretations, confidence, alternative explanations, recommendations.
  • When the current user message is itself a high-signal intervention, follow the recovery protocol in references/intervention-events.md before continuing.

Helper script

Use scripts/session-db.ts as a local evidence workbench. It loads pi JSONL sessions into DuckDB and exposes SQL/search/context helpers.

Install helper dependencies from the skill directory if they are missing:

cd /skills/session-reflect
npm install

Typical first step:

npx tsx scripts/session-db.ts build

If the user points to another session root:

npx tsx scripts/session-db.ts build --root 

Useful evidence commands:

npx tsx scripts/session-db.ts doctor
npx tsx scripts/session-db.ts preset --list
npx tsx scripts/session-db.ts preset overview
npx tsx scripts/session-db.ts preset exact-short-repeats
npx tsx scripts/session-db.ts preset long-sessions
npx tsx scripts/session-db.ts turns --role user --limit 80
npx tsx scripts/session-db.ts turns --role user --short --limit 100
npx tsx scripts/session-db.ts ngrams --n 2 --min-count 3 --limit 50
npx tsx scripts/session-db.ts ngrams --n 3 --min-count 3 --limit 50
npx tsx scripts/session-db.ts search "literal or fuzzy lead" --role user --limit 25
npx tsx scripts/session-db.ts context  --before 4 --after 8
npx tsx scripts/session-db.ts context  --before 4 --after 8 --compact --hide-tools
npx tsx scripts/session-db.ts examples --text "Go ahead." --limit 5 --before 3 --after 5
npx tsx scripts/session-db.ts sample --turns 5 --examples 2 --before 3 --after 5
npx tsx scripts/session-db.ts interventions --limit 30 --min-score 2 --sort score
npx tsx scripts/session-db.ts interventions --limit 30 --min-score 2 --sort recent
npx tsx scripts/session-db.ts interventions --signal autonomy_boundary,evidence_challenge --limit 20
npx tsx scripts/session-db.ts interventions --project quackdb --since 2026-06-01 --limit 20
npx tsx scripts/session-db.ts interventions --sample --limit 10 --min-score 2
npx tsx scripts/session-db.ts interventions --pasted include --limit 20 --min-score 2
npx tsx scripts/session-db.ts interventions --pasted only --limit 20 --min-score 2
npx tsx scripts/session-db.ts interventions --context --limit 10 --min-score 2 --sort recent --before 4 --after 6
npx tsx scripts/session-db.ts sql "select ..."

Use --format table|json|markdown before the subcommand when output will be read by the agent or quoted in a report:

npx tsx scripts/session-db.ts --format markdown preset exact-short-repeats
npx tsx scripts/session-db.ts --format json context 

Read [references/query-cookbook.md](references/query-cookbook.md) when selecting SQL queries. Read [references/reflection-protocol.md](references/reflection-protocol.md) before producing a user-facing reflection. Read [references/intervention-events.md](references/intervention-events.md) when analyzing shouting, profanity, corrections, frustration, stop/pause requests, evidence challenges, or any high-signal user redirect.

Recommended workflow

  1. Clarify scope: recent sessions vs all sessions, coding-only vs all pi use, desired output depth.
  2. Build or refresh the DuckDB evidence database with scripts/session-db.ts build.
  3. Run a bounded first-pass evidence set: overview, exact-short-repeats, tools, long-sessions, and sample --turns 3 --examples 2. Add n-grams only when exact repeats do not explain enough.
  4. Pick surprising evidence leads and retrieve compact context windows around representative message keys. For repeated exact turns, use examples --text ... for targeted sampling or sample for time-spread examples across top repeated turns. Use --compact --hide-tools first; rerun without them only when tool output matters.
  5. For friction analysis, treat candidate events as broad interventions, not just profanity/escalation. Use references/intervention-events.md before generalizing.
  6. Run targeted searches for explicit current-user preferences mentioned in the conversation, then inspect context.
  7. Reason manually from evidence; do not let preset names, query names, repeated phrases, profanity, or all-caps become conclusions.
  8. Present a concise reflection with citations and confidence levels.
  9. Ask whether the user wants tooling changes, pi prompt/default changes, or deeper follow-up analysis.

Output shape

Use this structure unless the user asks otherwise:

# Pi session behavior reflection

## Evidence inspected

- Database/session scope
- Query types used

## Observed patterns

For each pattern:

- Observation
- Evidence
- Interpretation
- Confidence
- Alternative explanation

## Friction / failure modes

## Preferences inferred from behavior

## Recommendations to test

## Open questions

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.